CN118251177A - Tissue Therapy System - Google Patents

Abstract

The cardiac information dynamic display system includes one or more electrodes and a cardiac information console. One or more electrodes record potential data sets representing cardiac activity at multiple time intervals. The cardiac information console includes a signal processor and a user interface module. The signal processor uses the recorded potential data sets to calculate cardiac activity data sets at multiple time intervals. The cardiac activity data is associated with the surface position of one or more cardiac chambers. The user interface module displays information related to the cardiac activity data. The information is presented relative to a graphical representation of the surface of one or more cardiac chambers.

Description

Tissue treatment system

Description of the invention

RELATED APPLICATIONS

The present application claims priority from U.S. provisional patent application serial No. 63/203,606, entitled "tissu TREATMENT SYSTEM (TISSUE treatment system)", filed 7/27 at 2021, which is incorporated herein by reference.

The present application claims priority from U.S. provisional patent application serial No. 63/335,939, entitled "tissu TREATMENT SYSTEM (TISSUE treatment system)", filed on 28, 4, 2022, which is incorporated herein by reference.

Although the present application does not require priority from U.S. provisional application serial No. 63/226,040, entitled "ENERGY DELIVERY SYSTEMS WITH Lesion Index," filed on 7.27 at 2021, which is incorporated herein by reference.

Although the present application does not require priority from U.S. provisional application titled "Intravascular Atrial Fibrillation Treatment (endovascular atrial fibrillation treatment)", serial No. 63/260,234, filed on month 13 of 2021, this application may be concerned and incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: the U.S. national phase application of the patent cooperation treaty of application number PCT/US2022/016722, entitled "ENERGY DELIVERY SYSTEMS WITH absorption Index (energy delivery system with Ablation Index)", filed on month 17 of 2022, claims priority to the U.S. provisional application of application number 63/150,555, entitled "ENERGY DELIVERY SYSTEMS WITH absorption Index (energy delivery system with Ablation Index)", filed on month 17 of 2021, each of which is incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: U.S. application Ser. No. 16/335,893 entitled "Ablation SYSTEM WITH Force Control", filed on day 3, 22, 2019, entitled "Ablation SYSTEM WITH Force Control", filed on day 10, 11, 35 USC 371 national phase application of the patent cooperation treaty of PCT/US2017/056064, which claims priority of the U.S. provisional application entitled "Ablation SYSTEM WITH Force Control", filed on day 10, 2016, U.S. provisional application Ser. No. 62/406,748, entitled "Ablation SYSTEM WITH Force Control", filed on day 5, 10, 2017, and U.S. provisional application of 62/504,139, each of which is incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: U.S. application Ser. No. 16/097,955 entitled "Cardiac Information DYNAMIC DISPLAY SYSTEM AND Method (cardiac information dynamic display System and Method)", filed 31, 10, 2018, entitled "Cardiac Information DYNAMIC DISPLAY SYSTEM AND Method (cardiac information dynamic display System and Method)", patent Cooperation treaty 35 USC 371 State application of application Ser. No. PCT/US2017/030915, filed 5, 2016, U.S. provisional application entitled "Cardiac Information DYNAMIC DISPLAY SYSTEM AND Method (cardiac information dynamic display System and Method)", ser. No. 62/331,351, each of which is incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: a catheter system titled "Catheter System and Methods of Medical Uses of Same,including Diagnostic and Treatment Uses for the Heart( and a method of medical use thereof filed 29 of 4/2020 including diagnostic and therapeutic use of the heart), a U.S. patent application serial No. 16/861,814 filed 19 of 6/2018 titled "Catheter System and Methods of Medical Uses of Same,Including Diagnostic and Treatment Uses for the Heart( catheter system and a method of medical use thereof including diagnostic and therapeutic use of the heart, a continuation of U.S. patent application 10,667,753, a U.S. patent application No. 10,667,753 titled "Catheter System and Methods of Medical Uses of Same,Including Diagnostic and Treatment Uses for the Heart( catheter system and a method of medical use thereof including diagnostic and therapeutic use of the heart filed 20 of 2015, a continuation of U.S. patent application No. 10,004,459, U.S. patent No. 10,004,459 entitled "Catheter System and Methods of Medical Uses of Same,Including Diagnostic and Treatment Uses for the Heart( catheter system and medical methods of use thereof, including diagnostic and therapeutic use of the heart, "35 USC 371 state phase application of the patent cooperation treaty of PCT/US2013/057579, which claims priority of U.S. provisional patent application entitled" SYSTEM AND Method for Diagnosing AND TREATING HEART Tissue (systems and methods for diagnosing and treating cardiac Tissue, "filed on month 31 2012, each of which is incorporated herein by reference, and methods of medical use thereof, entitled" diagnostic and therapeutic use of the heart, "serial No. 61/695,535.

Although the present application does not claim priority from the following applications, it may be related to the following applications: U.S. patent application serial No. 16/242,810 entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) ELECTRICAL PATHWAYS (expandable catheter assembly with flexible Printed Circuit Board (PCB) electrical pathway)", U.S. patent application serial No. 16/242,810 entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) ELECTRICAL PATHWAYS (expandable catheter assembly with flexible Printed Circuit Board (PCB) electrical pathway)", U.S. patent application serial No. 10,201,311 entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) ELECTRICAL PATHWAYS (expandable catheter assembly with flexible Printed Circuit Board (PCB) electrical pathway)", U.S. patent cooperation treaty 35 (USC national phase application serial No. PCT/US 2014/015261) entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) ELECTRICAL PATHWAYS (expandable catheter assembly with flexible Printed Circuit Board (PCB) electrical pathway)", U.S. patent application serial No. 10,201,311, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) ELECTRICAL PATHWAYS (expandable catheter assembly with flexible Printed Circuit Board (PCB) electrical pathway)", patent cooperation treaty claim to be filed on 2013, U.S. 2/fig. 8, and priority applications of patent application serial No. 61/762 are hereby incorporated by reference each of these patent applications.

Although the present application does not claim priority from the following applications, it may be related to the following applications: method and apparatus for determining and presenting surface charge and dipole density on the heart wall titled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls(, U.S. patent application serial No. 16/533,028, method and apparatus for determining and presenting surface charge and dipole density on the heart wall titled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls(, filed on 2018, 6, 21, are disclosed herein as "method and apparatus for determining and presenting surface charge and dipole density on the heart wall)", U.S. patent application serial No. 16/533,028, Continuation of U.S. patent application number 10,413,206, U.S. patent application number 10,413,206, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls( method and apparatus for determining and presenting surface charge and dipole density on the heart wall, filed on date 2 and 17 of 2017), continuation of U.S. patent application number 10,376,171, U.S. patent No. 10,376,171 entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls( method and apparatus for determining and presenting surface charge and dipole density on the heart wall, "filed on 25 th 2015, 9, etc.), Continuation of U.S. patent application number 9,610,024, U.S. patent application number 9,610,024, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls( method and apparatus for determining and presenting surface charge and dipole density on the heart wall, filed 11/19/2014, continuation of U.S. patent application number 9,167,982, U.S. patent No. 9,167,982 entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls( method and apparatus for determining and presenting surface charge and dipole density on the heart wall, "filed on 25.2.2014 Continuation of U.S. patent application No. 8,918,158, U.S. patent application No. 8,918,158, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls( method and apparatus for determining and presenting surface charge and dipole density on the heart wall filed on day 4, 2013, "continuation of U.S. patent application No. 8,700,119, U.S. patent application No. 8,700,119, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls( method and apparatus for determining and presenting surface charge and dipole density on the heart wall filed on day 2, 2009," method and apparatus for determining and presenting surface charge and dipole density on the heart wall, and "method and apparatus for determining and presenting surface charge and dipole density on the heart wall, respectively, are described herein Continuing with U.S. patent application No. 8,417,313, U.S. patent application No. 8,417,313 is filed on 8/3/2007 entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls( method and apparatus for determining and presenting surface charge and dipole density on heart wall, "35 USC 371 state-of-art application PCT application No. PCT/CH2007/000380, which claims priority from the swiss patent application No. 1251/06 filed on 8/3/2006, Each of the above applications is incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: apparatus and method for geometric determination of electric dipole density on heart wall filed on 9, 12, 2019) ", U.S. patent application No. 11,116,438, U.S. patent application No. 11,116,438, apparatus and method for geometric determination of electric dipole density on heart wall filed on 29, 2018, "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall()", continuation of U.S. patent application No. 10,463,267, apparatus and method for geometric determination of electric dipole density on heart wall filed on 10, 25, 10,463,267, continuation of U.S. patent application No. 9,913,589, U.S. patent application No. 9,913,589, "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall( for geometric determination of electric dipole density on heart wall filed on 10, U.S. patent application No. 9,504,395, U.S. patent No. 9,504,395 is filed on 7.19 of 2013 entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall( apparatus and method for geometric determination of electric dipole density on heart wall) ", continuation of U.S. patent No. 9,192,318, U.S. patent No. 9,192,318 is filed on 16 of 2010 entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall( apparatus and method for geometric determination of electric dipole density on heart wall), continuation of U.S. patent No. 8,512,255, U.S. patent No. 8,512,255 is filed on 16 of 2009 entitled "A Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall( apparatus and method for geometric determination of electric dipole density on heart wall), 35 USC 371 state of patent cooperation treaty of PCT/IB2009/000071 claiming priority of swiss patent application No. 00068/08 filed on 17 of 2008, each of the above applications is incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: apparatus and method for geometric determination of electric dipole density on heart wall, titled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall( filed on month 2, month 17, 2022), U.S. patent application Ser. No. 17/673,995, titled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall( filed on month 4, month 19, 2019, apparatus and method for geometric determination of electric dipole density on heart wall, continuation of U.S. patent application Ser. No. 11,278,209, U.S. patent application Ser. No. 11,278,209, titled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall( filed on month 3, month 20, apparatus and method for geometric determination of electric dipole density on heart wall, continuation of U.S. patent application Ser. No. 10,314,497, application Ser. No. 10,314,497, application Ser. No. "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall( filed on month 8, 2017, apparatus and method for geometric determination of electric dipole density on heart wall, continuation of U.S. patent application Ser. No. 9,968,268, U.S. patent No. 9,968,268 is filed on 6 of 2013, 9 entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall( apparatus and method for geometric determination of electric dipole density on heart wall) ", continuation of U.S. patent No. 9,757,044, U.S. patent No. 9,757,044 is filed on 9 of 2012, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall( apparatus and method for geometric determination of electric dipole density on heart wall)", 35 USC 371 national phase application of patent cooperation treaty of PCT/US2012/028593, which claims priority of U.S. provisional patent application serial No. 61/451,357 filed on 10 of 2011, each of which is incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: U.S. design patent No. 29/681,827 entitled "Set of Transducer-Electrode Pairs for A CATHETER (Transducer-electrode pair set for catheter)" filed on 2 nd 2019, U.S. design patent No. 29/681,827 entitled "Set of Transducer-Electrode Pairs for A CATHETER (Transducer-electrode pair set for catheter)" filed on 6 nd 2017, U.S. design patent No. D851,774 entitled "Transducer-Electrode Pair for A CATHETER (Transducer-electrode pair for catheter)" filed on 2 nd 2013, U.S. design patent No. D851,774, the U.S. design patent application No. D782,686 is filed 8.30.2013 entitled "Catheter System and Methods of Medical Uses of Same,Including Diagnostic and Treatment Uses for the Heart( catheter system and medical methods of use thereof, including diagnostic and therapeutic use of the heart, "35 USC 371 state phase application of the patent cooperation treaty filed 8.31.2012, entitled" SYSTEM AND Method for Diagnosing AND TREATING HEART Tissue (system and method for diagnosing and treating heart Tissue "), each of which is incorporated herein by reference, entitled" diagnostic and therapeutic use of the heart, "and methods of medical use thereof, entitled" diagnostic and therapeutic use of the heart, "and patent cooperation treaty application No. PCT/US2013/057579, each of which claims priority from U.S. provisional patent application No. 61/695,535.

Although the present application does not claim priority from the following applications, it may be related to the following applications: U.S. patent application Ser. No. 16/111,538 entitled "Gas-Elimination PATIENT ACCESS DEVICE (Gas Elimination patient access device)", U.S. patent application Ser. No. 16/111,538 entitled "Gas-Elimination PATIENT ACCESS DEVICE (Gas Elimination patient access device)", U.S. patent application Ser. No. 10,071,227, filed on Ser. No. 10,071,227 entitled "Gas-Elimination PATIENT ACCESS DEVICE (Gas Elimination patient access device)", patent cooperation treaty entitled "Gas-Elimination PATIENT ACCESS DEVICE (Gas Elimination patient access device)", U.S. provisional patent application Ser. No. 61/704,704, filed on even 14 th 2015, entitled "patent cooperation No. 35 USC 371 national stage application of PCT/US 2015/01312, which claims priority from U.S. provisional patent application entitled" Gas-Elimination PATIENT ACCESS DEVICE (Gas Elimination patient access device) ", filed on even 17 of 2014, ser. No. 61/704, which application is incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: U.S. patent application Ser. No. 17/578,522 entitled "CARDIAC ANALYSIS User INTERFACE SYSTEM AND Method," U.S. patent application Ser. No. 17/578,522 entitled "CARDIAC ANALYSIS User INTERFACE SYSTEM AND Method," U.S. patent application Ser. No. 11,278,231 entitled "CARDIAC ANALYSIS User INTERFACE SYSTEM AND Method," U.S. patent application Ser. No. 11,278,231 entitled "CARDIAC ANALYSIS User INTERFACE SYSTEM AND Method," U.S. patent application Ser. No. 2015, 3, 24, and U.S. patent application Ser. No. 35/371, entitled "CARDIAC ANALYSIS User INTERFACE SYSTEM AND Method," U.S. patent application Ser. No. 61/970,027, "filed on day 2014, and the 35 national phase application of the cooperation treaty of PCT/US2015/022187, which claims priority from U.S. patent application Ser. No. 5,5329 Method," U.S. No. 2014, 3, incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: U.S. patent application serial No. 17/063,901 entitled "DEVICES AND Methods for Determination of Electrical Dipole Densities on a Cardiac Surface (apparatus and method for determining electric dipole density on a heart surface)", U.S. patent application serial No. 17/063,901 entitled "DEVICES AND Methods for Determination of Electrical Dipole Densities on a Cardiac Surface (apparatus and method for determining electric dipole density on a heart surface)", U.S. patent application serial No. 10,828,011 entitled "DEVICES AND Methods for Determination of Electrical Dipole Densities on a Cardiac Surface (apparatus and method for determining electric dipole density on a heart surface)", U.S. patent application serial No. 2014, 9, 10, entitled "35 USC 371 state application of the patent cooperation treaty of PCT/US2014/054942, which claims priority from U.S. patent application serial No. DEVICES AND Methods for Determination of Electrical Dipole Densities on a Cardiac Surface (apparatus and method for determining electric dipole density on a heart surface)", U.S. patent application serial No. 61/877,617, filed on 2013, 9, and 9, which are hereby incorporated by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: united states patent application serial No. 16/849,045 entitled "Localization SYSTEM AND Method Useful in the Acquisition AND ANALYSIS of Cardiac Information (positioning system and method useful for the acquisition and analysis of cardiac information)", united states patent application serial No. 16/849,045 entitled "Localization SYSTEM AND Method Useful in the Acquisition AND ANALYSIS of Cardiac Information (positioning system and method useful for the acquisition and analysis of cardiac information)", united states patent application serial No. 10,653,318, united states patent application serial No. 10,653,318 entitled "Localization SYSTEM AND Method Useful in the Acquisition AND ANALYSIS of Cardiac Information (positioning system and method useful for the acquisition and analysis of cardiac information)", united states patent application serial No. 35 USC 371 state phase of patent cooperation treaty of PCT/US 2016/03420, united states provisional patent application serial No. Localization SYSTEM AND Method Useful in the Acquisition AND ANALYSIS of Cardiac Information (positioning system and method useful for the acquisition and analysis of cardiac information) ", united states patent application serial No. 62/161,213, filed on day 2016, 13, and united states patent cooperation treaty claims 35 USC 371 state phase application of patent cooperation treaty entitled" Localization SYSTEM AND Method Useful in the Acquisition AND ANALYSIS of Cardiac Information (positioning system and method useful for the acquisition and analysis of cardiac information) ", united states provisional patent application serial No. 62/161,213, which are incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: U.S. patent application serial No. 15/569,231, entitled "Cardiac Virtualization TEST TANK AND TESTING SYSTEM AND Method (cardiac virtualization test box and test system and Method)", filed on 10, 25, 2017, was filed on even date with the 35 USC 371 national phase application of the patent cooperation treaty filed on 11, 5, 2016/031823, which claims priority from U.S. provisional patent application entitled "Cardiac Virtualization TEST TANK AND TESTING SYSTEM AND Method (cardiac virtualization test box and test system and Method)", filed on 12, 5, 2015, 62/160,501, which is incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: U.S. patent application Ser. No. 17/735,285 entitled "Ultrasound Sequencing SYSTEM AND Method," U.S. patent application Ser. No. 17/735,285 entitled "Ultrasound Sequencing SYSTEM AND Method," U.S. patent application Ser. No. 15/569,185, "and further entitled" 35 USC 371 national stage application of the patent cooperation treaty of PCT/US 2016/03017, filed 5/12, and entitled "Ultrasound Sequencing SYSTEM AND Method," U.S. provisional patent application Ser. No. 62/160,529, filed 5/12, which is incorporated herein by reference, filed 17/735/285, which claims priority from U.S. provisional patent application Ser. No. 62/160,529, filed 5/12, which is entitled "Ultrasound Sequencing SYSTEM AND Method," U.S. provisional patent application of ultrasonic sequencing System and Method, "U.S. No. 17/735.

Although the present application does not claim priority from the following applications, it may be related to the following applications: the U.S. patent application Ser. No. 17/858174 entitled "CARDIAC MAPPING SYSTEM WITH EFFICIENCY Algorithm (cardiac mapping System with high efficiency Algorithm)", U.S. patent application Ser. No. 17/858174 entitled "CARDIAC MAPPING SYSTEM WITH EFFICIENCY Algorithm (cardiac mapping System with high efficiency Algorithm)", U.S. patent application Ser. No. 16/097,959, entitled "CARDIAC MAPPING SYSTEM WITH EFFICIENCY Algorithm (cardiac mapping System with high efficiency Algorithm)", 35 USC 371-national phase application Ser. No. 17/858174, entitled "CARDIAC MAPPING SYSTEM WITH EFFICIENCY Algorithm (cardiac mapping System with high efficiency Algorithm)", U.S. provisional patent application Ser. No. 62/097,959, entitled "and entitled" 35 USC-national phase application with 35/35,331 with high efficiency Algorithm) ", entitled" by PCT/US2017/030922, which patent application Ser. No. 10/26, entitled "CARDIAC MAPPING SYSTEM WITH EFFICIENCY Algorithm (cardiac mapping System with high efficiency Algorithm)", U.S. 62/104, and "entitled" US provisional patent application Ser. 62/097,959, entitled "by No. 5/2016, and entitled" cardiac mapping System with high efficiency Algorithm ", by way of which patent application No. 34 is filed by priority, and by the applicant of the disclosure of the patent application of the applicant of the year of the patent of the year of" 35/that is incorporated herein by reference of priority by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: U.S. patent application Ser. No. 16/961,809 entitled "System for IDENTIFYING CARDIAC Conduction Patterns (System for identifying cardiac conduction mode)" filed on 7/13/2020, entitled "System for IDENTIFYING CARDIAC Conduction Patterns (System for identifying cardiac conduction mode)" filed on 22/2019, 35 USC 371 national phase application of the patent Cooperation treaty of application Ser. No. PCT/US2019/014498, entitled "System for Recognizing Cardiac Conduction Patterns (System for identifying cardiac conduction mode)" filed on 21/2018, U.S. provisional patent application Ser. No. 62/619,897, and entitled "System for IDENTIFYING CARDIAC Conduction Patterns (System for identifying cardiac conduction mode)" filed on 5/2018, each of which is incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: U.S. patent application Ser. No. 17/048,151 entitled "Cardiac Information Processing System (cardiac information processing System)", U.S. patent application Ser. No. 17/048,151 entitled "Cardiac Information Processing System (cardiac information processing System)", patent Cooperation treaty 35USC371 national phase application of application Ser. No. PCT/US2019/031131, entitled "Cardiac Information Processing System (cardiac information processing System)", U.S. provisional application Ser. No. 62/668,659, and entitled "Cardiac Information Processing System (cardiac information processing System)", U.S. provisional patent application Ser. No. 62/811,735, each of which is incorporated herein by reference, filed on 8/2018.

Although the present application does not claim priority from the following applications, it may be related to the following applications: patent cooperation treaty filed on 8.11.2019 entitled "SYSTEMS AND Methods for Calculating Patient Information (system and method for calculating patient information)", PCT/US2019/060433, which claims priority from U.S. provisional application entitled "SYSTEMS AND Methods for Calculating Patient Information (system and method for calculating patient information)", serial No. 62/757,961, each of which is incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: U.S. patent application Ser. No. 17/601,661 entitled "System for CREATING A Composite Map" filed on 5 th year 2021, entitled "System for CREATING A Composite Map" filed on 17 th year 2020, 35 USC 371 State application of patent cooperation treaty having application number PCT/US2020/028779, entitled "System for CREATING A Composite Map" filed on 18 th year 2019, entitled "System for CREATING A Composite Map" filed on 23 th year 2019, and priority of U.S. provisional application Ser. No. 62/925,030, each of which is incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: U.S. patent application Ser. No. 17/613,249 entitled "SYSTEMS AND Methods For Performing Localization Within A Body (System and method for performing positioning in vivo)", U.S. patent application Ser. No. 17/613,249, entitled "SYSTEMS AND Methods For Performing Localization Within A Body (System and method for performing positioning in vivo)", patent Cooperation treaty 35 USC 371 State application, application Ser. No. PCT/US2020/036110, filed on even date 4 at month 6 of 2019, entitled "SYSTEMS AND Methods For Performing Localization Within A Body (System and method for performing positioning in vivo)", U.S. provisional application Ser. No. 62/857,055, each of which is incorporated herein by reference.

Although the present application does not claim priority from the following applications, it may be related to the following applications: U.S. patent application Ser. No. 17/777,104 entitled "Tissue TREATMENT SYSTEMS, devices, and Methods", U.S. patent application Ser. No. 17/777,104 entitled "Tissue TREATMENT SYSTEMS, devices, and Methods", filed on day 5/month 16 of 2022, 35 USC 371 national stage application of the patent cooperation treaty entitled "Tissue TREATMENT SYSTEMS, devices, and Methods", filed on day 11/month 22 of 2019, entitled "Tissue TREATMENT SYSTEMS, devices, and Methods", filed on day 9/month 7 of 2020, devices, and Methods ", and entitled" Tissue TREATMENT SYSTEMS, devices, and Methods ", U.S. provisional application Ser. No. 63/provisional 075,280", each of which is hereby incorporated by reference.

Field of the inventive concept

The present inventive concept relates generally to systems, devices, and methods for ablating tissue, and in particular, for ablating tissue of a patient's heart.

Background

Many medical procedures include mapping or other diagnosis of tissue, or delivering energy to ablate or otherwise treat tissue. Achieving the desired specificity and efficacy of tissue diagnosis and treatment can be challenging, and failure to do so can lead to less than desired results.

There is a need for systems, methods, and devices that enable improved tissue treatment via delivery of energy.

Disclosure of Invention

According to aspects of the inventive concept, a cardiac information dynamic display system includes one or more electrodes and a cardiac information console. The one or more electrodes are configured to record potential data sets representative of heart activity at a plurality of time intervals. The cardiac information console includes a signal processor and a user interface module. The signal processor is configured to calculate a cardiac activity dataset at a plurality of time intervals using the recorded potential dataset. The cardiac activity data is associated with surface locations of one or more heart chambers. The user interface module displays information related to the cardiac activity data. This information is presented relative to a graphical representation of the surface of one or more heart chambers.

According to another aspect of the inventive concept, a method of dynamic display of cardiac information includes recording a potential dataset representing cardiac activity at a plurality of time intervals using one or more electrodes. The method further includes using a cardiac information console including a signal processor and a user interface module to: calculating a cardiac activity dataset at a plurality of time intervals using the recorded potential dataset, wherein the cardiac activity data is associated with surface locations of one or more cardiac chambers; and displaying information related to the cardiac activity data, the information being presented relative to a graphical representation of a surface of one or more cardiac chambers.

The technology described herein, together with its attributes and attendant advantages, will be best appreciated and understood in view of the following detailed description, taken in conjunction with the accompanying drawings, in which representative embodiments are described by way of example.

Incorporated by reference

All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. The contents of all publications, patents and patent applications mentioned in this specification are herein incorporated by reference in their entirety for all purposes.

Drawings

Fig. 1 shows a schematic diagram of an embodiment of a system configured to enable a medical procedure to be performed on a patient consistent with the inventive concept.

Fig. 2 shows an anatomical model representing a tissue surface consistent with the inventive concept.

Fig. 3 illustrates an example of a graphical user interface displaying cardiac mapping data consistent with the inventive concepts.

Fig. 4A and 4B show examples of a graphical user interface including an anatomical model and an anatomical model including various markers, respectively, consistent with the inventive concepts.

Fig. 5A and 5B illustrate flow diagrams representing embodiments of machine learning or other artificial intelligence based heart rhythm classification methods and cardiac activity data representations, respectively, consistent with the inventive concepts.

Fig. 6 illustrates various embodiments of an anatomical model on which a map of cardiac activity is displayed, consistent with the inventive concepts.

Fig. 7 illustrates various embodiments of an anatomical model on which a map of cardiac activity is displayed, consistent with the inventive concepts.

Fig. 8A illustrates an embodiment of a spatiotemporal representative diagram of cardiac activity consistent with the inventive concepts.

Fig. 8B shows an embodiment of a color-coded map of cardiac activity consistent with the inventive concepts.

Detailed Description

Reference will now be made in detail to embodiments of the present technology, examples of which are illustrated in the accompanying drawings. Like reference numerals may be used to refer to like components. However, the description is not intended to limit the disclosure to the particular embodiments and should be construed to include various modifications, equivalents, and/or alternatives to the embodiments described herein.

It will be understood that, as used herein, the terms "comprises" (and any form of comprising, such as "comprises" and "comprising"), "having" (and any form of having, such as "having" and "having"), "including" (and any form of comprising, such as "including" and "including") or "containing" (and any form of containing, for example, "contain" and "contain" specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It will be further understood that, although the terms first, second, third, etc. may be used herein to describe various limitations, elements, components, regions, layers and/or sections, these limitations, elements, components, regions, layers and/or sections should not be limited by these terms. These terms are only used to distinguish one restriction, element, component, region, layer or section from another restriction, element, component, region, layer or section. Thus, a first restriction, element, component, region, layer or section discussed below could be termed a second restriction, element, component, region, layer or section without departing from the teachings of the present application.

It will also be understood that when an element is referred to as being "on," "attached to," "connected to," or "coupled to" another element, it can be directly on or over the other element or be directly connected or coupled to the other element or one or more intervening elements may be present. In contrast, when an element is referred to as being "directly on," "directly attached," "directly connected" or "directly coupled" to another element, there are no intervening elements present. Other words used to describe the relationship between elements should be interpreted in a similar fashion (e.g., "between …" and "directly between …", "adjacent" and "directly adjacent", etc.).

It will also be understood that when a first element is referred to as being "in," "on," and/or "within" a second element, it can be positioned: within the interior space of the second element, within a portion of the second element (e.g., within a wall of the second element); is positioned on the outer surface and/or the inner surface of the second element; and combinations of one or more of these.

As used herein, the term "proximate" when used to describe a first component or first location being proximate to a second component or second location will be considered to include: one or more locations proximate to the second component or second location, and locations in, on, and/or within the second component or second location. For example, components positioned proximate to an anatomical site (e.g., a target tissue location) should include: a component positioned proximate to the anatomical site, and a component positioned in, on, and/or within the anatomical site.

For example, as shown in the figures, spatially relative terms such as "below," "lower," "above," "upper," and the like may be used to describe an element and/or a characteristic relationship to another element and/or feature. It will also be understood that the spatially relative terms are intended to encompass different orientations of the device in use and/or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "lower" and/or "lower" than other elements or features would then be oriented "upper" the other elements or features. The device may be otherwise oriented (e.g., rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.

The terms "reduce", "decrease", and the like as used herein shall include a reduction in quantity, including a reduction to zero. Reducing the likelihood of occurrence should include preventing occurrence. Accordingly, the terms "prevent", "inhibit" and "prevent" shall include "reduce", "decrease" and "reduce" actions, respectively.

The term "and/or" as used herein is to be taken as a specific disclosure of each of the two specified features or components with or without the other. For example, "a and/or B" should be considered as a specific disclosure of each of (i) a, (ii) B, and (iii) a and B, as if each were individually recited herein.

The term "one or more" as used herein may refer to one, two, three, four, five, six, seven, eight, nine, ten or more, up to any number.

The terms "and combinations thereof" and "combinations of these" may be used individually herein after a list of items that are individually or collectively included. For example, components, processes, and/or other items selected from: A. b, C, and combinations thereof, will include a set of one or more components, the set of one or more components including: one, two, three or more of items a; one, two, three or more of items B; and/or one, two, three or more of item C.

In this specification, unless explicitly stated otherwise, "and" may mean "or", "or" may mean "and". For example, if a feature is described as having A, B or C, the feature may have any combination of A, B and C, or A, B and C. Similarly, if a feature is described as having A, B and C, the feature may have only one or two of A, B or C.

As used herein, when a quantifiable parameter is described as having a value between a first value X and a second value Y, it will include parameters having the following values: at least X, not greater than Y, and/or at least X and not greater than Y. For example, a length between 1 and 10 should include a length of at least 1 (including values greater than 10), a length of less than 10 (including values less than 1), and/or values greater than 1 and less than 10.

The expression "configured (or arranged)" as used in the present disclosure may be used interchangeably with, for example, the expressions "adapted to", "having the ability to", "designed to", "adapted to", "made to", and "capable of" as appropriate. The expression "configuration (or setting)" means not only "special design" in hardware. Alternatively, in some cases, the expression "a device is configured to" may mean that the device is "operable" with another device or component.

As used herein, the term "threshold" refers to a maximum level, minimum level, and/or range of values associated with a desired or undesired state. In some embodiments, the system parameters are maintained above a minimum threshold, below a maximum threshold, within a threshold range of values, and/or outside a threshold range of values, such as to cause a desired effect (e.g., effective treatment) and/or to prevent or otherwise reduce (hereinafter referred to as "prevent") undesired events (e.g., device and/or clinical adverse events). In some embodiments, the system parameter is maintained above a first threshold (e.g., above a first temperature threshold to cause a desired therapeutic effect on the tissue) and below a second threshold (e.g., below a second temperature threshold to prevent undesired tissue damage). In some embodiments, the threshold is determined to include a safety margin in order to account for patient variability, system variability, tolerability, and the like. As used herein, "exceeding a threshold" refers to a parameter exceeding a maximum threshold, being below a minimum threshold, being within a threshold range, and/or being outside a threshold range.

As described herein, "indoor pressure" shall refer to the pressure of the environment surrounding the systems and devices of the present inventive concept. Positive pressure includes a pressure that is higher than the pressure within the chamber or simply greater than another pressure, such as a positive differential pressure across a fluid path component (such as a valve). Negative pressure includes a pressure that is lower than the pressure within the chamber or less than another pressure, such as a negative differential pressure across a fluid component path (such as a valve). The negative pressure may comprise a vacuum, but does not mean a pressure lower than the vacuum. As used herein, the term "vacuum" may be used to refer to a full or partial vacuum, or any negative pressure as described above.

The term "diameter" as used herein to describe non-circular geometries is considered to be the diameter of an imaginary circle approximating the described geometry. For example, when describing a cross-section (such as a cross-section of a component), the term "diameter" should be taken to mean the diameter of an imaginary circle having the same cross-sectional area as the cross-section of the component being described.

The terms "major axis" and "minor axis" of a component as used herein are the length and diameter, respectively, of an imaginary cylinder of minimum volume that can completely enclose the component.

As used herein, the term "functional element" is considered to include one or more elements constructed and arranged to perform a function. The functional elements may include sensors and/or transducers. In some embodiments, the functional element is configured to deliver energy and/or otherwise treat tissue (e.g., the functional element is configured as a therapeutic element). Alternatively or additionally, the functional element (e.g., the functional element including the sensor) may be configured to record one or more parameters, such as patient physiological parameters, patient anatomical parameters (e.g., tissue geometry parameters), patient environmental parameters, and/or system parameters. In some embodiments, the sensor or other functional element is configured to perform a diagnostic function (e.g., collect data for performing a diagnosis). In some embodiments, the functional element is configured to perform a therapeutic function (e.g., to deliver therapeutic energy and/or therapeutic agents). In some embodiments, the functional elements include one or more elements constructed and arranged to perform a function selected from the group consisting of: delivering energy, extracting energy (e.g., to cool a component), delivering a drug or other agent, manipulating a system component or patient tissue, recording or otherwise sensing a parameter such as a patient physiological parameter or system parameter, and combinations of one or more of these. The functional element may comprise a fluid and/or a fluid delivery system. The functional element may include a reservoir, such as an inflatable balloon or other fluid retaining reservoir. "functional components" may include components constructed and arranged to perform functions, such as diagnostic and/or therapeutic functions. The functional components may include extensible components. The functional component may include one or more functional elements.

The term "transducer" as used herein is considered to include any component or combination of components that receives energy or any input and produces an output. For example, the transducer may include electrodes that receive electrical energy and distribute the electrical energy to tissue (e.g., based on the size of the electrodes). In some configurations, the transducer converts an electrical signal into any output, such as light (e.g., the transducer includes a light emitting diode or bulb), sound (e.g., the transducer includes a piezoelectric crystal configured to deliver ultrasonic energy), pressure (e.g., an applied pressure or force), thermal energy, cryogenic energy, chemical energy, mechanical energy (e.g., the transducer includes a motor or solenoid), magnetic energy, and/or a different electrical signal (e.g., an input signal different from the transducer). Alternatively or additionally, the transducer may convert a physical quantity (e.g., a change in a physical quantity) into an electrical signal. The transducer may include any component that delivers energy and/or medicament to tissue, such as a transducer configured to deliver one or more of the following: electrical energy to tissue (e.g., a transducer comprising one or more electrodes), optical energy to tissue (e.g., a transducer comprising a laser, a light emitting diode, and/or an optical component such as a lens or prism), mechanical energy to tissue (e.g., a transducer comprising a tissue manipulation element), acoustic energy to tissue (e.g., a transducer comprising a piezoelectric crystal), chemical energy, electromagnetic energy, magnetic energy, and combinations of one or more of these.

As used herein, the term "fluid" may refer to a liquid, gas, gel, or any flowable material, such as a material that may be advanced through a lumen and/or opening.

As used herein, the term "material" may refer to a single material or a combination of two, three, four or more materials.

It is appreciated that certain features of the inventive concept which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the inventive concept which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination. For example, it should be understood that all of the features set forth in any of the claims (whether independent or dependent) may be combined in any given manner.

It is to be understood that at least some of the figures and descriptions of the present inventive concept have been simplified to focus on elements that are relevant for a clear understanding of the present inventive concept, while eliminating, for purposes of clarity, other elements that one of ordinary skill in the art will recognize may also comprise a portion of the present inventive concept. However, because such elements are well known in the art, and because they do not necessarily facilitate a better understanding of the inventive concepts, a description of such elements is not provided herein.

The terminology defined in this disclosure is for the purpose of describing particular embodiments of this disclosure only and is not intended to be limiting of the scope of this disclosure. Terms provided in the singular are also intended to include the plural unless the context clearly indicates otherwise. Unless otherwise defined herein, all terms (including technical or scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the relevant art. Unless explicitly defined as such herein, terms defined in a dictionary generally used should be construed to have the same or similar meaning as the context of the related art and should not be construed to have ideal or exaggerated meanings. In some cases, the terms defined in the present disclosure should not be construed as excluding the embodiments of the present disclosure.

Provided herein are systems, devices, and methods for treating a target tissue of a patient, such as providing therapeutic benefits to the patient. The energy delivery console may be configured to deliver various "doses" of energy delivered by one or more energy delivery devices in order to ablate, cause necrosis of, and/or otherwise therapeutically alter the target tissue. The one or more energy delivery devices may include catheters and/or surgical tools that include electrodes and/or other energy delivery elements. In some embodiments, multiple interdependent energy doses are delivered to a common tissue site, such as to provide improved therapeutic benefit to a patient. The initial dose may be configured to warm tissue, such as the delivery of Radio Frequency (RF), heat, and/or other energy. Subsequent doses may include energy doses configured to irreversibly electroporate tissue that has previously been warmed, such as when the tissue is at an elevated temperature (e.g., above body temperature).

Referring now to fig. 1, there is shown a schematic diagram of an embodiment of a system configured to enable a medical procedure to be performed on a patient (e.g., a human or other living mammal) consistent with the present concepts. The medical procedure may include a diagnostic procedure, a therapeutic procedure, or a combined diagnostic and therapeutic procedure that may be performed by a clinician and/or other user (herein an "operator" or "user"). The system 10 includes a console 100, the console 100 including one or more discrete components (e.g., individual boxes) that are connected to various components of the system to provide power thereto, record information from the system 10 described herein, and/or otherwise implement one or more functions of the system 10 described herein. The system 10 also includes one or more diagnostic catheters, shown as mapping catheter 200. In some embodiments, the system 10 includes: one or more treatment catheters, treatment catheter 310; one or more functional conduits 320; one or more additional diagnostic catheters, diagnostic catheter 330; one or more patient patches, patches 340; one or more patient leads (lead), EKG (electrocardiogram) leads 350; and/or one or more delivery devices, sheath 360. The console 100 is operably attached (e.g., electrically, mechanically, fluidly, sonically, and/or optically) to one or more catheters or other devices 200, 310, 320, 330, 340, 350, and/or 360.

Mapping catheter 200 may include an array of elements (array 210, handle 202) and an elongate filament (shaft 201) therebetween. The array 210 may include a radially expandable array, such as an array that is elastically biased in a radially expanded geometry. In some embodiments, the array 210 may include a plurality of radially expandable arms (splines 213). Alternatively or additionally, the array 210 may include a balloon, radially expandable cage, or other expandable structure. The array 210 may include one or more functional elements, such as one or more electrodes (electrode 211), one or more ultrasound transducers (UST (ultrasound transducer, ultrasound transducer) 212), and/or one or more other functional elements (functional element 219).

The mapping catheter 200 may have a similar construction and arrangement as similar components described in applicant's co-pending application below: a catheter system of "Catheter System and Methods of Medical Uses of Same,including Diagnostic and Treatment Uses for the Heart( and a method of medical use thereof, including diagnostic and therapeutic use of the heart, filed on 29 th month 4 2020, U.S. patent application serial No. 16/861,814; U.S. patent application Ser. No. 16/242,810, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) ELECTRICAL PATHWAYS (an expandable catheter assembly with flexible Printed Circuit Board (PCB) electrical pathway)", filed on 1/8 of 2019; and U.S. patent application Ser. No. 17/735,285, entitled "Cardiac Virtualization TEST TANK AND TESTING SYSTEM AND Method (cardiac virtualization test Box and test System and Method)", filed 5/3/2022.

Treatment catheter 310 may include an elongate filament (shaft 311) with handle 312 at its proximal end. The treatment catheter 310 may include one or more functional elements (functional elements 319) positioned on a distal portion of the shaft 311 (e.g., at least one functional element 319 positioned on a distal end of the shaft 311). In some embodiments, functional element 319 includes one or more electrodes configured to deliver electrical energy (e.g., RF energy) to tissue, such as to thermally ablate tissue. Additionally or alternatively, functional element 319 can include one or more electrodes configured to generate an electric field therebetween, e.g., to electroporate tissue within the field (e.g., irreversibly electroporate tissue).

Treatment catheter 310 may have a similar construction and arrangement as similar components described in applicant's co-pending application below: U.S. application Ser. No. 16/335,893, entitled "Ablation SYSTEM WITH Force Control," filed on 3/22 in 2019; U.S. application Ser. No. 17/777,104, entitled "Tissue TREATMENT SYSTEMS, devices, and Methods," filed on 5/16, 2022; and patent cooperation treaty filed on day 17 of 2022 under the heading "ENERGY DELIVERY SYSTEMS WITH absorption Index (energy delivery System with Ablation Index)", application number PCT/US 2022/016722.

The functional catheter 320 may include an elongate filament (shaft 321) with a handle 322 at its proximal end. The functional catheter 320 may include one or more functional elements (functional elements 329) positioned on a distal portion of the shaft 321 (e.g., an array of at least 10 functional elements 329, such as the 13 elements shown, positioned on the distal portion of the shaft 321). In some embodiments, functional element 329 comprises one or more electrodes, e.g., one or more electrodes configured to record biopotential signals and/or other electrical signals from cardiac tissue.

Diagnostic catheter 330 may comprise a catheter that includes one or more functional elements (functional element 339). In some embodiments, the diagnostic catheter 330 comprises a coronary sinus (coronary sinus, CS) mapping catheter that is constructed and arranged to be positioned within the CS of the heart (e.g., to position the functional element 339 within the CS). The functional element 339 may include one or more electrodes configured to record biopotential signals and/or other electrical signals from heart tissue.

Patch 340 may include one or more patches configured for application to the skin of a patient (e.g., on the torso of a patient). Patch 340 may include one or more functional elements (functional elements 349). The functional elements 349 may include electrodes configured to generate an electric field within the patient (e.g., an electric field generated between at least two patches 340). The system 10 may be configured to position one or more devices thereof within and/or on the patient by measuring the electric field generated between the patches 340 (e.g., via impedance-based positioning as described herein).

The EKG leads 350 may include one or more patient patches configured to record electrical signals (e.g., cardiac electrical signals) from a patient. A plurality of EKG leads 350 may be positioned around the torso of the patient as shown.

Sheath 360 may include an elongate tube (shaft 361) including at least one lumen (lumen 363) therethrough. Sheath 360 may include at least one functional element, shown as functional element 369. Sheath 360 may be constructed and arranged to be advanced intravascularly into a chamber of the heart (e.g., into the left atrium of the heart via an atrial septum puncture). The lumen 363 of the sheath 360 may slidably receive one or more devices of the system 10, such as a distal portion of the mapping catheter 200 (e.g., when the array 210 is of radially collapsed geometry), such that the devices may be advanced from the distal end of the lumen 363 and into the chamber of the heart. For example, the array 210 of mapping catheters 200 may be advanced in a radially collapsed geometry through the lumen 363 of the sheath 360, out of the lumen 363 into the left atrium of the heart, and converted to a radially expanded geometry. In some embodiments, the lumen 363 comprises two or more lumens configured for each slidably receiving a device of the system 10, and/or the lumen 363 is constructed and arranged to simultaneously receive multiple devices, such as to allow multiple devices (e.g., the mapping catheter 200, the treatment catheter 310, and/or the functional catheter 320) to be inserted into the left atrium through a single atrial septum penetration.

As used herein, devices 310, 320, 330, 340, 350, and/or 360 may be referred to individually or collectively as patient device 300.

The console 100 may include a patient interface module 101, the patient interface module 101 being configured to operatively attach one or more patient devices (e.g., one or more catheters or other devices described herein) to one or more components of the console 100. The patient interface module 101 may include circuitry configured to protect a patient (such as from undesired shocks caused by the console 100) and/or to protect components of the console 100 from shocks (such as from defibrillation pulses or other energy delivered to the patient).

The console 100 may include a processing unit 110. The processing unit 110 may comprise at least one microprocessor, a computer, and/or another electronic controller (processor 111). The processing unit 110 may also include one, two or more algorithms, shown as algorithm 115. The processing unit 110 may include a memory 112 for storing instructions for executing an algorithm 115. The processor 111, via the algorithm 115, may perform one or more of the processes described herein, such as processes performed in response to one or more commands entered into the system 10 by a user (e.g., via the user interface 120 described herein). The processing unit 110 may receive signals, such as signals from one, two, or more functional elements of the devices 200 and/or 300 (e.g., signals from one, two, or more sensor-based functional elements of the devices). The processing unit 110 may be configured to perform one or more mathematical operations based on the received signals and produce results related to physiological parameters of the patient and/or to operating parameters related to at least one device of the system 10.

The console 100 may include an interface (user interface 120) for providing information to a user of the system 10 and/or receiving information from a user of the system 10. The user interface 120 may include one, two, or more user input and/or user output components. For example, the user interface 120 may include a joystick, keyboard, mouse, microphone, touch screen, and/or other input device. Additionally or alternatively, the user interface 120 may include speakers, haptic feedback devices, indicator lights, and/or other output devices. In some embodiments, the user interface 120 includes one or more displays, such as a touch screen or other display for providing graphical visual information to a user. The processing unit 110 may provide a graphical user interface (GUI (GRAPHICAL USER INTERFACE, graphical user interface) 125) for presentation to a user via the user interface 120.

The system 10 may include one or more modules for generating an output signal (e.g., a signal to be delivered to the device 200 and/or 300), receiving data (e.g., one or more recorded signals from the device 200 and/or 300), processing the received data (e.g., via the algorithm 115), and/or generating output data based at least in part on the processed data. For example, the system 10 may include a biopotential module 130, a localization module 140, an anatomical module 150, an imaging module 160, a mapping module 170, and/or a treatment module 180.

Biopotential module 130 may generate one or more outputs related to the electrical activity of the patient, such as dipole density information, surface charge information, and/or voltage information related to the activity of the patient's heart. Biopotential module 130 may have a similar construction and arrangement as similar components described in applicant's following application: U.S. patent No. 11,013,444 entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls( method and apparatus for determining and presenting surface charge and dipole density on heart wall, "filed on 8/6/2019; apparatus and method for geometric determination of electric dipole density on heart wall filed on 2019, 9, 12, ", U.S. patent No. 11,116,438; U.S. design patent No. D954,970 entitled "Set of Transducer-Electrode Pairs for A CATHETER (transducer-electrode pair set for catheter)" filed on 28, 2, 2019; and co-pending U.S. patent application serial No. 16/097,959, entitled "CARDIAC MAPPING SYSTEM WITH EFFICIENCY Algorithm (cardiac mapping System with efficient Algorithm)", filed on 10/31/2018.

The positioning module 140 may generate one or more outputs related to the position of one or more components of the system 10 relative to the patient P (such as relative to a coordinate system established by the positioning module 140). The positioning module 140 may have a similar construction and arrangement as similar components described in applicant's co-pending application: U.S. patent application Ser. No. 16/849,045, entitled "Localization SYSTEM AND Method Useful in the Acquisition AND ANALYSIS of Cardiac Information (positioning System and method useful for acquisition and analysis of cardiac information)" filed on month 4 and 15 of 2020.

The dissection module 150 may generate one or more outputs related to the dissection of the patient P, such as the size, shape, and/or structure of at least a portion (e.g., a chamber) of the heart H of the patient P. The dissection module 150 may have a similar construction and arrangement as similar components described in applicant's following application: U.S. design patent No. D954970 entitled "Set of Transducer-Electrode Pairs for A CATHETER (transducer-electrode pair set for catheter)" filed on 28, 2, 2019; and co-pending U.S. patent application Ser. No. 17/735,285, entitled "Cardiac Virtualization TEST TANK AND TESTING SYSTEM AND Method (cardiac virtualization test Box and test System and Method)", filed on month 5 of 2022.

The imaging module 160 may provide, generate, acquire, update, store, and maintain at least one image of the heart or at least one heart chamber of the heart H. The imaging module 160 may include at least one imaging device configured to record image data. For example, the imaging module 160 may include an imaging device selected from the group consisting of: a computed tomography (computerized tomography, CT) scanner; a fluoroscope; an X-ray imager; an MRI (magnetic resonance imaging ) scanner; an ultrasound imager; and combinations of these. In some embodiments, imaging module 160 receives and/or stores image information from an imaging device. Alternatively or additionally, imaging module 160 may be configured to receive image data from an imaging device separate from system 10. In some embodiments, the imaging module 160 and the anatomical module 150 are configured to provide, generate, and/or update an anatomical model, such as an anatomical model of at least a portion of a patient's heart, based on image data from the imaging device.

The mapping module 170 may receive cardiac activity information (e.g., information recorded by the biopotential module 130 from the devices 200 and/or 300) and generate one or more maps of cardiac activity. For example, the mapping module 170 may generate one or more dipole densities, surface charges, and/or voltage maps based on the recorded electrocardiographic activity. The mapping module 170 may have a similar construction and arrangement as similar components described in applicant's co-pending application below: U.S. patent application Ser. No. 17/673,995, titled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall(, apparatus and method for geometrically determining electric dipole density on heart wall, "filed on 2, 17, 2022; and U.S. application Ser. No. 16/097,955, entitled "Cardiac Information DYNAMIC DISPLAY SYSTEM AND Method (dynamic display System and Method for cardiac information)", filed on 10/31/2018.

The treatment module 180 may be configured to cause or drive a device of the system 10 (e.g., the treatment catheter 310) for delivering treatment energy to one or more locations of the patient's heart. In some embodiments, the treatment module 180 provides closed loop energy delivery based on mapping and/or other information generated by the system 10. The treatment module 180 may have a similar construction and arrangement as similar components described in applicant's co-pending application below: a catheter system of "Catheter System and Methods of Medical Uses of Same,including Diagnostic and Treatment Uses for the Heart and a method of medical use thereof, including diagnostic and therapeutic use of the heart, filed on 29 th month 4 2020, U.S. patent application serial No. 16/861,814; U.S. application Ser. No. 16/335,893, entitled "Ablation SYSTEM WITH Force Control," filed on 3/22 in 2019; and U.S. patent application Ser. No. 17/777,104, entitled "Tissue TREATMENT SYSTEMS, devices, and Methods," filed 5/16 of 2022.

In some embodiments, the system 10 is configured to generate one or more maps of cardiac activity (e.g., via the mapping module 170) based on information collected in a non-contact manner (e.g., information recorded from one or more electrodes positioned within the heart chamber without contacting the heart wall, "non-contact data" herein). Alternatively or additionally, the system 10 may be configured to generate one or more maps of cardiac activity based on the contactingly collected information (e.g., information recorded from one or more electrodes positioned in contact with the heart wall, herein "contact data"). In some embodiments, the system 10 is configured to generate one or more "hybrid" maps of cardiac activity based on both contact data and non-contact data.

The positioning module 140 may provide signals to and/or record signals from one or more devices of the system 10. For example, the positioning module 140 may provide one or more signals to the patch 340, such as to establish an electric field within the patient. The positioning module 140 may record signals from one or more devices of the system 10 related to the established electric field to determine the position and/or orientation of the device within the field (to "position" the device) via an impedance-based method. Alternatively or additionally, such as when the positioning module 140 is constructed and arranged to establish a magnetic field within at least a portion of the patient (e.g., via one or more permanent magnets and/or electromagnets), the positioning module 140 may position one or more devices of the system 10 via a magnetic-based method, one or more functional elements of the system 10 including a magnetic coil or other element configured to detect the magnetic field.

In some embodiments, the positioning module 140 is configured to position one or more devices of the system 10 that have been placed in a static position with a desired minimum further intentional movement (e.g., movement of an operator), such as the diagnostic catheter 330 when placed within the CS. The positioning module 140 may be configured to track the location of this static device and to use this device as a "physical reference" (e.g., a locatable physical point within the patient that is unlikely to move relative to the heart, as described below). In some embodiments, the positioning module 140 may position one or more other devices of the system 10 (e.g., any or all other devices present within the patient, such as a location within or near the heart) based at least in part on the relative position of the physical reference with respect to the additional devices being positioned.

The system 10 may be configured to navigate (e.g., provide navigation information and/or automatically navigate) one or more catheters and/or other devices of the system 10. The system 10 may use impedance measurements recorded by the system 10 to perform navigation of the device (e.g., by the positioning module 140), as described herein. The system 10 may perform impedance measurements in many different ways. In some embodiments, the system 10 uses multiple pairs (e.g., at least three pairs) of patch sets (e.g., three pairs of patches 340) to deliver current through the body. Three pairs of patches may deliver unique frequencies (e.g., simultaneously). A sensor (e.g., an electrode) in the body may measure the potential at each of three unique positioning frequencies relative to reference measurements made elsewhere on the body, such as a patch on a low body surface on the torso.

The system 10 may be configured to perform impedance-based device navigation, such as by using an "adaptive reference," while reducing the effects of motion artifacts and/or other artifacts. In some embodiments, such as when positioned in a location where minimal further movement is expected (e.g., minimal manipulation by an operator is expected, such as when placed in a coronary sinus or other endocardial location where no further manipulation is needed and/or desired), the system 10 tracks a specified set of electrodes (e.g., one or more catheters and/or other devices from the system 10) that may (e.g., are likely to) be placed in a "static location. The system 10 may be configured to track those electrode positions and use them as "physical references". For example, the system 10 may track other devices (e.g., catheters to be maneuvered) by using the physical reference as an "anchor" (such as by subtracting the real-time location of the physical reference). The system 10 may be configured to compensate for physical displacement of any electrode used as a physical reference, where such displacement causes a displacement of the coordinate system. In some embodiments, the system 10 uses a dynamic adaptive learning model to train a measurement set from a body surface sensor (e.g., patch 340) for creating a virtual intracardiac measurement set that quantitatively reconstructs a signal equivalent to a physical device (herein "virtual reference"). For example, the system 10 may track other devices (e.g., catheters) by using the virtual reference as an anchor (such as by subtracting the real-time location of the virtual reference). These virtual references of the system 10 are not susceptible to the types of physical motion that may be encountered when using a device inserted into a patient (e.g., an endocardial catheter as described above), but they are susceptible to interference associated with the body surface sensor itself, a positioning measurement reference (e.g., a reference patch electrode), and/or to electrical interference introduced to a single device (e.g., a single catheter), multiple devices (e.g., multiple catheters), and/or the entire system (e.g., associated with a single device, multiple devices, and/or the entire system). In some embodiments, system 10 may be configured to store (e.g., in memory) a temporal history of all tracked locations (e.g., device locations, physical reference locations, and/or virtual reference locations), such as previous locations (e.g., for positioning and/or positioning compensation of one or more system 10 devices) for recall (e.g., and use) of any devices. Such stored information may be used to detect differences between systematic interference and interference specific to a single device (e.g., a single catheter). For example, electrical interference may affect a single device, multiple devices, and/or the overall system, and device motion will be unique to each individual device. Each of the above methods may be calculated simultaneously and/or sequentially.

In some embodiments, the system 10 is configured to perform an impedance-based device (e.g., catheter) navigation method using a "location consistency algorithm (position agreement algorithm)" (e.g., an algorithm of the algorithm 115) configured to address motion artifacts and/or other measurement artifacts. If the system 10 utilizes more than one device tracking method (e.g., two or more of those described above), the system 10 may implement a location consistency algorithm to cross-compare solutions for each of the multiple methods to determine one or more subsets of "consistent" solutions. Alternatively or additionally, the system 10 position consistency algorithm may determine one or more subsets of solutions indicative of aberrations. The location consistency algorithm may bias the system 10 toward prioritizing and/or de-prioritizing (e.g., ignoring) one or more of the methods based on the determined consistency and/or an indication of found aberrations. In some embodiments, prioritization and/or de-prioritization may be performed by assigning a weighting factor (e.g., a quantitative weighting factor) to each of these positioning methods. In some embodiments, the location consistency algorithm may specify when a de-prioritization method (e.g., a method that is not currently being used) is to be restored (e.g., reused). The location consistency algorithm may use a predefined set of logical rules to determine the root cause of the possible interference, e.g., to make appropriate adjustments to mitigate. If the time histories of the physical reference, the virtual reference, and the location all fall within a specified distance (e.g., such as a geofence), the system 10 may determine that all tracking methods are consistent. However, if the physical reference and the virtual reference agree, but the most recently stored locations in the time history do not agree, sudden electrical disturbances may affect the location of at least the designated reference device. Comparing the location of other devices (e.g., catheters) in the body with their time-historic locations can help to distinguish catheter-specific interference from system-wide interference. The overall quantitative impact of interference on location can be mitigated by correcting the positions of the tracked physical and virtual references to align with the most recent valid time history positions. For example, if the physical reference and the time history of the physical reference agree, but the virtual reference does not agree, the virtual reference may be anomalous and may be ignored by the system 10 (e.g., until it recovers to agree with the time history of the physical reference and the physical reference, after which it may be recovered).

In some embodiments, the system 10 is configured to perform an impedance-based device (e.g., catheter) navigation method using a "location determination algorithm (position determination algorithm)" (e.g., an algorithm of the algorithm 115), such as one configured to determine (e.g., and display) the location of one or more (e.g., all) of the system 10 devices being used (e.g., the location of one or more electrodes and/or other devices of one or more catheters positioned within and/or on the patient) using the results of the tracking method and the location consistency algorithm described above. In some embodiments, the location determination algorithm is configured to generate a "probability score" that includes a confidence level and/or other probability measure (e.g., quantitative output) of the determined location with respect to one or more devices. The probability score may be displayed to the user via the displayed alphanumeric value and/or by modifying (e.g., enhancing) the manner in which the displayed device is displayed (e.g., hash marks, color changes, brightness changes, and/or another portion of the body and/or related conduit and/or other visual differences of other devices). In some embodiments, the system 10 includes various thresholds (e.g., quantitative thresholds) used by the algorithm 115 to classify the predicted location of the device by comparing the probability score to one or more thresholds.

In some embodiments, the system 10 includes a "data quality routine (data quality routine)", which is configured to evaluate the quality of data from one or more connected measurement devices of the system 10. The data quality routine may be configured to determine a "state" of the system 10 device, such as to determine whether the system 10 device is electrically disconnected, has been removed from the body, is undeployed (e.g., not fully deployed), and/or has degraded sensors. In some embodiments, the data quality routine of system 10 (e.g., as implemented by algorithm 115) determines the state of the device by comparing measured and/or calculated parameters associated with the state (e.g., as determined by the sensors of system 10) to a threshold of system 10 associated with the state.

In some embodiments, the system 10 (e.g., algorithm 115) is configured to perform "dynamically bounded adaptive impedance tracking (DYNAMICALLY BOUNDED ADAPTIVE IMPEDANCE TRACKING)" with the monitored positioning reference, such as using one or more of the methods described above (e.g., using one or more electrodes to calculate a virtual reference). In some embodiments, the system 10 uses one or more sensors (e.g., electrodes) positioned in-vivo and designated as a monitored positioning reference. The physical device may be expected to remain in a static position for a period of time (e.g., a period of time above a minimum amount of time). The system 10 may monitor this device to determine whether the device has been physically displaced and/or whether electrical interference has affected the device (e.g., a separate device), one or more other devices (e.g., multiple devices), or the entire system. The system 10 may be configured to determine whether any additional devices other than the monitored positioning reference are positioned (e.g., currently located) within the body. When more than one device is available in the body, the system 10 may determine whether any of the devices are moving due to user manipulation and/or are independently affected by electrical interference. The system 10 may be configured to track the historical location of the monitored positioning references (e.g., as well as other devices), such as when no electrical interference is occurring. The system 10 may create a set of virtual measurements (e.g., virtual endocardial measurements) that quantitatively reconstruct signals equivalent to the monitored positioning references. The system 10 may use the entire set of body table measurements (e.g., the functional elements of the system 10 including a 12 lead ECG) to construct this virtual reference. The system 10 may (e.g., alternatively) construct a virtual reference using a set of surface pairs (less than the entire set) that are selected to most closely (quantitatively) constrain the position data of the monitored position reference device. The method can be more accurate and resilient to larger disturbances in catheter position. The system 10 may track the monitored positional reference device, virtual reference, and/or all other devices located (e.g., at least partially located) in a coordinate system used by the system 10. The system 10 may adaptively mitigate physical and electrical interference. For example, when the monitored positioning reference device is physically shifted, the virtual reference may be recalculated to match, and the time history data may receive a valid, updated position of the monitored positioning reference device. Alternatively or additionally, the virtual reference may be recalculated (e.g., by algorithm 115) when the electrical interference affects the tracked position of the virtual reference and/or the monitored positioning reference. By, and by, computationally realigning the tracked position of the monitored positioning reference with the last known good position in the time history, the position offset caused by the interference can be mitigated.

In some embodiments, the system 10 is configured to perform an impedance-based device (e.g., catheter) navigation method, wherein compensation is performed to account for patient respiration. As described herein, the system 10 may use electrodes of the device in a static position to create a "physical respiration reference (physical respiratory reference)". The system 10 may be configured to filter motion of the associated device to a respiratory-related frequency range, such as a frequency less than or equal to 1.0Hz, 0.5Hz, and/or 0.3 Hz. The system 10 may use the measured respiratory signal to compensate for respiratory motion, such as by subtracting the measured respiratory signal from the raw positioning signal on any electrode. Alternatively or additionally, the system 10 may use one or both of a "gating approach" and/or a "dynamic learning model" to interpret patient respiration. For example, the system 10 may use a gating approach that uses measurements from a body surface, from within the patient, or both to establish a consistent period of the respiratory cycle. The gating approach may simplify data collection by: only data acquired during the gating range of the respiratory cycle is used, such as for discrete acquisition similar to collecting contact map points (e.g., anatomical "shells" and/or EGMs); and/or performing samples on device locations from one gating period to the next and holding for time-continuous measurement (e.g., while employing interpolation between gating locations). The system 10 may use a dynamic learning model (e.g., an adaptive model) to train a set of body table measurements (e.g., via patches 340) to create a set of virtual in-vivo (e.g., endocardial) respiratory measurements that preserve respiratory components of motion (e.g., using filtering as a technique that preserves only respiratory components). The system 10 may use the measured respiratory signal to compensate for respiratory motion, such as by subtracting the measured respiratory signal from the raw positioning signal on any electrode. The system 10 may be configured to perform two or more of the above respiration compensation methods simultaneously and/or sequentially. In some embodiments, system 10 performs a plurality of respiratory compensation methods (e.g., as described herein), wherein each method is assigned a weighting factor used by system 10 to prioritize one method over another and/or otherwise apply a different level of importance and/or impact (herein "prioritization").

In some embodiments, the system 10 is configured to track, monitor, analyze, and/or compensate for patient respiration using a "breath tracking consistency algorithm" (e.g., the algorithm of the algorithm 115). If the system 10 employs more than one of the respiratory compensation methods described above, the system 10 may use a respiratory tracking consistency algorithm to cross-compare the solutions of each method to determine one or more subsets of the consistent solutions and/or to determine one or more subsets of the solutions indicative of aberrations. As described herein, such a breath tracking consistency algorithm may specify a method of temporary "ignore" and also determine when the method of "ignore" should be included again.

In some embodiments, the system 10 is configured to compensate for patient respiration using a "respiration compensation algorithm" (e.g., an algorithm of the algorithm 115), such as configured to determine (e.g., and display) a respiration compensated position (e.g., a position of one or more electrodes and/or other devices of one or more catheters positioned within and/or on the patient) of one or more (e.g., all) of the system 10 devices being used using the results of the respiration compensation method and the respiration tracking consistency algorithm described above. The system 10 may perform compensation using direct subtraction of the tracked respiratory motion from all associated device (e.g., electrode) locations. The system 10 may perform compensation by: respiratory motion is tracked differently at different locations of the anatomy, and by subtracting the local respiratory motion from the device (e.g., electrode) location displayed in the associated location. The different anatomical locations may be organized on a regular spatial grid (e.g., using voxels). In some embodiments, the respiratory compensation algorithm is configured to generate a "probability score" that includes a confidence level, and/or other probability measures (e.g., quantitative scores) regarding compensation applied to determine a determined location of one or more devices (e.g., a location determined using respiratory compensation). For example, the probability score may be based on the probability of encountering a disturbance, and/or it may be based on the extent to which the remaining respiratory motion remains uncompensated. The probability score may be displayed to the user via the displayed alphanumeric value and/or by modifying (e.g., enhancing) the displayed manner of the displayed device (e.g., hash marks, color changes, brightness changes, and/or another portion of the body and/or related conduit and/or other visual differences of other devices). In some implementations, the respiratory compensation algorithm (e.g., implemented by algorithm 115) of system 10 compares the probability score to a threshold of system 10 associated with the respiratory compensation algorithm (e.g., categorizes the compensation and/or modifies the manner in which the displayed device is shown).

In some embodiments, the system 10 is configured to compensate for patient breathing using an "impedance navigation accuracy optimization and scaling algorithm (IMPEDANCE NAVIGATION ACCURACY OPTIMIZATION AND SCALING algorithm)" (e.g., the algorithm of algorithm 115). Variations in the impedance of the body structure and components can affect the accuracy of the navigation performed by the system 10. These variations can be compensated computationally by tracking their effect on the measured distances at different locations in the body. The process performed by the system 10 of estimating the relationship between a set of impedance measurements and a corresponding set of known distances (e.g., physical spacing between electrodes) at a given location may be referred to as "scaling. By tracking these variations and computationally adjusting them by variable scaling at different locations of the body, impedance navigation becomes more accurate. First, there is limited impedance data available when device (e.g., catheter) measurements are first made in the body. The limited data may be used to initially estimate the scaling in the body. As the device is maneuvered throughout the body, the scaling information becomes more discretely measured, and the scaling information becomes more granular refinement. This system 10 process is referred to as "dynamic scaling". The impedance navigation accuracy optimization and scaling algorithm may include an "impedance scaling optimization algorithm" (e.g., the algorithm of algorithm 115) that processes a set of discrete scaling measurements to create a cohesive coordinate space, where the measured impedance data may map directly to a unique coordinate location. As more measurements are made, an impedance scaling optimization algorithm may be performed iteratively (e.g., over 1 second or 5 second intervals) to provide increasingly accurate navigation of the device. As navigation accuracy improves, previously collected location information may be updated retrospectively. Any data determined by the system 10 stored and/or calculated using the location information may be correspondingly recalculated (e.g., "reconstructed"). The data includes anatomical measurements, electrical measurements, and/or marker locations from the device location. Some information that is not directly derived from the device location may also be recalculated by the system 10. For example, the marker locations placed in the coordinate space that are not made up of measured device locations may be saved with corresponding "equivalent impedances" based on the scaling information available at the time (e.g., the markers are placed anatomically with a mouse). When recalculating the position data, the positions of the markers may be recalculated using the equivalent impedance and displayed (e.g., in a new position on the screen).

In some embodiments, the system 10 includes one or more components configured to provide magnetic data (e.g., data recorded and/or derived by the magnetic components), such as for a hybrid positioning system. The system 10 may utilize a magnetic navigation system to simultaneously track devices in the body using a second (e.g., different) measurement modality (e.g., different than those described above). The system 10 (e.g., algorithm 115) may be configured to perform hybrid mitigation of interference. The system 10 may take advantage of the redundancy of measurements to mitigate interference with the impedance tracking subsystem and/or interference with the magnetic tracking subsystem. A monitoring algorithm (e.g., one or more of algorithms 115) may be used for each modality to determine (e.g., repeatedly and/or relatively continuously determine) whether interference affecting either modality is present. When interference is detected by algorithm 115, the affected subsystem may temporarily disengage the effect on the display of the tracking device (e.g., catheter) until the system determines that the associated interference has been mitigated and/or is no longer occurring. The system 10 may be configured to perform hybrid navigation accuracy optimization and scaling. When used in conjunction with an impedance navigation subsystem, the accuracy of the magnetic subsystem is less affected by variations in body structure and composition. Thus, the magnetic navigation subsystem may provide (e.g., more immediately) accurate navigation data that may be used to construct impedance scaling data (e.g., faster) as the device is maneuvered throughout. The system 10 may utilize magnetic data as a computational backbone for impedance scaling optimization, instead of using "known distances" (e.g., physical spacing between electrodes) to indirectly map impedance changes having a direct measured correspondence between accurate locations in space and impedance measurements. The system 10 may navigate a device that includes only impedance sensors, only magnetic sensors, or both. The impedance mapping can be built directly into the displayed coordinate system using a device equipped with both types of sensors. In areas where both magnetic and impedance data have been measured directly, impedance scaling optimization may not be necessary.

The system 10 may be configured to "reconstruct" and store a portion of the patient's anatomy, such as to provide a reconstruction of one or more portions of the patient's heart. The stored anatomy may be used as input for: a data calculation algorithm (e.g., one or more algorithms of algorithm 115 configured to perform an inverse solution calculation); a process & display algorithm (e.g., one or more algorithms of algorithm 115 configured to calculate and/or otherwise determine contact point acceptance criteria, a nearest surface location and/or orientation, and/or an estimate of treatment delivery into tissue); and/or as a visual "canvas" on which many forms of data may be displayed. The anatomical information may include one or more anatomical components (such as different anatomical structures), such as to distinguish between different chambers of the heart, veins, arteries, and/or appendages. The stored anatomical information may include point location data, surface (e.g., shell) data, volume data, data from direct measurements of anatomical features (e.g., density, thickness, tissue type, tissue composition), data from calculations and/or estimations of anatomical features (e.g., normal direction from surface, angle of incidence to object, conduction features such as fiber orientation, scar heterogeneity, preferred path or connection to other structures, etc.). The system 10 may be configured to record, store, process, and/or display image data to collect anatomical information. The stored anatomical information may include data recorded by the system 10 and/or data provided to the system 10. In some embodiments, the image data is determined by positioning an object in the body. The position of the object may be determined using one or more points at a time by determining imaging points, which may be processed to form an imaging surface and/or an imaging volume.

In some embodiments, the system 10 includes one or more devices (e.g., catheters and/or external devices) that transmit and/or receive ultrasound signals such that the resulting ultrasound data can be converted into image data, such as an anatomical "shell" of the heart wall tissue of the patient and/or other tissue of the patient (e.g., non-blood tissue). An image point may be created from the ultrasound reflection data. In some embodiments, the ultrasound data includes reflection data from one or more transducers. The location of the point from which the ultrasound is reflected may be used to determine the location of an object within the heart ("imaging point"), such as the heart wall. The system 10 may be configured to determine the point from which ultrasound is reflected by determining the following three basic elements: origin (transducer position), direction of transmission & detection (vector) and range to the target.

The system 10 may be configured to update and/or recalculate (e.g., reconstruct) one or more imaging data points. The system 10 may track each imaging point (calculated ultrasound point) and its three basic elements. In some embodiments, the system 10 may determine updated information (e.g., location information with greater accuracy) that may be retrospectively used to modify the base elements used to calculate each imaging point. The system 10 may then recalculate the updated imaging points based on the updated base elements. For example, if one or more device navigation algorithms (e.g., algorithm 115) of system 10 provide updated device locations, the origin of each imaging point and/or the direction of transmission and detection may be updated accordingly. The system 10 may then recalculate the anatomical surface based on the updated imaging points. Subsequent calculations made by system 10 (as described herein) may be updated based on the new anatomical surface and/or based on imaging point information.

The system 10 may be configured to create an image point from various forms of image data. The system 10 may utilize various image data from an ultrasound imaging device (e.g., a B-mode ultrasound imaging device), a CT scanner, an X-ray imager, and/or an MRI imaging device to determine and integrate imaging points. Such image data includes data that can distinguish objects based on intensity, color, brightness, and/or other quantitative values in a 2D plane or 3D volume. By determining the orientation and alignment of the image data within the coordinate space tracked by the system 10, the image data may be converted to imaging points. The system 10 may select one or more value ranges in the image data representing the object of interest, determine corresponding imaging points in the coordinate space of the system, and integrate new imaging points. In some embodiments, the system 10 utilizes data from two or more imaging devices described herein. In these embodiments, weighting factors may be applied to apply different levels of importance and/or impact to data obtained from one imaging device versus another imaging device.

The system 10 may be configured to organize the imaging points into data structures. The system 10 may track positional information in a coordinate space (e.g., a three-dimensional straight line and/or a cartesian coordinate system). Within the coordinate space, the system 10 may organize the imaging point data into a data structure, such as a 3D voxel space, where, for example, each voxel specifies a unique range of the volume space in the coordinate system, and each voxel may contain: no imaging point, a single imaging point or a plurality of imaging points. The voxels may be uniform in each direction (e.g. when 0.5mm x 0.5mm of a portion of the volume and/or 1mm x 1mm of the volume). The size of the voxels may be uniform and/or the size of the voxels may vary. Voxels may be adaptively merged and/or partitioned to efficiently organize and process the contained data. The merging and/or dividing may be based on data contained within the voxels, such as the location of the data within each voxel and/or the number or density of the data within each voxel. The data structures created by system 10 may include octree data structures and/or other efficient data structures, for example, to implement efficient data searching and/or data processing algorithms (e.g., one or more of algorithms 115). Additionally or alternatively, the data structure may take the form of a structured computational grid (e.g., a rectilinear grid) and/or an unstructured computational grid (e.g., a tetrahedral grid), for example, when the manifold neighborhood is explicit in the grid definition used by system 10 and/or must be determined by system 10 after the grid is created.

The system 10 may be configured to determine attributes and/or quantitative metrics that may be used to analyze the imaging points. The data structure created by system 10 may track the attributes and/or quantitative metrics of each voxel, such as to achieve processing efficiency. In some embodiments, each voxel has attributes related to (e.g., track) the following: filled or empty; the number of imaging points within a voxel; and/or the density of points within a voxel. Each voxel may keep track of the geometric center of that voxel. Each voxel may keep track of the geometric centroid of the points contained within the voxel. As imaging data is added, removed, and/or changed, the system 10 may update the attributes and/or quantitative metrics accordingly. The system 10 may track multiple parallel data structures such that points may be assigned to different data structures to track independent sets of points. For example, the system 10 may allocate points designated as from the left chamber of the heart to the first data structure and points designated as from the right chamber of the heart to the second data structure. Alternatively, system 10 may organize all points using a single data structure and track such designations using attributes of voxels and/or individual points.

The system 10 may be configured to identify an artificial imaging point. Imaging points may occur artificially inside the anatomical structure. In some embodiments, imaging points located within a threshold distance of any previous location of the endocardial device may be excluded from further calculations. In some embodiments, the system 10 includes multiple threshold distances (e.g., multiple thresholds related to different device types or other differences) for excluding one or more devices.

The system 10 may be configured to generate a surface (e.g., a shell or a portion of a shell), such as a surface representing a heart wall of a patient and/or a surface of other tissue. The system 10 may create a surface from a set of image points. In some embodiments, a poisson surface is used to calculate the surface from a set of points. In some embodiments, system 10 organizes a set of image points using a data structure of voxels. The "filled" voxels are used to generate a surface. Organizing the imaging points into voxels may improve the efficiency of surface computation, as the data density of the original set of imaging points may far exceed the necessary resolution required to create the surface. In some embodiments, system 10 (e.g., algorithm 115 of system 10) uses the location of the filled voxels (e.g., the center point of each voxel) as a surface guide point. Each surface guide point is associated with a vector whose direction is estimated to be in the direction of the normal of the desired computing surface. In some embodiments, system 10 approximates the normal vector using a normalized radial projection from a center point in the coordinate system to the center of each voxel. The center point is preferably within and near the center of the intended closure surface. In anatomical applications, it is appropriate to select a point in the center of the anatomy. The central point may be reassigned by the system 10 as desired and/or needed. In some embodiments, the surface may be calculated by the system 10 by first establishing a set of gaussian basis functions, the locations of which are placed approximately at each surface guide point. From the gaussian basis function, along with the normal vector, a solution to the poisson equation defining the surface is obtained (below:

The solution phi of the control equation is obtained discretely by the inverse method, which yields the "strength" of each gaussian basis function. In short, one implementation of the control equation is written for each surface guide point, and the divergence of the normal at that point is expressed as a linear sum of contributions from nearby surface guide points. To limit the area of influence around each surface guide point, the basis functions are defined with a compact support. This produces a sparse influence matrix that facilitates fast solutions. The calculated surface is selected as a closed isosurface having a value of phi=0.5.

The surface may be represented as a triangular mesh. The resulting surface will be a closed surface following the guide point as closely as possible. In areas without guide points or with limited guide points, the accuracy of the calculated surface may be poor and may be removed from the triangular mesh. In some embodiments, the system 10 does not calculate the surface until a sufficient number (e.g., 48, 64, or 100) of guidance points distributed in the coordinate system are collected. In some embodiments, a "sufficiency algorithm" of system 10 (e.g., one or more algorithms of algorithm 115) may determine when the number and distribution of guidance points is sufficient to initiate computation of the surface (e.g., when a threshold is exceeded). In some embodiments, the calculated surface is biased toward the interior or exterior of a set of surface guide points. A "scaling algorithm" of system 10 (e.g., one or more of algorithms 115) may be applied that estimates the signed offsets of each surface guide point to the calculated surface and relocates each vertex of the calculated surface such that the average offset disappears. In some embodiments, the surface calculation process of system 10 generates isolated segments of the surface, and algorithm 115, including an isolated segment removal algorithm, eliminates these structures.

In some embodiments, the system 10 may create a visualization of the surface from the imaging points, for example as described herein with reference to fig. 2. The system 10 may be configured to iteratively calculate and display the imaging surface as the image point data is continuously collected. The system 10 may display raw imaging points, surface guidance points, surface grids, or any combination of these. In some embodiments, the surface mesh may be displayed as a single color. In some embodiments, the surface mesh may be visualized differently (e.g., via changes in color, transparency, intensity, etc.) based on attributes and/or quantitative data of nearby surface guide points. For example, the surface may be colored and/or otherwise graphically distinguished based on the density of imaging points within nearby voxels. This visual distinction may allow the user to view areas where sufficient data has been collected and/or areas where data collection has been restricted so that the user may adjust the data collection accordingly. The opacity of a portion (e.g., triangle) of the anatomical model mesh may also be based on the density of imaging points within nearby voxels. During live collection of data and/or iterative computation of surfaces, the visualization surface may have an initial appearance to facilitate user interpretation of the data collection process and/or to optimize computational efficiency and processing speed. The visualization surface provided by the system 10 may have an optimized appearance when not actively collecting. In some embodiments, the recalculation and updating of the display by system 10 is reinitiated at regular intervals (such as intervals of no more than 0.5 seconds, 1 second, and/or 2 seconds). In some embodiments, the recalculation and updating of the display is reinitialized asynchronously to the collection of additional image points. In some embodiments, an "update reinitiation algorithm" of system 10 (e.g., one or more algorithms of algorithm 115) is used to estimate and/or calculate the degree of change introduced by the newly acquired data, and may determine when acquisition of the new data exceeds a threshold for initiating a surface re-calculation and a corresponding update to the display. In some embodiments, the system 10 uses a combination of reinitiation methods (e.g., where each reinitiation method is assigned a weighting factor, such as applying a distinguishing importance to each method). In some embodiments, the system 10 uses shorter intervals of any one of the combinations of reinitiation methods.

The system 10 may be configured to create an anatomical reconstruction including a volumetric reconstruction. In some applications, anatomy may be used as a volumetric structure rather than a surface structure. If a surface structure has been created, the system 10 may calculate a volume structure by filling the interior volume of the surface with points and generating a tetrahedral volume mesh to represent the volume object. In some embodiments, a set of interior points may be determined (e.g., strictly determined) by testing each point on the regular 3D mesh to determine whether it falls inside or outside the surface. In some embodiments, an "interior point placement algorithm (internal point placement algorithm)" (e.g., one or more algorithms of algorithm 115) of system 10 may effectively search for points inside the surface and may determine a set of interior points at a density sufficient to create a tetrahedral volumetric mesh. In some embodiments, the interior point placement algorithm may construct large tetrahedral segments with portions of the surface (e.g., of uniform or other similar size) using a first seed point inside the closed boundary of the surface, and iteratively subdivide the tetrahedrons into smaller and smaller sizes until a threshold (e.g., a desired number of interior points, interior point density, average tetrahedral edge length, or volume) is met. Alternatively, an interior point placement algorithm may use compartment subdivision to perform a search to test points on a regular 3D grid, where the data space is divided into large compartments, and each boundary point of a compartment is tested to be classified as being inside or outside the surface. Subdividing the compartment with one or more boundary points inside, and repeating the process for each subdivision of the previous compartment. In some embodiments, previously tested boundary points are no longer tested. The subdivision process may continue until a threshold (e.g., a desired number of interior points, interior point density, and/or average tetrahedral edge length or volume) is met. In some embodiments, the system 10 includes a plurality of these thresholds that may be used to classify (e.g., quantify or define) the quality (e.g., resolution, accuracy, etc.) of the output of the process.

In some embodiments, a plurality of tetrahedral meshes (e.g., low resolution meshes and/or high resolution meshes) are pre-computed, and when the system 10 completes the anatomical shell, the bounding box of the mesh may be estimated (e.g., by the algorithm 115 of the system 10), and the pre-computed mesh may be transformed into an anatomical bounding box. In some embodiments, after transformation of the pre-computed mesh, each node of the mesh may be evaluated (e.g., by algorithm 115 of system 10) and classified as falling inside or outside the anatomical mesh.

In some embodiments, the pre-computed mesh includes varying resolutions, with dense tetrahedrons near the center of the bounding box and sparse tetrahedrons towards the outside of the bounding box. This parameterization of resolution may be determined based on the population-level average shape and metrics calculated therefrom, such as distance functions (e.g., determined by algorithm 115 of system 10).

The system 10 may be configured to create an anatomical reconstruction by creating anatomical data from tracked catheter and/or other device locations. The system 10 may create anatomical data by tracking the location of the navigated device and determining the outer boundary of the volume "tracked" by the different locations of the device. The tracked location of the device as a whole may be used by the system 10 (e.g., by the algorithm 115) for forming a representation of the anatomical volume.

The system 10 may be configured to create anatomical data by integrating anatomical data collected from multiple methods (e.g., two or more of the methods described herein or other methods). In some embodiments, the system 10 integrates (e.g., by the system 10) various (e.g., all or a portion) anatomical data collected using multiple methods. In some embodiments, imaging surface data is converted to volumetric data by system 10. The device tracking data may also be represented as volumetric data (e.g., volumetric data including structured and/or unstructured elements). Integration of the two data sets may be performed by merging the volumetric data in the same coordinate space and processing the union of the two data sets as an cohesive (e.g., by algorithm 115).

The system 10 may be configured to edit (e.g., allow editing and/or automatic editing by an operator) an anatomical structure. The system 10 may be configured to enable a user to add, remove, and/or edit anatomical structures. In some embodiments, surface or volume data may be deleted and/or modified (e.g., "shaved" ("shaked")). The surface or volume data may be reassigned to a different structure. The surface data may be modified by cutting holes in the surface mesh. In some embodiments, user-selectable geometries (e.g., predefined geometries stored in a library of system 10) are used to facilitate creation of the anatomy. For example, one, two, three, or more of each of a sphere, oval, teardrop, and/or other shape may be provided by the system 10 and used to facilitate initial creation of anatomy.

The system 10 (e.g., via the algorithm 115) may be configured to perform cardiac signal (e.g., EGM) optimization. For example, the system 10 may be configured to perform far field compensation. In some applications, such as by solving for atrial activation by inversion, the far field effects of activation in chambers other than the atrium being diagnosed and/or treated (such as the ventricle or the opposite atrium) may be destructive. Before computing the inverse solution map, it may be advantageous to isolate relevant components of the EGM from the chamber of interest. As an example, separation and exclusion of ventricular components (QRS) from atrial components may greatly benefit the quality of data generated by system 10 (e.g., mapping data generated by system 10). The system 10 may perform various signal processing functions, such as processing ECG to reverse model QRST or PVC (e.g., in a self-optimizing arrangement). In some embodiments, the body surface ECG may be used by system 10 to create a rough model of ventricular depolarization and repolarization. In some embodiments, system 10 (e.g., via algorithm 115) may create the model using an inverse solution that dynamically detects ventricular activity (e.g., QRS) on the body surface leads, may calculate an inverse solution estimate of ventricular activity (as would be measured at the location of the endocardial catheter) for each ventricular beat, and may subtract the calculated ventricular activity from each respective beat of the endocardial signal. In addition to and/or in lieu of ECG-based parameterization, ventricular templates may be identified automatically (e.g., by algorithm 115) based on the intracardiac signals, and/or templates may be determined by the user. Where the ventricular component is inversely computed by the system 10 independent of the atrial component, the forward matrix may be estimated by the system 10 (e.g., by the algorithm 115) using subject-specific anatomies, rule-based averages, and/or geometric primitives. This combination of estimation and subtraction aims to preserve as many atrial components as possible of the intra-cardiac signal while removing as many ventricular components as possible. The "atrium only" EGM may then be processed (e.g., by algorithm 115) using an inverse solution for the full chamber non-contact mapping, resulting in a map with minimal ventricular artifact or false annotations due to the presence of ventricular artifact. The ventricular estimate performed by the system 10 may also be self-optimized by evaluating the residuals of the removed ventricular signals and then updating the estimate accordingly to minimize the residuals. Additionally, the estimates may be refined by the system 10 (e.g., via the algorithm 115) by first applying groupings, clusters, and/or classifications to ensure that the collected heartbeats (also referred to herein as "beats" or "beats") used for the estimates are similar to and well-matched to the beats to which the estimates were applied. Heartbeats with different features will be classified as falling into different groups and will therefore have different estimates to be applied.

The system 10 may be configured to perform measurements (e.g., direct measurements) in an arrangement that reduces artifacts introduced by far field activity. The system 10 may take measurements from a first electrode (e.g., of a system 10 device inserted into a heart chamber) using a second electrode (e.g., of the same or a different system 10 device) as a near reference. The first electrode may be configured to touch tissue for measurement (e.g., configured to make contact measurements). The second electrode may be configured to not contact tissue when the measurement is made (e.g., configured to make a non-contact measurement). By subtracting the signal of the second electrode from the signal of the first electrode, the far field component measured by both electrodes will be suppressed, but only the local signal measured by the contacted electrode will be preserved (e.g., and not significantly suppressed).

The system 10 may include one or more catheters or other devices that include stacked electrodes (e.g., stacked micromachined electrodes). For example, in some embodiments, the first electrode and the second electrode are configured in a stacked orientation, wherein the stacked electrodes are separated by a small separation distance. In such a configuration, the electrodes may be printed on a flexible circuit and may be deployed on the steerable catheter, with only the first electrode contacting the tissue in most deployment configurations.

The system 10 may be configured to perform measurements from far-field contributors (such as in the ventricles) when treating and/or diagnosing the atrium. In some embodiments, the ventricular estimate creation and subtraction process of system 10 may utilize one or more direct measurements from the opposing chambers. Direct measurements may be used in place of the inverse solved templates, or these measurements may be used to cross-check the inverse solved templates.

The system 10 may include an algorithm 115 that includes one or more machine learning, neural networks, and/or other artificial intelligence algorithms (herein "AI algorithms"). The system 10 may include a machine learning algorithm or other AI algorithm (e.g., one or more of the algorithms 115) for determining EGM benchmarks, categories, and other parameters. The system 10 may be configured to store, share, and/or learn user intervention (e.g., user intervention of an automated process). The system 10 may also be configured to adaptively self-optimize an automated process in response to learning. The system 10 may maintain a self-updating dataset that covers any user input of automatic detection or measurement by the system. The user's selections and corresponding raw data may be considered tagged data and stored. Some examples of such markers include, but are not limited to: measurement channels to be excluded; a change in the threshold to be made; changes in the time annotation to be made; jitter to be included and/or excluded; points to be included and/or excluded; etc. The stored database of tagged data may be used to locally "learn" (e.g., identify or evaluate) the underlying features in the data that produce more accurate automated results. The system 10 may also be configured to automatically share the database and/or transfer the database to a master repository, which may be shared with one or more other system 10 units (e.g., at another location, such as when shared via a secure wired or wireless connection arrangement).

As described herein, the system 10 may be configured to perform various forms of cardiac electrical mapping. The system 10 may process cardiac signals to detect and analyze cardiac events, such as heart beats and/or cycles. The system 10 may be configured to perform "heart rhythm tracking", for example, when a heart beat is classified. The system 10 (e.g., via the algorithm 115) may automatically distinguish and/or classify each individual heart beat based on real-time or near real-time (herein "real-time") of each individual heart beat having similar characteristics and/or in a post-processing step. The set may be used to map patterns that sequentially aggregate data using multiple heartbeats. Some mapping modes may utilize beats that do not fit into pre-existing groups, and the functionality of the beat groupings helps automate the identification of these unique beats (e.g., via a "trigger mapping mode" of the system 10 as described herein). The system 10 may perform heart beat detection, annotating the reference beat at the initial point in time (time T ₀). In some mapping modes, the system 10 time aligns the heart beat, such as on a time reference. The system 10 may detect each beat by looking for signal features on one or more cardiac signals recorded by the system 10. The cardiac signal analyzed by the system 10 for beat detection may be unipolar, bipolar, full-polar, laplace operator, and/or any mathematical combination of one or more signals. The analyzed signals may also be derivatives, envelopes, energy functions, histograms, and/or other results of mathematical operations performed on the measured cardiac signals. The system 10 may obtain these signals from one or more devices of the system 10. The signal (e.g., processed signal) may be a mathematical composite of a plurality of signals. For example, when system 10 analyzes both monopolar and bipolar (e.g., simultaneously or sequentially), system 10 may analyze more than one of the signals described above (e.g., including processed signals) in combination. The signal characteristics identified by the system 10 may include one or more characteristics selected from the group consisting of: crossing a threshold (positive, negative or absolute); Local maximum or minimum peak (positive, negative or absolute); local maximum slope (positive or uphill and/or negative or downhill). The system 10 may use the time of the detected feature for T ₀ alignment. The system 10 may optionally apply a time offset from the detected feature. The system 10 may use more than one of the above-described signal features in combination, such as features in which a cardiac event both crosses a threshold and has a sufficiently large local maximum negative slope. If the system 10 uses more than one signal type (e.g., simultaneously), the signal characteristics of each signal type may be the same or different for each signal type. In some embodiments, the system 10 may require that the positive peak of the filtered bipolar signal be of sufficient magnitude to establish independent jitter and establish a T ₀ reference time. Alternatively or additionally, the system 10 may use the mean, median, and/or maximum of the envelope or energy function of one or more bipolar signals to establish the T ₀ reference time.

In some embodiments, the conduction velocity is estimated as a global optimization function, whereby the global velocity is regularized and adjusted by the system 10 (e.g., by algorithm 115) based on physiological ranges and loss functions defined based on deviations from average physiological values and/or layered based on a series of averages compiled from pathological populations.

In some embodiments, the solutions are regularized by the system 10 (e.g., by the algorithm 115), such as by spatially filtering on the graph and/or grid using techniques such as arithmetic mean, arithmetic median, and/or projection to graph-based basis functions. In the case of conduction velocity, filtering may be applied to the coordinates of the velocity vector in its canonical form and/or in its quaternion form. The filtering may also be applied via non-linear techniques, such as via a neural network and/or local non-linear filters, such as median values.

The system 10 may be configured to perform a heart beat classification in which reference channel exclusion is performed. The system 10 may perform reference channel exclusion selected from the group consisting of: excluding (e.g., automatically excluding) one or more channels; disabling (e.g., automatically disabling) one or more channels used as T ₀ reference times; disabling (e.g., automatically disabling) one or more channels for detecting and/or classifying heart beats; and combinations of these. These different channel exclusions may be made due to electrical disconnection and/or lack of detectable features on those channels, for example due to poor performance when used as a T ₀ reference time and/or classification jitter. If a sufficient amount (e.g., a significant amount) of a particular frequency (e.g., 60 Hz) is present on the channel, the system 10 may automatically disconnect one or more channels, as the condition may generally be an indication that the sensor (e.g., electrode) is electrically disconnected and/or "noisy" (e.g., experiences electrical interference or other signal noise). If the signal amplitude is below a threshold (e.g., if the sensor is in a suboptimal position to measure the cardiac signal) and/or if the signal amplitude is above a threshold (e.g., when pacing results in a large pacing artifact that is much larger than the underlying cardiac signal), the system 10 may be configured to automatically exclude the use of channels. The system 10 may be configured to exclude a sensor (e.g., an electrode) if the sensor's location is ambiguous, inconsistent, and/or abnormal, as this may indicate a low quality electrical connection and/or may degrade the impact of an external system of performance. The system 10 may be configured to automatically exclude channels based on: analyzing the statistical data; data mining; and/or machine learning or predictive analysis to analyze measured signals for a previously accepted and/or excluded signal (e.g., resulting in a determination of a set of channels to be excluded) against a model and/or library of that channel. In some embodiments, the models and/or libraries used by the machine learning and/or predictive analysis algorithm (e.g., the AI algorithm of algorithm 115) of system 10 include marking signals that are accepted and/or excluded based on operator decisions.

The system 10 may be configured to perform "best reference channel selection". The system 10 may automatically select and/or suggest the best signal or set of signals to use for T ₀ reference and/or for cardiac classification. The system 10 may use ranges and/or other thresholds of amplitude and/or timing interval (e.g., period length) stability and/or consistency. The system 10 may utilize a confidence metric for evaluating a plurality of signal characteristics, including but not limited to: cycle length stability; amplitude of vibration; signal morphology (e.g., degree of fractionation or presence of certain signal components, like a certain number of waveform deflections or specific shapes, like an "RS" morphology); and/or confidence scores for the signals (e.g., when only signals with sufficient confidence scores are used).

The system 10 may be configured to compare various characteristics of the recorded signals. The system 10 may be configured to use one or more features to distinguish or classify heart beats, such as distinguishing based on a feature selected from the group consisting of: stability and/or consistency of timing intervals (e.g., cycle length); signal morphology (e.g., unipolar signal morphology); envelope and/or energy function (e.g., example, bipolar signal envelope); a timing and/or pattern across multiple signals (e.g., a pattern across an annotated time reference of a set of unipolar and/or bipolar signals from sensors at different locations in the heart); and combinations of these. The system 10 may be configured to use wavelet decomposition to isolate unique signal components from the morphology of the signal, such as to quantitatively compare those components to those of other signals. System 10 may perform cross-correlation between signals and/or wavelet components to quantify a degree of matching between two or more signals. The system 10 may be configured to perform statistical analysis on the period lengths (intervals between beats) to characterize the beats. Similar cycle lengths may indicate the same heart rhythm or cardiac circuit, and differences in cycle lengths may indicate changes in the heart rhythm or cardiac circuit. The system 10 may be configured to use the envelope and/or energy function of one or more signals. The signal may be unipolar, bipolar, full-polar, laplace operator, and/or any other mathematical combination of one or more signals. When multiple signals are used by the system 10, the envelope and/or energy function may be evaluated for each individual signal, and/or may be evaluated for aggregation or recombination of a combined set of signals. For example, the envelope and/or energy function of each signal in a set of multiple bipolar signals from one or more locations in the heart may be used as a template against which the same signals from other heartbeats may be compared. The system 10 may be configured to "match" a comparison signal set that is compatible with the template (e.g., like a key entering a lock). Alternatively or additionally, the system 10 may stack, aggregate, and/or otherwise create a composite of the plurality of signals, and then determine an envelope and/or energy function of the composite to create the template. Then, for each comparison beat, a similar composite and envelope and/or energy function may be constructed and compared to the envelope/energy function of the template. In some embodiments, cross-correlation of the template envelope and/or energy function with the envelope and/or energy function from comparing jitter may be used by the system 10 to quantitatively compare jitter. The system 10 may be configured to match jitter with sufficient correlation scores. In some embodiments, a combination of multiple characteristics may be used by the system 10 to distinguish jitter.

The system 10 may be configured to group heart beats. For example, the system 10 may be configured to automatically create unique groups and categorize individual beats into them. The system 10 may perform various forms of clustering and/or classification methods including, but not limited to: linear or quadratic discriminant analysis, correlation analysis, principal component analysis, K-means (e.g., connectivity-based or centroid-based) clustering, support vector machines, kernel methods, neural networks, spectral clustering, hierarchical clustering, distribution-based clustering, density-based clustering, and/or mesh-based clustering. These techniques may be configured to identify similar beats and/or to classify beats into groups. In some embodiments, the system 10 processes the wavelet decomposed signal sets using k-means clustering to determine a set of clustered beat groups. The system 10 may use a combination of multiple signal features and/or weighted scores to determine an overall group classification. For example, the system 10 may group heartbeats based on the combined weighted scores using both the cycle length and k-means clustering of the wavelet decomposed signals to generate (e.g., identify) multiple groups. By utilizing a weighting method, the system 10 may be configured to allow the user to select the relative weights assigned to each individual score. For example, a user may prefer to increase the relative weights provided for morphology scores while reducing the importance of cycle length changes in clusters/classifications (or vice versa). The system 10 may be configured to perform live (e.g., real-time) and/or post-processing calculations. For example, as each new heartbeat is encountered, the system 10 may process the beat detection and classification as a post-processing step on the recorded data, or iteratively "on the fly" (e.g., live in real-time).

The system 10 may be configured in a "triggered mapping mode," such as a mode configured to perform a "unique beat detection and rapid mapping routine" in which the routine detects a unique heart beat and/or performs rapid mapping (e.g., based on PAC, PVC, and/or other triggers). The system 10 may be configured to utilize (e.g., included in one or more analyses) heartbeats that do not fit in a pre-existing group, such as identifying unique beats and/or beats with a small number of repetitions. The system 10 may establish one or more groups of beats and then identify beats that do not match the established groups. The system 10 may be configured to visually specify (e.g., via graphical differentiation as described herein) and/or otherwise identify these mismatched beats (e.g., via a display of the system 10). Once these unique beats are detected, the system 10 may employ several mapping methods to identify signal derived values (e.g., derived cardiac data) from these heartbeats, including, but not limited to: activation time, peak-to-peak amplitude, beat or other inter-beat metric wave timing (e.g., cycle length, S-T segments), and/or other data derived from the cardiac signal. Cardiac data may be calculated directly from measurement signals on one or more devices (e.g., catheters), which may be displayed as a visualization (e.g., 3D visualization) on the anatomical shell. Deriving cardiac data from cardiac signals (e.g., from a single or multiple catheters) to generate visualizations may be performed by the system 10 in a variety of ways including, but not limited to: interpolation and/or direct assignment of values based on proximity of the electrodes to the shell; solving the inverse; and combinations of these. The system 10 may be configured to distinguish (e.g., color or otherwise graphically distinguish) on a display: a sensor; objects (shell, marker, volume of space) near the sensor; and/or combinations of these. The displayed differentiation may be based on activation time and/or amplitude data.

The system 10 may be configured to perform a "heart rhythm classification routine". For example, the system 10 may utilize detected and classified heartbeats to make automated "suggestions" of heart rhythm types (e.g., as presented to the operator on a display of the system 10). The system 10 may base the suggestion on a fixed metric, such as a range of cycle lengths or R-R intervals between QRS (or any other inter-beat metric). The system 10 may base the suggestion on a statistical analysis; data mining; machine learning or other AI algorithms and/or predictive analysis to analyze signals with models and/or libraries for previously classified heart rhythms; and combinations of these. For example, cardiac signals, such as those used to detect and classify individual beats (e.g., from a set of reference EGMs placed in the coronary sinus) may be processed by the system 10 (e.g., via algorithm 115) using wavelet transforms that decompose each waveform into different frequency bands while still preserving temporal information to produce a wavelet scalar map image. Such processing of cardiac signals may also be performed by system 10 in an unsupervised workflow. Image-based features in comparing the beat scale patterns may be evaluated by the system 10 using a convolutional neural network or other AI algorithm trained on a library of labeled scale pattern images, where the heart rhythm type is known. Some of the sortable heart rhythms of system 10 include, but are not limited to: atrial flutter, atrial tachycardia, atrial fibrillation (atrial fibrillation, AF), sinus rhythm, pacing rhythm, or ventricular tachycardia. The heart rhythm may also be classified using any data duration (not just single, detected, and classified beats) and/or any set of signals acquired by the system 10 (e.g., as obtained by a reference catheter, mapping catheter, body surface electrode, etc.).

The system 10 may be configured to perform mapping (e.g., cardiac mapping) of the inventive concepts using various forms of data collection and data analysis. The system 10 may be configured to display electrical events and/or activities (e.g., heart) in the form of electroanatomical maps (electroanatomic map, EAM) based on data generated from electrical signals (e.g., electrograms or EGMs), where the data is visualized on a display of the anatomical structure. The EAM of system 10 may show activity data (ACTIVITY DATA, AD) at a single location, region, chamber, entire heart, and/or any other body volume (e.g., tissue volume). AD may include activation time, amplitude, conduction velocity, fractionation, complexity index, pattern detection, sequence detection, causality index, recurrence index, dispersion index, refractory (e.g., angle between subsequent activations) metrics, and/or any result from computation of signals (e.g., cardiac signals and/or imaging signals). Similarly, an integration metric (such as a metric that uses an activation sequence in conjunction with a refraction metric) may be applied by the system 10 (e.g., by the algorithm 115) to determine the spatiotemporal initiator of the refractory event. The EAM produced by the system 10 may also include an AD based on signals from an inner surface (endocardial surface), from an outer surface (epicardial surface), and/or from tissue between the two surfaces (central or trans-myocardial tissue). The AD may be based on signals acquired by electrodes in contact with tissue (contact data) and/or the AD data may be derived from calculated signals at tissue (non-contact data) derived from electrode measurements not in contact with tissue. The AD may be based on signals that may be unipolar, bipolar, full-polar, laplace, and/or any other mathematical combination of one or more signals, and/or other results from derivative, envelope, energy function, or mathematical signal operations. The AD may be based on signals that may be acquired from one, two, or more devices of system 10 (e.g., one, two, or more catheters, patches, and/or other components of system 10). Both contact mapping and non-contact mapping based on an inverse solution are described herein. The AD may be based on signals of contact measurements and/or signals of non-contact calculations. The EAM may include one or more types of combinations, aggregations, integrations, and/or fusions of map data, such as an AD that is both monopolar and bipolar, and/or an AD that is both contact and non-contact signals. The system 10 may also include a "data fusion algorithm" (e.g., one or more of the algorithms 115) that may be configured to coherently combine data of different forms and/or different signal sources. For example, the data fusion algorithm may calculate the activation time based on: both bipolar signals and corresponding unipolar signals; And/or contact signals and non-contact signals. The data fusion algorithm may determine whether either, both, or none are viable and, if both are viable, determine the activation time used in the EAM. If the activation times are not consistent, the data fusion algorithm may use a set of rules relating to different signal characteristics (such as amplitude, slope, width, morphology, energy, etc.) to select the best activation time to use and/or calculate an intermediate value to use. In these embodiments, the system 10 includes one or more thresholds for evaluating signal feasibility or performing another data evaluation. Alternatively or additionally, the data fusion algorithm may include a learning model configured to determine an optimal activation time to use based on historical data (e.g., tagged data) and/or through the use of an unsupervised workflow (e.g., a workflow using untagged, non-benchmarked data). In some embodiments, the data fusion algorithm may combine the contact bipolar amplitude with the non-contact unipolar amplitude. In other embodiments, the data fusion algorithm may combine activation times from a contact signal (e.g., a contact bipolar signal) and from a non-contact signal (e.g., a non-contact unipolar signal). The data fusion algorithm may establish a relationship between data of two different types and/or different signal sources and provide the data for display in cohesive units of measure (e.g., normalized percentage or equivalent units of measure).

The system 10 (e.g., via a data fusion algorithm) may be configured to perform mapping directly from measurements, such as performing a "live scan" (e.g., a real-time scan). The system 10 may be configured to directly calculate electrical activity information from the measured signals and display them (e.g., quickly display them) on an anatomical structure (e.g., a shell or other image of the anatomical structure provided on a display by the system 10 as described herein). In some embodiments, the data is distributed or projected onto an anatomical surface on a display of the system 10. When collected in close proximity (e.g., a distance less than 5 mm), electrical activity information may be shown on the anatomy. The system 10 may calculate (e.g., directly calculate) electrical activity information from the electrogram at a distance (e.g., a threshold distance of at least 5 mm) and project the low-density map to the anatomy as sparse samples of the electrical activity information. In some embodiments, the system 10 includes an "interpolation algorithm" (e.g., one or more of the algorithms 115) configured to calculate the displayed data in areas where measurements are not available. The data fusion algorithm may coherently integrate the AD collected in close proximity with the AD measured at a distance (e.g., a distance above a threshold). In some embodiments, for example, using the steepest negative slope, a unipolar signal from a device (e.g., catheter) that is not in contact with tissue (e.g., electrode is not in contact with tissue) is measured and the local activation time is directly annotated. These activation times are projected onto the nearest surface or along a vector (such as a vector oriented perpendicular to the electrodes). When the device (e.g., its set of associated electrodes) is located near the center of the chamber, the activation time may be projected around the entire chamber. When the device (e.g., its set of associated electrodes) is closer to the wall of the chamber, the activation time may be projected to the close wall. Activation times may be calculated by system 10 for each detected heart beat, and/or these times may be calculated for only a unique detected beat (e.g., as described herein with reference to the trigger mapping mode and/or the unique beat detection and rapid mapping routine). Once the system 10 has calculated the AD, as another visualization, the images (e.g., electrodes) of the measurement sensors may be distinguished (e.g., colored or otherwise distinguished on a display) to show the relative relationship between each sensor. For example, if AD is a local activation time, the earliest detection sensor may be color-coded red, designating the "early" portion of the circuit, and the latest detection electrode may be color-coded purple, designating the "late" portion of the signal. Alternatively or additionally, the AD may be displayed on the anatomical structure and may be similarly color coded. Other variations of visual distinction are also within the spirit and scope of the present application.

The system 10 may be configured to automatically place "markers of interest" on a display of information provided by the system 10 (e.g., mapping and/or other information related to a clinical procedure performed using the system 10). The system 10 may be configured to place a marker of interest (also referred to herein as a "marker") in the displayed coordinate system and/or on anatomical structures, such as AD-based placement at a significant location. The markers may have visual attributes that specify the confidence of the marker (e.g., confidence associated with the salient location). For example, if AD is the local activation time, system 10 may display a large marker at the "earliest" location on the anatomical shell. For each heart beat detected, a new large marker may be placed on the anatomical shell. The operator may interact with the markers (e.g., via the user interface 120 of the system 10), such as to display (e.g., add) relevant information about the respective beats. Over many consecutively detected beats, the system 10 may provide (e.g., visually provide) a plurality of markers, such as when the markers indicate spatial consistency of marker positions.

The system 10 may be configured to adjust the resolution of the map (e.g., EAM), such as to change (e.g., from a lower resolution map) to (e.g., upgrade to) a high resolution map. When used with the unique beat detection and rapid mapping routines described herein, the map and label of each unique beat may be used to show the earliest location of activation. The map data may be slightly coarser when computed directly. However, any directly calculated map of detected runout may be further processed into a high resolution map using an inverse solution. The corresponding display color map and large markers will become more detailed, the EAM will be calculated at a higher resolution, and the markers will become smaller and more positionally accurate. The direct calculation of AD can be performed by the system 10 for any jitter specified by the operator to form a map, automatic placement of markers, and further processing to solve the map for high resolution inverse.

The system 10 may be configured to provide a confidence score associated with the source chamber. The system 10 may be configured to execute a "source chamber routine (chamber of origin routine)" (e.g., via the algorithm 115) that provides a confidence score for the source point (e.g., if one is present) of a given beat located in the chamber being mapped or in an adjacent chamber. The source chamber routine may use the relative timing information between the endocardial and body surface measurements to calculate a confidence score. The routine (e.g., algorithm 115) may also use one or more morphology analyses to automatically identify characteristic morphology features in the EGM at the earliest location of activation, e.g., slight positive peaks of the rS pattern, such as used by the system 10 to determine a confidence score.

The system 10 may include an algorithm 115 configured to perform a "region inverse mapping routine," such as a routine in which an inverse calculation of heart activity within a region is performed. The EAM calculated by the system 10 may be a full-chamber inverse solution map. In some embodiments, an inverse solution is applied to solve for EGMs that are applied to only a portion of the chamber. The EGMs in the calculated region may be used by the system 10 to determine AD in the region, including activation time, signal amplitude, and/or scar region. The forward matrix may be adapted (e.g., by algorithm 115) to solve the entire chamber, such as when the entire chamber lacks certain structures (e.g., lacks the left atrial appendage), and/or the forward matrix may be adapted to solve those individual structures themselves. Using the forward matrix, the inverse solution may (e.g., by algorithm 115): derived via a direct inverse method, by solving a system of equations, via one or more neural networks, and/or via an iterative solution of a regularized optimization problem. These optimizations may include residual terms, which may implement consistency with measurement and regularization terms (e.g., as well as impose regularity or a priori knowledge on the inverse solution). The residual term may use various loss metrics including least squares, absolute differences, and/or re-weighted least squares. The residual terms may also be used by the system 10 to constrain the solution to meet certain constraints, such as non-decreasing or non-increasing in time, or residing within a predetermined subspace (e.g., a subspace derived from data via linear or nonlinear methods and/or from a domain in which the solution is defined, such as a graph-based basis function). The regularization term used by the system 10 may be in the form of zero-order, first-order, and/or second-order Tikhonov regularization. Alternatively or additionally, other regularization methods may be used, such as graph-derived basis functions, dictionary-based methods, median filtering, plug-and-play methods, and/or neural network-derived regularization.

In some embodiments, system 10 may directly estimate relevant AD information without first explicitly deriving the EAM. This estimation may be performed by the system 10 by projecting and interpolating the signals captured by the AD from the catheters (e.g., signals captured by one or more catheters as described herein) to the chamber (and/or region thereof) and/or by nonlinear iterative optimization, such as to solve the AD directly given a predetermined model for electrical activity over the region of interest.

Post-processing methods may be applied to both EAM and AD solutions by system 10 (e.g., by algorithm 115) using various methods including mean and median filtering, graph-based filtering methods, and/or neural network methods.

The system 10 may include an algorithm 115 configured to perform a "superscalar procedure (supermap routine)", such as a routine configured to generate an activity map derived from signals recorded over time and from an electrode array repositioned during the recording process.

The system 10 may be configured to perform cardiac information analysis. The system 10 may process the activity data (ACTIVITY DATA, AD) to calculate additional metrics that may be used to analyze the electrical activity of the patient tissue. The system 10 (e.g., algorithm 115) may perform various analyses to identify one, two, or more clinical sites of interest. One analysis that may be performed by the system 10 is to identify a conduction pattern by the system 10 (e.g., by the algorithm 115) using one or more of a variety of path finding techniques, such as identifying streamlines and/or other techniques of clinically relevant paths, the identification process being referred to herein as "automatic path formation (autopathing)". The system 10 may perform automatic path formation by taking a series of streamlines and clustering their traversals using AD (e.g., electrophysiology data and/or biophysical data) to determine a descriptive path for electrical propagation across a given heart chamber (e.g., the atrial body). This process is an extension of the streamline-based technique of determining nearly all paths (e.g., given enough starting locations). Some of the analysis performed by the system 10 (e.g., by the algorithm 115) may be configured to quantify the spatial distribution and/or temporal occurrence of one or more features of interest, such as to characterize one or more clinical sites of interest. The automatic path formation performed by the system 10 may operate within a changing coordinate system (e.g., other than cartesian coordinates). The system 10 may represent data in a 2D conformal data space and/or in an anatomically determined data space created from subject-specific anatomies (e.g., general atrial coordinates). Some of the analyses performed by the system 10 may quantify the spatial distribution and/or temporal occurrence of features of interest, for example, to characterize clinical sites of interest. Feature recognition includes, but is not limited to: a block; isolation; isthmus part; breaking through; and/or epicardial bridging. The feature "block" may represent the disappearance of an activity at a location, with no correspondence to a juxtaposed location. The feature "isolated" may mean that the location of adjacent tissue has been electrically isolated from another region of tissue (such as the pulmonary vein electrically isolated from the body of the left atrium). The feature "isthmus" may denote a tissue region in which juxtaposed (but not necessarily continuous) pathological tissue is present, and in which re-entry phenomena are more likely to occur. The feature "breakthrough" may represent a surface location (e.g., a surface such as endocardium) in which the source of electrical activity has not originated from the surface location (e.g., the earliest active site on the surface but not within the structure). The feature "epicardial bridge" may represent a conductive path of tissue proximal to the tissue, which may be tremor or ablated.

The system 10 may be configured to identify various conduction modes, such as local irregular activation (localized irregular activation, LIA), local area activation (localized regional activation, LRA), and/or focus modes. The system 10 may be configured to detect and count the occurrence of spatiotemporal conduction patterns anywhere in a chamber (e.g., a heart chamber such as the left atrium) by analyzing spatiotemporal activation sequences present in an activation map. At each location in the chamber (vertices on the mesh) each activation at that location can be analyzed by the system 10 in the context of adjacent activations within a small surrounding area (diameter of at least 5mm or 10mm and/or diameter of no more than 25mm or 15 mm). The conduction velocity may be calculated from the activation time in the region. The activation sequence and conduction direction of each beat may be evaluated against one or more rule sets to classify local conduction patterns. The occurrence of each pattern type at each location may be quantified and may be displayed as a histogram (e.g., a color-differentiated histogram) on an anatomical model (e.g., shell), wherein higher occurrences in the same location may be visualized with differentiated visual characteristics (e.g., greater opacity and color intensity). The system 10 may simultaneously display a visualization of the occurrence of multiple pattern types.

The system 10 may be configured to determine various conductive characteristics, such as when Activation Data (AD) is processed to quantify different conductive characteristics. The system 10 may be configured to determine a conduction velocity. Conduction velocity through tissue is a highly correlated measure of tissue activity. The system 10 may use spatially and/or temporally distributed local activation times to calculate local conduction velocities. In some embodiments, local activation times on the 3D shell surface mesh may be projected onto a plane, and the spatial gradient of activation in the projected plane may be used to approximate conduction velocity. Similarly, conduction velocities may be calculated by the system 10 (e.g., by the algorithm 115) using similar operations (e.g., calculating gradient estimates for elements) arranged in an elemental manner, for example, to selectively enhance performance in certain regions (e.g., increase sensitivity to small spatial structures). The gradient operations may be performed by the system 10 using triangulation and/or limited-difference methods for estimating gradients. The conduction velocity may be color mapped (e.g., where the data is presented in a color coded or other graphically distinguished arrangement) and/or the velocity may be displayed directly. The deceleration may be calculated by calculating a gradient of conduction velocity and displayed as a conduction metric. The relative conduction velocity may be determined by the system 10, such as the determined conduction velocity (e.g., and provided to an operator) as a percentage change (e.g., decrease and/or increase) and/or a normalized value. The relative conduction velocity may be an advantageous metric to account for potential inter-patient or map-to-map variations in conduction velocity. In general, identifying areas of greatest acceleration (e.g., greatest deceleration or acceleration) may be more clinically relevant and valuable for diagnosing cardiac arrhythmias (e.g., AF) than crossing a particular speed threshold. The relative conduction velocity may be calculated as a percentage decrease or increase, and/or the velocity may be normalized to the fastest percentage of velocity in the map. Refraction of the wavefront may also be estimated by the system 10 (e.g., by the algorithm 115) using the estimated conduction velocity throughout the chamber. Refraction may be based on the angle between subsequent activations of the tissue region. Using any of these parameters, in conjunction with the activation sequence, the location of the refraction map may thus be estimated by the system 10 (e.g., by the algorithm 115). The system 10 may be configured to determine a signal amplitude. For example, the system 10 may use the amplitude of the local signal as a measure of conduction. In some embodiments, the peak-to-peak amplitude of the local bipolar signal may be color mapped (e.g., maps distinguished by changing color and/or other graphical properties). In some embodiments, the peak negative amplitude of the local unipolar signal may be color mapped. In some embodiments, the full pole or laplace operator amplitude may be color mapped. In some embodiments, the amplitude of the non-contact calculated charge density signal may be related to the amplitude of the contact voltage signal in the same location. The correlation may be used to define a representative relationship between the charge density units and the voltage units of the map. In some embodiments, this relationship may be used to create an amplitude map of both charge density and voltage data types. A representative relationship between charge density calculations and millivolt equivalents may be calibrated by the system 10 (e.g., by the algorithm 115) using the inversely calculated potentials (e.g., the potentials of signals in the direct sampling blood pool in the vicinity of the anatomy).

The system 10 may be configured to perform spatiotemporal analysis. The system 10 may perform a number of forms of spatiotemporal analysis. The map data of system 10 may include a set of spatially connected time-varying signals (electrograms) and/or time events (e.g., local activations). The system 10 may determine a multi-dimensional activation sequence or pattern. In some embodiments, the spatial distribution of temporal events may be rendered (e.g., and displayed) by the system 10 as a multi-dimensional image, where time is the first dimension, and the spatial distribution of anatomical locations may be 3 additional dimensions, or may be reduced to a smaller number of dimensions (e.g., by projection into a 2D parameterized space, or by dimension reduction mapping to common coordinates (common atrial coordinates for atrial EAM). The reduced data space may also be calculated in a conformal data space, where the system 10 (e.g., algorithm 115) uses the mitral valve as a unwrap point. Thus, the time-varying nature of the electrical activity may be captured as a spatio-temporal representative still image (spatiotemporally-REPRESENTATIVE STATIC IMAGE, SRSI), with the size of the image being largely dictated by the duration of the map data. Thus, the techniques allow highly complex multi-dimensional datasets to be processed by system 10 using image analysis and/or comparison techniques. In some embodiments SRSI may be analyzed by system 10 by searching for kernel modes of a given window size in the time dimension that repeat in other portions of the image. These repeated relapses may be clinically relevant in characterizing activity and targeted therapies. The window size may vary from very small to very large to reprocess SRSI multiple times to search for possible kernels of different sizes. In some embodiments, SRSI may be analyzed by the system 10 to obtain a spatiotemporal coupling relationship between different regions of anatomy. In SRSI, the activation sequence between the two regions of the chamber with strong coupling relationships will follow a common and consistent vector that can be detected by a variety of pattern detection techniques, including machine learning, deep learning, and/or other AI algorithms. In some embodiments, the coupling is determined by the system 10 (e.g., by the algorithm 115) via analysis of temporal variations in spatial correlation.

The system 10 may be configured to perform spatiotemporal analysis by performing network analysis. In some embodiments, the activated spatiotemporal sequence may be analyzed by system 10 as a network analysis. The network may be formed by anatomic interconnected nodes, wherein adjacent nodes are anatomically adjacent locations, and further separated nodes are further separated along the anatomic surface. For any activation at a node, the upstream and downstream activations encode the coupling relationship between anatomical regions, and the downstream activation extends through this bottleneck over a large region of anatomy, which may be an effective therapeutic target for modifying or eliminating perpetuated heart rhythms. In some embodiments, each activation at each node may be evaluated within a window (e.g., a window of at least 25ms or 50ms, and/or a window of no more than 250ms or 100 ms), such as to evaluate the area of downstream impact of the activation at each node. Areas of greater downstream impact may be more effective in perpetuating cardiac arrhythmias.

The system 10 may be configured to analyze the activated spatiotemporal sequence by visualizing the activation region at the reference time. The activation time data in the EAM may be divided into a plurality of time intervals by the system 10 (e.g., by the algorithm 115). The activation region of the reference time may be calculated as a region corresponding to a portion of the EAM having an active time within each time interval. Alternatively or additionally, the system 10 may use the number of measurements and/or points. The activation region of the reference time may be provided (e.g., visualized) as a curve, e.g., as a histogram with one axis representing time and the other axis representing activation region. In some embodiments, the system 10 may display more than one visual image simultaneously using the same timeline. Each visualization may show data from a different EAM. Each visualization may alternatively show data from the same EAM, but indicate the form of different data types, such as when non-contact data, unipolar data, and/or bipolar data are displayed (e.g., and graphically distinguished).

Similarly, the system 10 may be configured to analyze the activated spatiotemporal sequence via activation region visualization of reference amplitudes. The activation time data in the EAM may be divided into amplitude ranges. The activation region of the reference amplitude may be calculated as a region corresponding to the portion of the EAM having an active time within each time amplitude range. Alternatively or additionally, the number of measurements and/or points may be used. The system 10 may display the activation region of the reference amplitude using visualizations similar to those described above.

The system 10 may be configured to perform cardiac information analysis including data aggregation and statistical analysis. A single data set may be vulnerable to false positives and false negatives, particularly when the selected metric may only cause a deviation from the measurement of the metric itself. In some embodiments, the system 10 includes a bias (e.g., a user-configurable bias) that causes the analysis to trend toward and/or away from false positives and/or false negatives. The amplitude of the bipolar (subtracting one unipolar signal from the other unipolar signal) signal is typically used as a surrogate for measuring tissue abnormalities, and is typically measured only once. However, measuring orientation, wavefront direction, and tissue rate response all affect the amplitude of the bipolar signal, making it an unspecific measure of tissue abnormality. In some embodiments, the system 10 is configured to overcome one or more of these limitations by performing multiple measurements, changing the wavefront direction and tissue rate response to remove potential inherent deviations. Once these multiple measurements are made (e.g., using system 10), understanding the spatial consistency of any anomaly metrics improves the specificity of detecting anomalies. The system 10 may be configured to compound according to a plurality of measurements. For example, the system 10 may take multiple measurements (activations in one or more maps) under different conditions. Each measurement may include Activity Data (AD) at a anatomically common set of locations (vertices of the mesh). With multiple measurements (activations in one or more maps), each location anatomically possesses a composite set of data samples that can be statistically analyzed by the system 10. A composite dataset is a structure that aggregates multiple data into a generally analyzable structure. In some embodiments, the structure is a mesh of vertices of the anatomical structure. In some embodiments, the AD assessed as a complex is conduction velocity. In some embodiments, the AD assessed as complex is signal amplitude. The data in the composite map may be statistically analyzed or evaluated using a threshold. For example, the composite dataset may be used to visualize minimum, average, maximum and/or median conduction velocities and/or amplitudes at all locations in the heart chamber. The system 10 may be configured to perform a consistency analysis. For example, the composite data may be consolidated by the system 10 by applying a threshold. For example, if a conduction velocity threshold (e.g., a threshold of 0.3 m/s) is used as the threshold for abnormal conduction (slower, typically more abnormal), the composite data may be evaluated by counting any conduction velocities (conduction velocity, CVs) in the composite data set that are less than the threshold as abnormal and/or any CVs that are greater than the threshold as normal. A consistency map may then be displayed that shows areas with consistent abnormal CVs, consistent normal CVs, or inconsistent abnormal CVs in a visually differentiated manner (e.g., via color coded visualization). in some embodiments, the CV is thresholded by the system 10 to form a consistency map. In some embodiments, the signal amplitude is thresholded by the system 10 to form a consistency map. In some embodiments, the thresholds of abnormal activity are combined by the system 10 between the CV and the signal amplitude to form a consistency map. In some embodiments, multiple metrics or thresholded metrics may be combined into a score for each activation by the system 10, which may then be displayed in the composite map.

As described above, the system 10 may be configured to perform one or more cardiac activation analyses, such as when performing "fusion of clinical measurements and/or computational modeling. The system 10 may be configured to perform "anatomical data co-registration," such as via "generic anatomical model" and/or "landmark co-registration" (skeleton). The anatomical data co-registration performed by the system 10 may include the system 10 (e.g., algorithm 115), which system 10 assumes a fairly consistent collocation of the four chambers of the heart, and where a subject-specific (i.e., patient-specific) orientation cannot be determined, the relative positioning of the various chambers may be determined via population-level averages (e.g., averages from human subject samples). the general anatomical model of the system 10 may utilize a global heart positioning system to coordinate chambers of the heart relative to one another. Landmark co-registration of system 10 may utilize a computed skeleton that tracks the relationship between four chambers of the heart and/or a series of 2D cross-sections in 3D space, such as to determine relative orientation and positioning between opposing chambers. The system 10 may be configured to perform "CV aberration/divergence modeling" such as when the system 10 makes measurements (e.g., clinical pacing measurements) from one or more locations (e.g., using the over-mapping routine and/or single location routine described herein). The activation may be mapped and analyzed to find areas of the block (e.g., quarantine). The system 10 may use the same chamber anatomy (e.g., as previously calculated and/or presented) and computationally apply the regions of the block. The "recovery score (restitution score)" determined by the system 10 may include a score determined by analyzing conduction velocity at a series of sites across varying pacing rates, so that subject-specific recovery information may be developed to thereby parameterize subject-specific simulations or compare the extent of recovery-related changes to population averages that are an index of recovery (e.g., recovery score). The system 10 may use a model of simulated propagation to calculate the chamber wide activation sequence. The model may be isotropic. In some embodiments, the model is parameterized by the system 10 (e.g., by the algorithm 115) based on the measured activation, so that the system 10 can begin simulation from the point of change in the measured activation. For example, the system 10 may use the first 10% of the measured activation, which may be compared to the remaining 90% of the measured activation for the simulation. This process can induce a number of propagation properties that exhibit anisotropic-like divergence, which can be attributed to fiber orientation or substrate-related changes. Alternatively or additionally, the model may be anisotropic (e.g., have heterogeneous characteristics) and/or the model may be determined based on population averages and/or population atlases. The heterogeneity of the model may be based on: a standard model; and/or data from one or more measurements, such as data acquired from CT and/or MRI. For example, the system 10 may use a fibrosis score and/or an arrhythmogenic score based on spatial configuration of intensities in CT and/or MRI data and/or substrate specific changes. These intensity changes may be elicited via contrast agents or by analysis of standard imaging protocols and/or clinical maps (e.g., composite maps of conduction velocities). The system 10 may use a series of conduction velocity maps to estimate fiber orientation across a heart chamber (e.g., atrium), and these estimates may then be used to generate an anisotropic simulation that takes into account fiber orientation. In some embodiments, differences between anisotropic simulation and measurement tend to indicate that the simulation framework of system 10 does not account for substrate-related differences. The system 10 may compare the clinical pacing maps to the simulated propagation to determine differences in conduction behavior. The system 10 may calculate the directional divergence between the clinical and simulated maps, such as to display preferential conduction directions (e.g., those that may exist in anisotropic fiber orientations) or substrate-related changes. As described above, the estimate provided by the system 10 simulation framework (which itself is parameterized by the EAM or population average) may be compared to the measurement itself, such as to determine a region that may indicate maximum divergence of abnormal tissue. the system 10 may calculate aberrations in the clinical map (e.g., aberrations that differ from the simulation framework in terms of velocity, recovery, start-up, breakthrough, and/or other propagation-related phenomena). The system 10 may perform analog pacing mapping to find gaps, for example, to: tracking delivered therapy and all therapy parameters; modeling the effect of delivered therapy on local conduction (e.g., local conduction may be unchanged, partially/moderately changed, or completely eliminated (no conduction)); and/or simulate initiation of activation from one or more regions engaged with the delivered therapy. For example, based on one or more system 10 thresholds (e.g., user-defined thresholds), a series of criteria settings, or a combination of both, system 10 may generate an electrical propagation simulation (e.g., subject-specific, rule-based, or combination) to track delivered therapy. Additionally, the system 10 may be configured to model the effect of the delivered therapy on local conduction. The local conduction may be unchanged, partially, and/or otherwise moderately changed or completely eliminated (e.g., no conduction), and as such, in the physiological simulation described herein, it may be parameterized by the system 10 (e.g., by the algorithm 115). The parameterization performed by the system 10 may take into account default geometric parameters (e.g., tissue thickness), electrophysiological parameters (e.g., recovery curve data), and/or anatomical measurement data.

The system 10 may include a "data management architecture". The system 10 data management architecture (DATA MANAGEMENT architecture, DMA) may include an arrangement in which information (e.g., captured and/or information related to a clinical procedure performed on a patient) is stored spatially. Broadly, the DMA may comprise a regular grid of volumes large enough to contain a typical four-chamber heart. The architecture may be formed of a regular structured rectilinear mesh, an unstructured tetrahedral mesh, and/or an unstructured hexahedral mesh. The data store may include: one or more commonly referenced data spaces; and/or a hierarchy of data spaces (e.g., parent, peer, child, etc.). The system 10 may perform data processing in each data space. The system 10 may recalculate (e.g., reconstruct) the data in the "child" data space, such as based on the recalculation of the "parent" data space. The system 10 may provide a visualization of these different data (e.g., measured, determined, and/or calculated data).

The treatment and navigation information may be stored by the system 10 on an element-by-element basis, such as for further processing and/or analysis performed by the system 10 and/or an operator using the system 10. One or more commonly referenced data spaces (e.g., domains within a DMA) may be computationally associated with each other, for example, to facilitate two-chamber mapping. In some embodiments, the shift in geometry may be more appropriate than the shift in physiology, but the system 10 may be configured to constrain biophysical activity to reflect the actual physiological configuration. These data spaces may be: provided (e.g., displayed), obscured, highlighted, and/or enhanced by system 10 (e.g., by the needs of a user of system 10).

A hierarchy of data spaces within the data space (e.g., in a parent, peer, child arrangement) may exist, whereby several independent geometries may include dependent properties that may be inherited based on examination in one geometry (e.g., in a portion of the geometry) but not in another geometry. These computational attribute inheritance mechanisms may be governed and facilitated by the interrelationship of different geometries by the system 10. For example, the left and right atria may have a peer-to-peer relationship in which there is a relatively minimal inheritance of characteristics. Alternatively, the left atrial appendage may be classified by the system 10 as a child of the left atrium, and the parent-child relationship may have more substantial inheritance. Data processing in each data space may utilize DMA to regularize and constrain data manipulation, for example, to facilitate analysis and visualization of the whole chamber to whole organ level. For example, the DMA may be used to: constraining global electrical solutions across the dual atrial geometry; constrained geometry manipulation and performing "shaving" of the heart chamber (e.g., one or two atrial bodies); and/or solve the piecewise inverse problem of the optimization for the dual atrium.

The recovery data in the "child" data space based on the "parent" data space may include changes based on particular criteria, and certain parameters may be delegated by the system 10 (e.g., by the algorithm 115) to peers and/or children of a particular geometry, changing geometry and/or anatomy based on the delegation of measurements and/or parameters.

The visualization of the data by the system 10 may include a volumetric mesh for facilitating a 3D visualization that may be only partially associated with the underlying cardiac mesh. These visual elements may be scalar, vector, matrix, and/or tensor-based visualization and/or analysis. Additionally, visual elements provided by system 10 may utilize changes in color, size, shape, and/or other variable graphical parameters to suggest tissue, organ characteristics, and/or accuracy and/or confidence in a given metric.

Establishing a Finite Element Method (FEM) framework for subject-specific or atlas-based simulation may include using structured and unstructured grids including DMA and a series of FEM-based analyses. Within the DMA, the system 10 may perform a dual domain (bidomain), a single domain (monodomain), a pseudo-dual domain, an elution domain (eikonal), a reactive elution domain, and a french simulation (courtemanche-type simulation), for example, when one or more anatomical cavity models generated by the system 10 are simulated by the system 10. These computational models may be parameterized by subject-specific AD, and/or the models may be strictly rule-based and/or population-based.

The system 10 may be configured to perform event-driven user interface control and data presentation. The system 10 may provide "data elements" of information throughout the clinical procedure, and the system 10 may track these data elements, for example when tracking one, two or more data elements selected from: recording the signal; a user action; clinical events; EAM; treatment delivered; classification of heart rhythm; a jitter group; a jitter feature; and combinations of these. Each data element may be saved with time information (e.g., a timestamp). The data elements may be displayed in a time-sequential timeline. Data elements of different data types (e.g., classified by algorithm 115 of system 10) may be displayed in a timeline simultaneously. For example, data collection, beat sets, created EAMs, delivered treatments, markers, collected anatomies, case events, and/or time calipers may be shown in different parallel tracks in a synchronized timeline. Each track may be graphically distinguished (e.g., color coded or otherwise graphically distinguished) corresponding to user interface representations in and/or for the remainder of system 10. The system 10 operation may be initiated via one or more specific user actions (e.g., clicking an icon or pressing a hot key), which may minimize recording time and/or mark a reference visualization on a timeline as an "event". Additional information may be added to the event, such as a text label and/or a note. The event may be modified to other forms of data including time calipers, EAMs, beat tags, beat sets, anatomical markers, and/or tags. Upon review, the user (e.g., clinician or other operator) may return to the event on the timeline and observe the system environment (e.g., system parameter levels, patient physiological data, and/or other information) that is present and/or otherwise relevant at that time in the clinical procedure. Operations of system 10 initiated through the user interface of system 10 may also be recorded as events on a timeline, where corresponding attributes of the operations are automatically applied to the events. User actions and changes may also be stored as events, including actions and changes such as setting up (e.g., modifying) an application, computing parameters, applying filters, creation of data entities (e.g., anatomical parts, beat sets, text labels, and/or graphical markers), changes to anatomical data (e.g., shaving, cutting, and/or adding anatomy), and so forth. The event information may also be displayed as a corresponding log or list. In some embodiments, the time-series representation of event data may be used to undo or redo user actions through a user interface, such as the Graphical User Interface (GUI) of the system 10 described herein, the GUI 125, such as by dragging an indicator to exclude previous user actions or clicking on previous actions and deleting them. Undoing a previous event may be performed on a set of consecutive events that resulted in the current state of the system. The events may be deleted asynchronously and if deletion of an event asynchronously requires that the associated event also be deleted, the system 10 may notify the user and visually designate the associated event prior to deletion.

Fig. 2 to 8B below illustrate various examples of the inventive concept described above.

Referring now to fig. 2, an embodiment of an anatomical model representing a tissue surface consistent with the inventive concepts is shown. The system 10 may be configured to display an anatomical model representing a wall of a heart chamber and/or another tissue surface of a patient. The anatomical model may be displayed to a user (e.g., a clinician of a patient) via a user interface, such as via GUI 125 shown and described herein. Portions of the anatomical model may include varying graphical characteristics, such as varying color intensity, opacity, or another variable graphical parameter used to distinguish data. The changed characteristics may represent changes in the anatomical model, for example, changes in the density of data collected to calculate a portion of the anatomical model. In some embodiments, the system 10 may display one or more points proximate to the anatomical model, such as one or more points representing data collected by the system 10. For example, the system 10 may display points representing ultrasound data points collected using one or more devices configured to transmit and/or receive ultrasound signals (e.g., as described herein).

Referring now to fig. 3, an example of a graphical user interface displaying cardiac mapping data consistent with the inventive concepts is shown. The system 10 may be configured to provide a graphical user interface, such as the GUI 125 shown. GUI 125 may include a plurality of display areas configured to present to a user information collected and/or calculated by system 10. The information may be displayed to the user via one or more graphical representations, such as representations selected from: visual models, such as 2D and/or 3D models; a figure; a graph; a time line; stacking; an icon; other visual representations of the data (e.g., geometry, orientation, and/or other location data of the system 10 device); and combinations of these. GUI 125 may display information related to one or more groups of heartbeats identified by system 10 as described herein. In some embodiments, beat groups may be graphically distinguished (e.g., color coded) based on one or more unique features of the beat groups (e.g., morphology, cycle length, time series, energy distribution, and/or other one or more features). In some embodiments, each beat group identified by system 10 is indexed (e.g., assigned an integer value). In some embodiments, beats of one or more records not assigned to a beat group may be displayed with unique graphical attributes (e.g., unique colors and/or patterns).

In some embodiments, GUI 125 includes a "timeline view," as shown in FIG. 3. The timeline view may display various data types relative to the timeline (e.g., parallel relative to the timeline). For example, the timeline view may indicate when data is collected and/or what time period the results are calculated. The timeline view may display an indicator representing data selected from the group consisting of: an indicator of a time period when the mapping data is recorded and/or processed by the system 10 (e.g., wherein a beat packet is performed); an indicator of a time period for which a cardiac activity map has been created; an indicator of when the treatment was delivered to the patient; an indicator of a time period when the system 10 collects anatomical data; an indicator of when to annotate the displayed data (e.g., an annotation made by a clinician via a user interface of system 10); and combinations of these.

In some embodiments, GUI 125 includes an "activation region," as shown. The activation region may display a histogram representation of the activation curve with reference to time, such as a curve showing the number of points and/or the region of the active heart chamber with respect to time. Alternatively or additionally, the activation display area may display a histogram representation of the activation curve of the reference amplitude, such as a curve showing the activation amplitude range versus the point and/or area of the heart chamber.

Referring now to fig. 4A and 4B, examples of a graphical user interface including an anatomical model and an anatomical model including various markers, respectively, consistent with the inventive concepts are shown. The system 10 may be configured to provide a graphical user interface including an anatomical model, such as the GUI 125 shown. GUI 125 may include (e.g., provide) graphical data, such as cardiac activity data shown in graphical form, as shown. In fig. 4A, beats (e.g., heartbeats) that have been identified by the system 10 are identified (e.g., distinguished) by white boxes accordingly in the graphical data, as shown. The anatomical model may include a map of activation times (e.g., color maps of different gray scales as shown and/or color maps of other different color schemes). One or more visual markers may be displayed relative to the anatomical model, such as visual markers indicating the point on the anatomical model where the earliest activation was recorded. In fig. 4B, additional visual indicia are displayed. In some embodiments, the size of the visual marker displayed on the anatomical model may be related to the accuracy of the data represented by the marker.

Referring now to fig. 5A and 5B, there are shown, respectively, flowcharts representative of embodiments of machine learning and/or other AI-based cardiac rhythm classification methods and cardiac activity data representations consistent with the present concepts. The heart signals (e.g., unipolar EGM signals recorded from the coronary sinus) may be recorded and classified to represent various heart rhythms to be identified by a "heart rhythm classification algorithm" (e.g., algorithm 115, including AI algorithms or other algorithms configured to perform heart rhythm classification). The heart rhythm to be identified may be selected from the following: sinus rhythm; atrial flutter; atrial fibrillation; a paced heart rhythm; and combinations of these. In some embodiments, the recorded signal is downsampled. The classified set of recorded cardiac data may be transformed by the system 10 into a wavelet scalar map (e.g., may be decomposed into different frequency bands while preserving time information). An algorithm (e.g., algorithm 115 described herein), such as an AI algorithm including a convolutional neural network, may be trained on the wavelet scalar map to identify heart rhythms. In some embodiments, the performance of the AI algorithm may be evaluated using the validation data, for example, to determine the classification accuracy and/or positive predictive value of the algorithm.

Referring now to fig. 6, various embodiments of an anatomical model are shown on which a cardiac activity map is displayed, consistent with the inventive concepts. In some embodiments, system 10 calculates the conduction velocity based on the time of cardiac activation. In the lower left part of fig. 6a map of the time of activation of the heart is shown. A plot of conduction velocity is shown in the bottom middle portion of fig. 6. The system 10 may identify a spatiotemporal pattern across cardiac tissue. These spatiotemporal patterns can be displayed relative to one or more anatomical models, as shown on the right-hand portion of fig. 6. In some embodiments, the spatiotemporal pattern is displayed as a histogram that is colored (e.g., using other arrays of gray level changes or color changes as shown) on the surface of the anatomical model. In the example shown in fig. 6, a brighter, more opaque color represents a greater popularity for each mode displayed. Further, in the example shown, the modes shown include focus (a), rotation (B), and complex pivoting and re-entry modes (C).

Referring now to fig. 7, various embodiments of an anatomical model on which a map of cardiac activity is displayed are shown consistent with the inventive concepts. In some embodiments, the cardiac activity map generated by the system 10 may indicate an average velocity of conduction through cardiac tissue. In fig. 7, based on data collected over 5-7 seconds (where 50 activations occur), a slowly conducting region (e.g., less than 0.3 m/s) is identified. In some embodiments, multiple cardiac activity maps may be aggregated by the system 10 and an associated composite map may be displayed.

Referring now to fig. 8A, an embodiment of a spatiotemporal representative diagram of cardiac activity consistent with the inventive concepts is shown. In fig. 8, the long axis of the graph represents time (e.g., time axis). In some embodiments, the map includes a 2D representation of a 3D map of cardiac electrical activity data (e.g., EAM described herein). Referring additionally to fig. 8B, an embodiment of a color-coded map of cardiac activity consistent with the inventive concepts is shown. In some embodiments, the system 10 may analyze the spatiotemporal representation for a repeating sequence (e.g., a repeating sequence of cardiac activity). As shown, the identified repeated sequence may be color coded.

The above embodiments should be understood to be used as illustrative examples only; other embodiments are contemplated. Any feature described herein with respect to any one embodiment may be used alone, or in combination with other features described, and may also be used in combination with one or more features of any other embodiment, or any combination of any other embodiment. Furthermore, equivalents and modifications not described above may also be employed without departing from the scope of the inventive concept, which is defined in the accompanying claims.

Claims (2)

1. A cardiac information dynamic display system, comprising: one or more electrodes configured to record a data set of electrical potentials representing cardiac activity at a plurality of time intervals; and Cardiac information console, including: The signal processor is configured as: calculating a cardiac activity data set at the plurality of time intervals using the recorded electrical potential data set, wherein the cardiac activity data is associated with a surface location of one or more cardiac chambers; and A user interface module is configured to display information related to the cardiac activity data, the information presented relative to a graphical representation of a surface of the one or more cardiac chambers. 2. A method for dynamically displaying cardiac information, comprising: Using one or more electrodes, recording a data set of electrical potentials representing cardiac activity at a plurality of time intervals; and Using the Cardiac Information Console including the signal processor and user interface module: calculating a cardiac activity data set at the plurality of time intervals using the recorded electrical potential data set, wherein the cardiac activity data is associated with a surface location of one or more cardiac chambers; and Information related to the cardiac activity data is displayed, the information presented relative to a graphical representation of a surface of the one or more cardiac chambers.