WO2025224544A1

WO2025224544A1 - Operation of a medical device system to identify particular cardiac data samples associated with a cardiac event

Info

Publication number: WO2025224544A1
Application number: PCT/IB2025/053479
Authority: WO
Inventors: Kelvin MEI; Rodolphe Katra; Brandon D. Stoick
Original assignee: Medtronic Inc
Current assignee: Medtronic Inc
Priority date: 2024-04-24
Filing date: 2025-04-02
Publication date: 2025-10-30
Anticipated expiration: 2026-10-24

Abstract

An example system includes a memory configured to store a plurality of sets of cardiac data, each set of cardiac data of the plurality of sets of cardiac data is associated with a patient; and processing circuitry in communication with the memory, wherein the processing circuitry is configured to: cause a user interface to display a set of cardiac data of the plurality of sets of cardiac data, wherein the set of cardiac data includes a plurality of cardiac data samples, and wherein each cardiac data sample is associated with a cardiac event probability that the cardiac data sample is associated with a cardiac event; receive, from the user interface, a user selection of a cardiac event probability threshold corresponding to the cardiac event; and cause the user interface to indicate each cardiac data sample associated with a cardiac event probability that is greater than the cardiac event probability threshold.

Description

OPERATION OF A MEDICAL DEVICE SYSTEM TO IDENTIFY PARTICULAR CARDIAC DATA SAMPLES ASSOCIATED WITH A CARDIAC EVENT

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63/638,054, filed April 24, 2024, the entire content of which is incorporated herein by reference.

FIELD

[0002] The disclosure relates generally to medical device systems and, more particularly, medical device systems configured to cause a user interface to display a set of cardiac data.

BACKGROUND

[0003] Medical devices may be used to monitor physiological signals of a patient. For example, some medical devices are configured to sense cardiac data, such as electrocardiogram (ECG) signals. Some medical devices are additionally or alternatively configured to sense other cardiac data, such as heart sound signals indicative of the mechanical activity of the heart via a motion or vibration sensor, such as an accelerometer or microphone. Some medical devices may be configured to deliver a therapy in conjunction with or separate from the monitoring of physiological signals.

SUMMARY

[0004] In general, this disclosure is directed to techniques for causing a user interface to indicate one or more particular cardiac data samples of a plurality of cardiac data samples in a set of cardiac data is associated with a cardiac event probability that is greater than a cardiac event probability threshold corresponding to a particular cardiac event. Processing circuitry may implement the techniques to store, in a memory, a plurality of sets of cardiac data, each set of cardiac data of the plurality of sets of cardiac data being associated with a patient. In some examples, processing circuitry may cause a user interface to display a set of cardiac data of the plurality of sets of cardiac data. A set of cardiac data may include a plurality of cardiac data samples. In some examples, each cardiac data sample may be associated with a cardiac event probability that the cardiac data sample is associated with a particular cardiac event, such as arrythmia, premature ventricular contraction, or an indication the respective cardiac data sample comprises at least part of a particular feature of the ECG signal, such as P-wave or T-wave.

[0005] In some examples, processing circuitry may receive, from the user interface, a selection of a cardiac event probability threshold corresponding to the particular cardiac event. Processing circuitry may cause the user interface to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the cardiac event probability threshold.

[0006] In some examples, processing circuitry may determine and assign a cardiac event probability percentage to each of cardiac data samples of the set of cardiac data. In some examples, processing circuitry may apply a model, such as a machine learning model, to determine and assign a cardiac event probability percentage to one or more cardiac data samples. In some examples, an assigned cardiac event probability percentage may indicate a percentage chance that a respective cardiac data sample is associated with a particular cardiac event.

[0007] The techniques of this disclosure causing a user interface to indicate each cardiac data sample that is associated with a cardiac event probability that is greater than a user selected cardiac event probability threshold corresponding to a particular cardiac event may more efficiently and more quickly cause a user interface to display identified cardiac data samples of cardiac data that correspond to cardiac events with user selected specificity and sensitivity and/or provide a display on a user interface of patient personalized identified cardiac data samples of cardiac data that correspond to cardiac events. In some examples, setting cardiac event probability thresholds on a patient-by- patient basis may generate an improved display on a user interface that may reduce an amount of time that a clinician has to spend identifying relevant cardiac events in cardiac data and/or may help a clinician evaluate data corresponding to the patient to track one or more patient conditions.

[0008] In one example, this disclosure describes a system comprising a memory configured to store a plurality of sets of cardiac data, wherein each set of cardiac data of the plurality of sets of cardiac data is associated with a patient; and processing circuitry in communication with the memory, wherein the processing circuitry is configured to: cause a user interface to display a set of cardiac data of the plurality of sets of cardiac data, wherein the set of cardiac data includes a plurality of cardiac data samples, and wherein each cardiac data sample of the plurality of cardiac data samples is associated with a cardiac event probability that the cardiac data sample is associated with a cardiac event; receive, from the user interface, a user selection of a cardiac event probability threshold corresponding to the cardiac event; and cause the user interface to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the cardiac event probability threshold.

[0009] In another example, this disclosure describes a method comprising: storing a plurality of sets of cardiac data, wherein each set of cardiac data of the plurality of sets of cardiac data is associated with a patient; causing a user interface to display a set of cardiac data of the plurality of sets of cardiac data, wherein the set of cardiac data includes a plurality of cardiac data samples, and wherein each cardiac data sample of the plurality of cardiac data samples is associated with a cardiac event probability that the cardiac data sample is associated with a cardiac event; receiving, from the user interface, a user selection of a cardiac event probability threshold corresponding to the cardiac event; and causing the user interface to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the cardiac event probability threshold.

[0010] In another example, this disclosure describes a non-transitory computer- readable storage medium storing instructions, which when executed, cause processing circuitry to execute storing a plurality of sets of cardiac data, wherein each set of cardiac data of the plurality of sets of cardiac data is associated with a patient; causing a user interface to display a set of cardiac data of the plurality of sets of cardiac data, wherein the set of cardiac data includes a plurality of cardiac data samples, and wherein each cardiac data sample of the plurality of cardiac data samples is associated with a cardiac event probability that the cardiac data sample is associated with a cardiac event; receiving, from the user interface, a user selection of a cardiac event probability threshold corresponding to the cardiac event; and causing the user interface to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the cardiac event probability threshold.

[0011] The summary is intended to provide an overview of the subject matter described in this disclosure. It is not intended to provide an exclusive or exhaustive explanation of the systems, device, and methods described in detail within the accompanying drawings and description below. Further details of one or more examples of this disclosure are set forth in the accompanying drawings and in the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012] FIG. 1 illustrates the environment of an example medical system in conjunction with a patient.

[0013] FIG. 2 is a functional block diagram illustrating an example configuration of the implantable medical device (IMD) of the medical system of FIG. 1.

[0014] FIG. 3 is a conceptual side-view diagram illustrating an example configuration of the IMD of FIGS. 1 and 2.

[0015] FIG. 4A is a conceptual side-view diagram illustrating an example configuration of the IMD of FIGS. 1-2.

[0016] FIG. 4B is a conceptual side-view diagram illustrating an example configuration of the IMD of FIGS. 1-3.

[0017] FIG. 5 is a functional block diagram illustrating an example configuration of the external device of FIG. 1.

[0018] FIG. 6 is a block diagram illustrating an example system that includes an access point, a network, external computing devices, such as a server, and one or more other computing devices, which may be coupled to the IMD and external device of FIGS. 1-5.

[0019] FIGS. 7A-7C are diagrams illustrating examples of user interface displaying a set of cardiac data, in accordance with examples of the current disclosure.

[0020] FIG. 7D is diagram illustrating an example of user interface displaying a selection of one or more cardiac events of a plurality of cardiac events, in accordance with examples of the current disclosure.

[0021] FIG. 8 is a flow diagram illustrating an example technique for operating a system to cause a user interface to indicate each cardiac data sample associated with a cardiac event probability that is greater than a selected cardiac event probability threshold. [0022] FIG. 9 is a conceptual diagram illustrating an example training process for an artificial intelligence model, in accordance with examples of the current disclosure.

[0023] FIG. 10 is a conceptual diagram illustrating an example ML model configured to determine a cardiac event probability a cardiac data sample is associated with a particular cardiac event.

[0024] Like reference characters denote like elements throughout the description and figures.

DETAILED DESCRIPTION

[0025] A variety of types of medical devices sense cardiac data. In some examples, cardiac data may include one or more of electrocardiogram (ECG or EKG) signals, electrogram signals, and/or heart sound signals. Some medical devices that sense cardiac data are non-invasive, e.g., using a plurality of electrodes placed in contact with external portions of the patient, such as at various locations on the skin of the patient. The electrodes used to monitor the cardiac data in these non-invasive processes may be attached to the patient using an adhesive, strap, belt, or vest, as examples, and electrically coupled to a monitoring device, such as an electrocardiograph, Holter monitor, or other electronic device. The electrodes are configured to sense electrical signals associated with the electrical activity of the heart or other cardiac tissue of the patient, and to provide these sensed electrical signals to the electronic device for further processing and/or display of the electrical signals. The non-invasive devices and methods may be utilized on a temporary basis, for example to monitor a patient during a clinical visit, such as during a doctor’s appointment, or for example for a predetermined period of time, for example for one day (twenty-four hours), or for a period of several days.

[0026] External devices that may be used to non-invasively sense and monitor cardiac data include wearable devices with electrodes configured to contact the skin of the patient, such as patches, watches, or necklaces. One example of a wearable physiological monitor configured to sense cardiac data is the SEEQ™ Mobile Cardiac Telemetry System, available from Medtronic, Inc., of Minneapolis, Minnesota. Such external devices may facilitate relatively longer-term monitoring of patients during normal daily activities, and may periodically transmit collected data to a network service, such as the Medtronic Carelink™ Network. [0027] Some IMDs also sense and monitor cardiac data. The electrodes used by IMDs to sense cardiac data are typically integrated with a housing of the IMD and/or coupled to the IMD via one or more elongated leads. Example IMDs that monitor cardiac data include pacemakers and implantable cardioverter-defibrillators, which may be coupled to intravascular or extravascular leads, as well as pacemakers with housings configured for implantation within the heart, which may be leadless. An example of pacemaker configured for intracardiac implantation is the Micra™ Transcatheter Pacing System, available from Medtronic, Inc. Some IMDs that do not provide therapy, e.g., implantable patient monitors, sense cardiac data. One example of such an IMD is the Reveal LINQ™ and LINQ II™ Insertable Cardiac Monitors (ICMs), available from Medtronic, Inc, which may be inserted subcutaneously. Such IMDs may facilitate relatively longer-term monitoring of patients during normal daily activities, and may periodically transmit collected data to a network service, such as the Medtronic Carelink™ Network. In some examples, such IMDs may transmit sensed cardiac data to an external computing device. [0028] Any medical device configured to sense cardiac data, such as via implanted or external electrodes, including the examples identified herein, may sense a plurality of sets of cardiac data. In some examples, a medical device, such as an IMD, may include a memory and store the plurality of sets of cardiac in the memory. In some examples, an external computing device may store the plurality of sets of cardiac data.

[0029] In some examples, each set of cardiac data may include a plurality of cardiac data samples. In some examples, a cardiac data sample may include one or more data points of the cardiac data. For example, a cardiac data sample may include between 1 and 100 data points of the cardiac data. In some examples, processing circuitry, such as processing circuitry of an IMD, external computing device, and/or cloud computing device, may determine and assign a cardiac event probability percentage to one or more cardiac data samples. In some examples, processing circuitry may apply a model, such as a machine learning model, to generate a cardiac event probability percentage of one or more cardiac data samples. In some examples, an assigned cardiac event probability percentage may indicate a percentage chance that a respective cardiac data sample is associated with a particular cardiac event. In some examples, a cardiac event may include a P-wave, a T- wave, portion of a P-wave, portion of a T-wave, or arrhythmias, such as premature ventricular contractions (PVCs), premature atrial contraction (PAC), noise, asystole, artifact, pacing spike, defibrillation spike, polymorphous ventricular tachycardia (PVT), or nonsustained ventricular tachycardia (NSVT).

[0030] In some examples, processing circuitry may receive, such as from a user interface, a selection of a cardiac event probability threshold corresponding to the cardiac event. Processing circuitry may cause the user interface, such as a user interface of an external computing device, to indicate one or more cardiac data samples of the plurality of data samples of a set of cardiac data that is associated with a cardiac event probability that is greater than the cardiac event probability threshold. In some examples, processing circuitry may cause the user interface, such as a user interface of an external computing device, to indicate each cardiac data samples of the plurality of data samples of a set of cardiac data that is associated with a cardiac event probability that is greater than the cardiac event probability threshold. For example, processing circuitry may determine that a first cardiac data sample has a first cardiac event probability of being associated with the cardiac event, such as 5%, and may determine a second cardiac data sample has a second cardiac event probability of being associated with the cardiac event, such as 75%. In response to receiving a user selection of a cardiac event probability threshold, such as 45%, processing circuitry may cause the user interface to indicate the second cardiac data sample satisfies the cardiac event probability threshold of being associated with the cardiac event. In some examples, processing circuitry may cause the user interface to indicate a particular cardiac data sample, such as the second cardiac data sample, by highlighting, coloring, adjusting line thickness, adjusting line style (e.g., dashed, doubledashed, symbol, etc.), highlighting of an entire region with a box, underlining a region, using transparency to indicate a region (e.g., alpha value), labelling with floating text, and/or providing arrows or lines to indicate a start and/or stop of a region, etc.

[0031] The processing circuitry may also either not provide indications or provide different indications of the cardiac data samples that do not satisfy the cardiac event probability threshold, such as the first cardiac data sample, so the cardiac data samples of the set of cardiac data that do satisfy the cardiac event probability thresholds, which are selected by a user (e.g., a clinician), may be more quickly and clearly displayed on a user interface. The techniques of this disclosure may more efficiently and more quickly cause a user interface to display identified cardiac data samples of cardiac data that correspond to cardiac events with user selected specificity and sensitivity. In some examples, setting cardiac event probability thresholds on a patient-by-patient basis may generate an improved display on a user interface that may reduce an amount of time that a clinician has to spend identifying relevant cardiac events in cardiac data and/or may help a clinician evaluate data corresponding to the patient to track one or more patient conditions.

[0032] FIG. 1 illustrates the environment of an example medical system 2 in conjunction with a patient 4, in accordance with one or more techniques of this disclosure. The example techniques may be used with an IMD 10, which may be in wireless communication with at least one of external device 12 and other devices not pictured in FIG. 1. In some examples, IMD 10 is implanted outside of a thoracic cavity of patient 4 (e.g., subcutaneously in the pectoral location illustrated in FIG. 1). IMD 10 may be positioned near the sternum near or just below the level of the heart of patient 4, e.g., at least partially within the cardiac silhouette. IMD 10 includes a plurality of electrodes (not shown in FIG. 1), and is configured to sense a cardiac data via the plurality of electrodes. In some examples, IMD 10 takes the form of the Reveal LINQ™ or LINQ II ICM™, or another ICM similar to, e.g., a version or modification of, the LINQ™ ICMs.

[0033] External device 12 may be a computing device with a display viewable by the user and an interface for providing input to external device 12 (i.e., a user input mechanism). In some examples, external device 12 may be a notebook computer, tablet computer, workstation, one or more servers, cellular phone, smartphone, personal digital assistant, or another computing device that may run an application that enables the computing device to interact with IMD 10. External device 12 is configured to communicate with IMD 10 and, optionally, another computing device (not illustrated in FIG. 1), via wireless communication. External device 12, for example, may communicate via near-field communication technologies (e.g., inductive coupling, NFC or other communication technologies operable at ranges less than 10-20 cm) and far-field communication technologies (e.g., RF telemetry according to the 802.11 or Bluetooth® specification sets, or other communication technologies operable at ranges greater than near-field communication technologies).

[0034] In some examples, external device 12 may be or additionally include wearable computing device. Awearable computing device may include electrodes and other sensors to sense physiological signals of patient 4, and may collect and store physiological data and detect episodes based on such signals. Wearable computing device may be incorporated into the apparel of patient 4, such as within clothing, shoes, eyeglasses, a watch or wristband, a hat, etc. In some examples, a wearable device may be a smartwatch or other accessory or peripheral for external device 12, for example when external device 12 is a smartphone or tablet.

[0035] External device 12 may be used to configure operational parameters for IMD 10. External device 12 may be used to retrieve data from IMD 10. The retrieved data may include a plurality of sets of cardiac data sensed by IMD 10. In some examples, cardiac data may include an ECG signal. In some examples, a set of cardiac data may include a plurality of cardiac data samples. For example, a set of cardiac data may include an ECG signal for a first period of time, and a cardiac data sample may be a portion of the ECG signal over a second period of time, the second period of time being within the first period of time and being less than the first period of time. In some examples, a cardiac data sample may include one or more data points of the cardiac data. For example, a cardiac data sample may include between 1 and 100 data points of the cardiac data.

[0036] External device 12 may also retrieve set(s) of cardiac data recorded by IMD 10, e.g., according to a schedule, due to IMD 10 determining that an acute cardiac event, such as a PVC or another arrhythmia, occurred during the set, or in response to a request to record the segment from patient 4 or another user. As discussed in greater detail below with respect to FIG. 6, one or more remote computing devices may interact with IMD 10 in a manner similar to external device 12, e.g., to program IMD 10 and/or retrieve data from IMD 10, via a network.

[0037] Processing circuitry of medical system 2, e.g., of IMD 10, external device 12, and/or of one or more other computing devices, may be configured to perform the example techniques of this disclosure for causing a user interface to display a set of cardiac data of the plurality of sets of cardiac data, wherein the set of cardiac data includes a plurality of cardiac data samples, and wherein each cardiac data sample of the plurality of cardiac data samples is associated with a cardiac event probability that the cardiac data sample is associated with a cardiac event, receiving, from the user interface, a user selection of a cardiac event probability threshold corresponding to the cardiac event, and causing the user interface to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the cardiac event probability threshold. [0038] In some examples, processing circuitry of medical system 2 may determine and assign a cardiac event probability percentage to one or more cardiac data samples of a set of cardiac data. In some examples, processing circuitry of medical system 2 may apply a model, such as a machine learning model, do determine and assign a cardiac event probability percentage to one or more cardiac data samples. In some examples, the assigned cardiac event probability percentage may indicate the percentage chance that a respective cardiac data sample is associated with a particular cardiac event.

[0039] In some examples, processing circuitry of medical system may receive, such as from a user via a user interface, a selection of a cardiac event probability threshold corresponding to the cardiac event. In some examples, external device 12 may include the user interface. In some examples, processing circuitry of medical system 2 may cause the user interface to indicate each cardiac data sample of the plurality of data samples of a set of cardiac data that is associated with a cardiac event probability that is greater than the cardiac event probability threshold. For example, processing circuitry of medical system 2 may determine that a first cardiac data sample has a first cardiac event probability of being associated with the cardiac event, such as 5%, and may determine a second cardiac data sample has a second cardiac event probability of being associated with the cardiac event, such as 92%. In response to receiving a user selection of a cardiac event probability threshold, such as 85%, processing circuitry of medical system 2 may cause the user interface to indicate the second cardiac data sample, such as by highlighting, coloring, etc., satisfies the cardiac event probability threshold of being associated with the cardiac event. [0040] Although described in the context of examples in which IMD 10 that senses the cardiac data comprises an insertable cardiac monitor, example systems including one or more implantable or external devices of any type configured to sense cardiac data may be configured to implement the techniques of this disclosure.

[0041] FIG. 2 is a functional block diagram illustrating an example configuration of IMD 10 of FIG. 1 in accordance with one or more techniques described herein. In the illustrated example, IMD 10 includes electrodes 16A and 16B (collectively “electrodes 16”), antenna 26, processing circuitry 50, sensing circuitry 52, communication circuitry 54, storage device 56, switching circuitry 58, and sensors 62. Although the illustrated example includes two electrodes 16, IMDs including or coupled to more than two electrodes 16 may implement the techniques of this disclosure in some examples. [0042] Processing circuitry 50 may include fixed function circuitry and/or programmable processing circuitry. Processing circuitry 50 may include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or analog logic circuitry. In some examples, processing circuitry 50 may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as other discrete or integrated logic circuitry. The functions attributed to processing circuitry 50 herein may be embodied as software, firmware, hardware or any combination thereof. [0043] Sensing circuitry 52 may be selectively coupled to electrodes 16 via switching circuitry 58, e.g., to select the electrodes 16 and polarity, referred to as the sensing vector, used to sense an ECG signal, as controlled by processing circuitry 50. Sensing circuitry 52 may sense signals from electrodes 16, e.g., to produce an ECG signal, in order to facilitate monitoring the electrical activity of the heart. Sensing circuitry 52 may include components/modules for converting the raw ECG signal to a processed ECG signal that can be analyzed to detect sense events. Sensing circuitry 52 also may monitor signals from sensors 62, such as heart sound sensor(s). In some examples, sensors 62 may be configured to sense cardiac vibrations. In some examples, sensors 62 may include one or more accelerometers. In some examples, sensors 62 may additionally or alternatively include one or more microphones and/or other vibration/motion sensors. Sensing circuitry 52 may receive raw cardiac vibrations monitored by sensors 62. Sensing circuitry 52 may include components/modules for converting the raw cardiac vibrations to a processed heart sound beat signal that can be analyzed to detect sense events. In some examples, cardiac data may include one or more signals received from sensors 62, such as a heart sound beat signal, and/or sensed cardiac ECG signals. In some examples, sensing circuitry 52 may include one or more filters and amplifiers for filtering and amplifying signals received from electrodes 16 and/or sensors 62.

[0044] Sensing circuitry 52 and/or processing circuitry 50 may be configured to detect cardiac depolarizations (e.g., P-waves of atrial depolarizations or R-waves of ventricular depolarizations) when the ECG signal amplitude crosses a sensing threshold. An ECG signal may include P-waves (depolarization of the atria), R-waves (depolarization of the ventricles), and T-waves (repolarization of the ventricles), among other events. Sensing circuitry 52 and/or processing circuitry 50 may be configured to detect one or more features of the P-waves, R-waves, and/or T-waves in an ECG signal. For cardiac depolarization detection, sensing circuitry 52 may include a rectifier, filter, amplifier, comparator, and/or analog-to-digital converter, in some examples. In some examples, sensing circuitry 52 may output an indication to processing circuitry 50 in response to sensing of a cardiac depolarization. In this manner, processing circuitry 50 may receive detected cardiac depolarization indicators corresponding to the occurrence of detected R-waves and P-waves in the respective chambers of heart. Processing circuitry 50 may use the indications of detected R-waves and P-waves for determining inter-depolarization intervals, heart rate, and detecting cardiac events. In some examples, cardiac events may include arrhythmias, such tachyarrhythmias and asystole.

[0045] Sensing circuitry 52 may also provide one or more digitized ECG signals and/or heart sound beat signals to processing circuitry 50 for analysis. Sensing circuitry 52 may include one or more detection channels, each of which may include an amplifier. The detection channels may be used to sense cardiac data, such as an ECG signal. Some detection channels may detect events, such as R-waves, P-waves, and T-waves and provide indications of the occurrences of such events to processing circuitry 50. One or more other detection channels may provide the signals to an analog-to-digital converter, for conversion into a digital signal for processing or analysis by processing circuitry 50.

[0046] In some examples, processing circuitry 50 may store a plurality of sets of the cardiac data, such as digitized ECG signal and/or heart sound beat signal in storage device 56. Processing circuitry 50 of IMD 10, and/or processing circuitry of another device that retrieves data from IMD 10, may analyze the cardiac data to determine and assign a cardiac event probability percentage to one or more cardiac data samples of a set of the cardiac data that a respective cardiac data sample is associated with the cardiac event. In some examples, processing circuitry 50 of IMD 10, and/or processing circuitry of another device that retrieves data from IMD 10, may analyze the cardiac data to determine an acute cardiac event occurred.

[0047] Communication circuitry 54 may include any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as external device 12, another networked computing device, or another IMD or sensor. Under the control of processing circuitry 50, communication circuitry 54 may receive downlink telemetry from, as well as send uplink telemetry to external device 12 or another device with the aid of an internal or external antenna, e.g., antenna 26. In addition, processing circuitry 50 may communicate with a networked computing device via an external device (e.g., external device 12) and a computer network, such as the Medtronic CareLink™ Network. Antenna 26 and communication circuitry 54 may be configured to transmit and/or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth, WiFi, or other proprietary or non-proprietary wireless communication schemes.

[0048] In some examples, storage device 56 includes computer-readable instructions that, when executed by processing circuitry 50, cause IMD 10 and processing circuitry 50 to perform various functions attributed to IMD 10 and processing circuitry 50 herein. Storage device 56 may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital media. Storage device 56 may store, as examples, programmed values for one or more operational parameters of IMD 10 and/or data collected by IMD 10 for transmission to another device using communication circuitry 54. Data stored by storage device 56 and transmitted by communication circuitry 54 to one or more other devices may include one or more sets of cardiac data, digitized ECG signals, and/or digitized heart sound beat signals, as examples.

[0049] FIG. 3 is a conceptual side-view diagram illustrating an example configuration of IMD 10 of FIGS. 1 and 2. In the example shown in FIG. 3, IMD 10 may include a leadless, subcutaneously-implantable monitoring device having a housing 15 and an insulative cover 76. Electrode 16A and electrode 16B may be formed or placed on an outer surface of cover 76. Circuitries 50-62, described above with respect to FIG. 2, may be formed or placed on an inner surface of cover 76, or within housing 15. In the illustrated example, antenna 26 is formed or placed on the inner surface of cover 76, but may be formed or placed on the outer surface in some examples. In some examples, one or more of sensors 62 may be formed or placed on the outer surface of cover 76. In some examples, insulative cover 76 may be positioned over an open housing 15 such that housing 15 and cover 76 enclose antenna 26 and circuitries 50-62, and protect the antenna and circuitries from fluids such as body fluids. [0050] One or more of antenna 26 or circuitries 50-62 may be formed on the inner side of insulative cover 76, such as by using flip-chip technology. Insulative cover 76 may be flipped onto a housing 15. When flipped and placed onto housing 15, the components of IMD 10 formed on the inner side of insulative cover 76 may be positioned in a gap 78 defined by housing 15. Electrodes 16 may be electrically connected to switching circuitry 58 through one or more vias (not shown) formed through insulative cover 76. Insulative cover 76 may be formed of sapphire (i.e., corundum), glass, parylene, and/or any other suitable insulating material. Housing 15 may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes 16 may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes 16 may be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.

[0051] FIG. 4A is a conceptual drawing illustrating an IMD 10A, which may be an example configuration of IMD 10 of FIGS. 1-3 as an ICM. In the example shown in FIG. 4A, IMD 10A may be embodied as a monitoring device having housing 15, proximal electrode 16A and distal electrode 16B. Housing 15 may further comprise first major surface 14, second major surface 18, proximal end 20, and distal end 22. Housing 15 encloses electronic circuitry located inside the IMD 10A and protects the circuitry contained therein from body fluids. Electrical feedthroughs provide electrical connection of electrodes 16A and 16B.

[0052] In the example shown in FIG. 4A, IMD 10A is defined by a length /., a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D. In one example, the geometry of the IMD 10A - in particular a width W greater than the depth D - is selected to allow IMD 10A to be inserted under the skin of the patient using a minimally invasive procedure and to remain in the desired orientation during and after insertion. For example, the device shown in FIG. 4 A includes radial asymmetries (notably, the rectangular shape) along the longitudinal axis that maintains the device in the proper orientation following insertion. For example, the spacing between proximal electrode 64 and distal electrode 66 may range from 30 millimeters (mm) to 55mm, 35mm to 55mm, and from 40mm to 55mm and may be any range or individual spacing from 25mm to 60mm. In addition, IMD 10A may have a length L that ranges from 30mm to about 70mm. In other examples, the length L may range from 5mm to 60mm, 15mm to 50mm, 40mm to 60mm, 45mm to 60mm and may be any length or range of lengths between about 5mm and about 80mm. In addition, the width W of major surface 14 may range from 5mm to 15mm, 3mm to 10mm, and may be any single or range of widths between 3mm and 15mm. The thickness of depth D of IMD 10A may range from 2mm to 9mm. In other examples, the depth D of IMD 10A may range from 2mm to 5mm, may range from 5mm to 15mm, and may be any single or range of depths from 2mm to 15mm. In addition, IMD 10A according to an example of the present disclosure is has a geometry and size designed for ease of implant and patient comfort. Examples of IMD 10A described in this disclosure may have a volume of three cubic centimeters (cm) or less, 1.5 cubic cm or less or any volume between three and 1.5 cubic cm.

[0053] In the example shown in FIG. 4A, once inserted within the patient, the first major surface 14 faces outward, toward the skin of the patient while the second major surface 18 is located opposite the first major surface 14. In addition, in the example shown in FIG. 4 A, proximal end 20 and distal end 22 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient. IMD 10A, including instrument and method for inserting IMD 10 is described, for example, in U.S. Patent Publication No. 2014/0276928, incorporated herein by reference in its entirety.

[0054] Proximal electrode 16A and distal electrode 16B are used to sense cardiac data, e.g. ECG signals, intra-thoracically or extra-thoracically, which may be sub-muscularly or subcutaneously. Cardiac data, such as ECG signals, may be stored in a memory of IMD 10 A, and data may be transmitted via integrated antenna 30A to another medical device, which may be another implantable device or an external device, such as external device 12. In some example, electrodes 16A and 16B may additionally or alternatively be used for sensing any bio-potential signal of interest, which may be, for example, an ECG, EGM, EEG, EMG, or a nerve signal, from any implanted location.

[0055] In the example shown in FIG. 4A, proximal electrode 16A is in close proximity to the proximal end 20 and distal electrode 16B is in close proximity to distal end 22. In this example, distal electrode 16B is not limited to a flattened, outward facing surface, but may extend from first major surface 14 around rounded edges 24 and/or end surface 25 and onto the second major surface 18 so that the electrode 16B has a three-dimensional curved configuration. In some examples, electrode 16B is an uninsulated portion of a metallic, e.g., titanium, part of housing 15.

[0056] In the example shown in FIG. 4A, proximal electrode 16A is located on first major surface 14 and is substantially flat, and outward facing. However, in other examples proximal electrode 16A may utilize the three dimensional curved configuration of distal electrode 16B, providing a three dimensional proximal electrode (not shown in this example). Similarly, in other examples distal electrode 16B may utilize a substantially flat, outward facing electrode located on first major surface 14 similar to that shown with respect to proximal electrode 16 A.

[0057] The various electrode configurations allow for configurations in which proximal electrode 16A and distal electrode 16B are located on both first major surface 14 and second major surface 18. In other configurations, such as that shown in FIG. 4 A, only one of proximal electrode 16A and distal electrode 16B is located on both major surfaces 14 and 18, and in still other configurations both proximal electrode 16A and distal electrode 16B are located on one of the first major surface 14 or the second major surface 18 (e.g., proximal electrode 16A located on first major surface 14 while distal electrode 16B is located on second major surface 18). In another example, IMD 10A may include electrodes on both major surface 14 and 18 at or near the proximal and distal ends of the device, such that a total of four electrodes are included on IMD 10 A. Electrodes 16A and 16B may be formed of a plurality of different types of biocompatible conductive material, e.g. stainless steel, titanium, platinum, iridium, or alloys thereof, and may utilize one or more coatings such as titanium nitride or fractal titanium nitride.

[0058] In the example shown in FIG. 4A, proximal end 20 includes a header assembly 28 that includes one or more of proximal electrode 16 A, integrated antenna 30 A, antimigration projections 32, and/or suture hole 34. Integrated antenna 30A is located on the same major surface (i.e., first major surface 14) as proximal electrode 16A and is also included as part of header assembly 28. Integrated antenna 30A allows IMD 10A to transmit and/or receive data. In other examples, integrated antenna 30A may be formed on the opposite major surface as proximal electrode 16 A, or may be incorporated within the housing 15 of IMD 10 A. In the example shown in FIG. 4 A, anti-migration projections 32 are located adjacent to integrated antenna 30A and protrude away from first major surface 14 to prevent longitudinal movement of the device. In the example shown in FIG. 4A, anti -migration projections 32 include a plurality (e.g., nine) small bumps or protrusions extending away from first major surface 14. As discussed above, in other examples anti-migration projections 32 may be located on the opposite major surface as proximal electrode 16A and/or integrated antenna 30A. In addition, in the example shown in FIG. 4A, header assembly 28 includes suture hole 34, which provides another means of securing IMD 10A to the patient to prevent movement following insertion. In the example shown, suture hole 34 is located adjacent to proximal electrode 16A. In one example, header assembly 28 is a molded header assembly made from a polymeric or plastic material, which may be integrated or separable from the main portion of IMD 10 A.

[0059] FIG. 4B is a perspective drawing illustrating another IMD 10B, which may be another example configuration of IMD 10 from FIGS. 1-3. IMD 10B of FIG. 4B may be configured substantially similarly to IMD 10A of FIG. 4A, with differences between them discussed herein.

[0060] IMD 10B may include a leadless, subcutaneously-implantable monitoring device, e.g. an ICM. IMD 10B includes housing having a base 40 and an insulative cover 42. Proximal electrode 16C and distal electrode 16D may be formed or placed on an outer surface of cover 42. Various circuitries and components of IMD 10B, e.g., described below with respect to FIG. 3, may be formed or placed on an inner surface of cover 42, or within base 40. In some examples, a battery or other power source of IMD 10B may be included within base 40. In the illustrated example, antenna 30B is formed or placed on the outer surface of cover 42, but may be formed or placed on the inner surface in some examples. In some examples, insulative cover 42 may be positioned over an open base 40 such that base 40 and cover 42 enclose the circuitries and other components and protect them from fluids such as body fluids.

[0061] Circuitries and components may be formed on the inner side of insulative cover 42, such as by using flip-chip technology. Insulative cover 42 may be flipped onto a base 40. When flipped and placed onto base 40, the components of IMD 10B formed on the inner side of insulative cover 42 may be positioned in a gap 44 defined by base 40. Electrodes 16C and 16D and antenna 30B may be electrically connected to circuitry formed on the inner side of insulative cover 42 through one or more vias (not shown) formed through insulative cover 42. Insulative cover 42 may be formed of sapphire (i.e., corundum), glass, parylene, and/or any other suitable insulating material. Base 40 may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes 16C and 16D may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes 16C and 16D may be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.

[0062] In the example shown in FIG. 4B, the housing of IMD 10B defines a length /., a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D, similar to IMD 10A of FIG. 4B. For example, the spacing between proximal electrode 64 and distal electrode 66 may range from 30mm to 50mm, from 35mm to 45mm, or be approximately 40mm. In addition, IMD 10B may have a length L that ranges from 30mm to about 70mm. In other examples, the length L may range from 5mm to 60mm, 40mm to 60mm, 45mm to 55mm, or be approximately 45mm. In addition, the width may range from 3mm to 15mm, such as approximately 8mm. The thickness of depth D of IMD 10B may range from 2mm to 15mm, from 3 to 5mm, or be approximately 4mm. IMD 10B may have a volume of three cubic centimeters (cm) or less, or 1.5 cubic cm or less, such as approximately 1.4 cubic cm.

[0063] In the example shown in FIG. 4B, once inserted subcutaneously within the patient, outer surface of cover 42 faces outward, toward the skin of the patient. In addition, as shown in FIG. 4B, proximal end 46 and distal end 48 are rounded to reduce discomfort and irritation to surrounding tissue once inserted.

[0064] FIG. 5 is a block diagram illustrating an example configuration of components of external device 12. In the example of FIG. 5, external device 12 includes processing circuitry 80, communication circuitry 82, storage device 84, and user interface 86.

[0065] Processing circuitry 80 may include one or more processors that are configured to implement functionality and/or process instructions for execution within external device 12. For example, processing circuitry 80 may be capable of processing instructions stored in storage device 84. Processing circuitry 80 may include, for example, microprocessors, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Accordingly, processing circuitry 80 may include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions ascribed herein to processing circuitry 80. [0066] Communication circuitry 82 may include any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as IMD 10. Under the control of processing circuitry 80, communication circuitry 82 may receive downlink telemetry from, as well as send uplink telemetry to, IMD 10, or another device. Communication circuitry 82 may be configured to transmit or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth, WiFi, or other proprietary or non-proprietary wireless communication schemes. Communication circuitry 82 may also be configured to communicate with devices other than IMD 10 via any of a variety of forms of wired and/or wireless communication and/or network protocols.

[0067] Storage device 84 may be configured to store information within external device 12 during operation. Storage device 84 may include a computer-readable storage medium or computer-readable storage device. In some examples, storage device 84 includes one or more of a short-term memory or a long-term memory. Storage device 84 may include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. In some examples, storage device 84 is used to store data indicative of instructions for execution by processing circuitry 80. Storage device 84 may be used by software or applications running on external device 12 to temporarily store information during program execution.

[0068] Data exchanged between external device 12 and IMD 10 may include cardiac data and/or operational parameters. External device 12 may transmit data including computer readable instructions which, when implemented by IMD 10, may control IMD 10 to change one or more operational parameters and/or export collected data. For example, processing circuitry 80 may transmit an instruction to IMD 10 which requests IMD 10 to export one or more sets of collected cardiac data (e.g., digitized ECG signals, and/or digitized heart sound beat signals) to external device 12. In turn, external device 12 may receive the collected one or more sets of cardiac data from IMD 10 and store the collected one or more sets of cardiac data in storage device 84. Processing circuitry 80 may implement any of the techniques described herein to cause a user interface to display a set of cardiac data of the plurality of sets of cardiac data, wherein the set of cardiac data includes a plurality of cardiac data samples, and wherein each cardiac data sample of the plurality of cardiac data samples is associated with a cardiac event probability that the cardiac data sample is associated with a cardiac event, receive, from the user interface, a user selection of a cardiac event probability threshold corresponding to the cardiac event, and cause the user interface to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the cardiac event probability threshold.

[0069] A user, such as a clinician or patient 4, may interact with external device 12 through user interface 86. User interface 86 includes a display (not shown), such as a liquid crystal display (LCD) or a light emitting diode (LED) display or other type of screen, with which processing circuitry 80 may present information related to IMD 10, e.g., cardiac data, ECG signals, ECG signal features, heart sound beat signals, heart sound features, and/or indication(s) that a cardiac data sample is associated with a cardiac event probability that is greater than a selected cardiac event probability threshold corresponding to a cardiac event. In addition, user interface 86 may include an input mechanism to receive input from the user. The input mechanisms may include, for example, any one or more of buttons, a keypad (e.g., an alphanumeric keypad), a peripheral pointing device, a touch screen, or another input mechanism that allows the user to navigate through user interfaces presented by processing circuitry 80 of external device 12 and provide input. In other examples, user interface 86 also includes audio circuitry for providing audible notifications, instructions or other sounds to the user, receiving voice commands from the user, or both.

[0070] FIG. 6 is a block diagram illustrating an example system that includes an access point 90, a network 92, external computing devices, such as a server 94, and one or more other computing devices 100A-100N (collectively, “computing devices 100”), which may be coupled to IMD 10 and external device 12 via network 92, in accordance with one or more techniques described herein. In this example, IMD 10 may use communication circuitry 54 to communicate with external device 12 via a first wireless connection, and to communicate with an access point 90 via a second wireless connection. In the example of FIG. 6, access point 90, external device 12, server 94, and computing devices 100 are interconnected and may communicate with each other through network 92.

[0071] Access point 90 may include a device that connects to network 92 via any of a variety of connections, such as telephone dial-up, digital subscriber line (DSL), or cable modem connections. In other examples, access point 90 may be coupled to network 92 through different forms of connections, including wired or wireless connections. In some examples, access point 90 may be a user device, such as a tablet or smartphone, that may be co-located with the patient. IMD 10 may be configured to transmit cardiac data, such as digitized ECG signals, digitized heart sound signals, to access point 90. Access point 90 may then communicate the retrieved cardiac data to server 94 via network 92.

[0072] In some cases, server 94 may be configured to provide a secure storage site for data that has been collected from IMD 10 and/or external device 12. In some cases, server 94 may assemble data in web pages or other documents for viewing by trained professionals, such as clinicians, via computing devices 100. One or more aspects of the illustrated system of FIG. 6 may be implemented with general network technology and functionality, which may be similar to that provided by the Medtronic CareLink™ Network. In some examples, server 94 may communicate with computing device 100 via network 92. For example, server 94 may communicate an analysis of data, such as determination of a cardiac event probability percentage a cardiac data sample is associated with a cardiac event, to computing device 100, external device 12, or any other computing device via network 92. For example, server 94 may communicate the cardiac event probability percentage a cardiac data sample is associated with a cardiac event to computing device 100, external device 12, or any other computing device via network 92. For example, server 94 may communicate a determination that an acute cardiac event, such as an arrythmia or PVC, occurred during a set of cardiac data to computing device 100, external device 12, or any other computing device via network 92.

[0073] In some examples, one or more of computing devices 100 may be a tablet or other smart device located with a clinician, by which the clinician may program, receive alerts from, and/or interrogate IMD 10. For example, the clinician may access data collected by IMD 10 through a computing device 100, such as when patient 4 is in in between clinician visits, to check on a status of a medical condition. In some examples, the clinician may enter instructions for a medical intervention for patient 4 into an application executed by computing device 100, such as based on a status of a patient condition determined by IMD 10, external device 12, server 94, or any combination thereof, or based on other patient data known to the clinician. Device 100 then may transmit the instructions for medical intervention to another of computing devices 100 located with patient 4 or a caregiver of patient 4. For example, such instructions for medical intervention may include an instruction to change a drug dosage, timing, or selection, to schedule a visit with the clinician, or to seek medical attention. In further examples, a computing device 100 may generate an alert to patient 4 based on a status of a medical condition of patient 4, which may enable patient 4 proactively to seek medical attention prior to receiving instructions for a medical intervention. In this manner, patient 4 may be empowered to take action, as needed, to address his or her medical status, which may help improve clinical outcomes for patient 4.

[0074] In the example illustrated by FIG. 6, server 94 includes a storage device 96, e.g., to store data retrieved from IMD 10, and processing circuitry 98. In some examples, storage device 96 may be a memory. Although not illustrated in FIG. 6 computing devices 100 may similarly include a storage device and processing circuitry. Processing circuitry 98 may include one or more processors that are configured to implement functionality and/or process instructions for execution within server 94. For example, processing circuitry 98 may be capable of processing instructions stored in storage 96. Processing circuitry 98 may include or be coupled to communication circuitry that may include any suitable hardware, firmware, software or any combination thereof for communicating with another device. In some examples, a description of processing circuitry 98 outputting a signal, such as a classification, may include processing circuitry 98 causing communication circuitry of server 94 to output the signal. Processing circuitry 98 may include, for example, microprocessors, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Accordingly, processing circuitry 98 may include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions ascribed herein to processing circuitry 98. Processing circuitry 98 of server 94 and/or the processing circuity of computing devices 100 may implement any of the techniques described herein to cause a user interface to display a set of cardiac data of the plurality of sets of cardiac data, wherein the set of cardiac data includes a plurality of cardiac data samples, and wherein each cardiac data sample of the plurality of cardiac data samples is associated with a cardiac event probability that the cardiac data sample is associated with a cardiac event, receive, from the user interface, a user selection of a cardiac event probability threshold corresponding to the cardiac event, and cause the user interface to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the cardiac event probability threshold.

[0075] Storage device 96 may include a computer-readable storage medium or computer-readable storage device. In some examples, storage device 96 includes one or more of a short-term memory or a long-term memory. Storage device 96 may include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. In some examples, storage device 96 is used to store data indicative of instructions for execution by processing circuitry 98. In some examples, storage device 96 may store a plurality of sets of cardiac data associated with patient 4. [0076] Although the techniques for causing a user interface to display a set of cardiac data of the plurality of sets of cardiac data, wherein the set of cardiac data includes a plurality of cardiac data samples, and wherein each cardiac data sample of the plurality of cardiac data samples is associated with a cardiac event probability that the cardiac data sample is associated with a cardiac event, receiving, from the user interface, a user selection of a cardiac event probability threshold corresponding to the cardiac event, and causing the user interface to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the cardiac event probability threshold, are described herein primarily (e.g., with respect to FIGS. 7- 9) as being performed by processing circuitry 80 of external device 12, such techniques may be performed, in whole or part, by processing circuitry of any one or more devices of system 2, processing circuitry 98 of server 94, processing circuitry of one or more computing devices 100, or processing circuitry 50 of IMD 10.

[0077] FIGS. 7A-7B show examples of a user interface 186 displaying a set of cardiac data 130 of a plurality of sets of cardiac data 105. The set of cardiac data 130 includes a plurality of cardiac data samples, such as cardiac data samples 131, 132. The display 110 may include an indication of a particular cardiac event. In some examples, processing circuitry 80 may receive, such as via user interface 186, a user selection of one or more particular cardiac events. For example, in FIGS. 7A-7B, the particular cardiac event is NSVT. The user interface 186 may display an indication of a cardiac event probability threshold 120 corresponding to a particular cardiac event 125, such as NSVT. For example, the cardiac event probability threshold 120 in FIG. 7A is set at 0.39, while the cardiac event probability threshold 120 in FIG. 7B is set at 0.49. In some examples, a cardiac event probability threshold 120 corresponding to the cardiac event 125 may be selected and/or adjusted by a user. In some examples, the cardiac event probability threshold 120 may range between 0 and 1.0.

[0078] In some examples, processing circuitry 80 may receive from the user interface 186, a user selection of a cardiac event probability threshold 120 corresponding to the cardiac event 125. In some examples, processing circuitry 80 may cause user interface 186 to indicate a cardiac data sample 131 associated with a cardiac event probability that is greater that the cardiac event probability threshold 120. For example, in FIGS. 7A-7B cardiac data samples 131 are indicated as being associated with a cardiac event probability that is greater than the cardiac event probability threshold 120, such as by differentiating a color, shape, highlight, form, etc. of the cardiac data samples 131 associated with a cardiac event probability greater than the cardiac event probability threshold 120 from the cardiac data samples 132 associated with a cardiac event probability less than or equal to the cardiac event probability threshold 120.

[0079] For example, in FIG. 7A, the cardiac event probability threshold 120 is set at 0.39, while in FIG. 7B, the cardiac event probability threshold 120 is raised to 0.49. As an example, in response to the raising of the cardiac event probability threshold 120 from 0.39 to 0.49, the size of cardiac data samples 131 associated with a cardiac event probability that is greater than the cardiac event probability threshold 120 decreases, while the size of cardiac data samples 132 associated with a cardiac event probability that is less than or equal to the cardiac event probability threshold 120 increased.

[0080] In some examples, processing circuitry 80 may determine and assign a cardiac event probability percentage to one or more cardiac data samples, such as 131, 132, of a set of cardiac data 130. In some examples, processing circuitry 80 may apply a model, such as a machine learning model, to determine and assign a cardiac event probability percentage to one or more cardiac data samples. In some examples, the assigned cardiac event probability percentage may indicate the percentage chance that a respective cardiac data sample is associated with a particular cardiac event.

[0081] In some examples, processing circuitry 80 may receive, such as from a user via a user interface 186, a selection of a cardiac event probability threshold 120 corresponding to the cardiac event 125. In some examples, processing circuitry 80 may cause the user interface 186 to indicate each cardiac data sample, such as cardiac data samples 131, of the plurality of cardiac data samples of a set of cardiac data 130 that is associated with a cardiac event probability that is greater than the cardiac event probability threshold 120. For example, processing circuitry 80 may determine that a first cardiac data sample, such as cardiac data sample 132, has a first cardiac event probability of being associated with the cardiac event, such as between 5%, and may determine a second cardiac data sample, such as cardiac data sample 131, has a second cardiac event probability of being associated with the cardiac event, such as 60%. In response to receiving a user selection of a cardiac event probability threshold, such as 0.49 (e.g., 49%), processing circuitry 80 may cause the user interface 186 to indicate the second cardiac data sample, such as cardiac data sample 131, such as by highlighting, coloring, etc, satisfies the cardiac event probability threshold 120 of being associated with the cardiac event 125.

[0082] FIG. 7C shows an example of processing circuitry 80 indicating cardiac data samples 131 as being associated with a cardiac event probability that is greater than the cardiac event probability threshold 120. In some examples, processing circuitry 80 may indicate cardiac data samples 132 associated with a cardiac event probability less than or equal to the cardiac event probability threshold 120 differently than cardiac data samples 131 associated with a cardiac event probability that is greater than the cardiac event probability threshold 120.

[0083] FIG. 7D shows an example of a user interface 186 displaying a selection of one or more particular cardiac events 125 from a plurality of cardiac events 126. The examples of the plurality of cardiac events 126 shown in FIG. 7D as examples and the cardiac events are not limited, in any fashion, to example cardiac events shown in FIG. 7D. In some examples, a user may select one or more particular cardiac events 125 from a plurality of cardiac events 126 via a user interface 186. In some examples, processing circuitry 80 may receive a selected one or more particular cardiac events 125 from a plurality of cardiac events 126 via a user interface 186.

[0084] Processing circuitry 80 causing the user interface 186 to indicate each cardiac data sample, such as cardiac data sample 131, of the plurality of cardiac data samples of the cardiac data set 130, associated with a cardiac event probability that is greater than a selected cardiac event probability threshold 120 corresponding to a particular cardiac event 125 may more efficiently and more quickly cause a user interface 186 to display identified cardiac data samples of cardiac data that correspond to cardiac events with user selected specificity and sensitivity. In some examples, setting cardiac event probability thresholds on a patient-by-patient basis may generate an improved display on a user interface that may reduce an amount of time that a clinician has to spend identifying relevant cardiac events in cardiac data and/or may help a clinician evaluate data corresponding to the patient to track one or more patient conditions.

[0085] FIG. 8 is a flow diagram illustrating an example technique for operation of a medical system 2 to cause a user interface to indicate each cardiac data sample associated with a cardiac event probability that is greater than a selected cardiac event probability threshold. In some examples, a memory, such as storage device 84 and/or storage device 96, may be configured to store a plurality of sets of cardiac data. In some examples, each set of the stored plurality of sets of cardiac data may be associated with a particular patient 4. In some examples, the cardiac data comprises an ECG signal. As indicated by FIG. 8, processing circuitry 80 may determine, for each of a plurality of cardiac data samples of a set of cardiac data 130, such as cardiac data samples 131, 132, a cardiac event probability that a respective cardiac data sample is associated with a cardiac event 125 (800). In some examples, a cardiac event 125 may include a particular arrhythmia. In some examples, a cardiac event 125 may indicate a respective cardiac data sample comprises at least part of a particular feature of an ECG signal, such as a P-wave, a beginning of a P-wave, an end of a P-wave, a T-wave, a beginning of a T-wave, an end of a T-wave. In some examples, a cardiac event 125 may include PVC. Processing circuitry 80 may cause a user interface 186 to display a set of cardiac data 130 of a plurality of sets of cardiac data (810). In some examples, the set of cardiac data 130 may include a plurality of cardiac data samples, such as cardiac data samples 131, 132. Processing circuitry 80 may receive a user selection of a cardiac event probability threshold 120 corresponding to a cardiac event 125 (820).

Processing circuitry 80 may cause the user interface 186 to indicate each cardiac data sample (e.g., cardiac data sample 131 in FIGS. 7A-7B) of a plurality of cardiac data samples associated with a cardiac event probability that is greater than the cardiac event probability threshold (830).

[0086] In some examples, processing circuitry 80 may receive a second user selection of a second cardiac event probability threshold corresponding to the cardiac event 125. The cardiac event second probability threshold being different an initial cardiac event probability threshold receives from an initial user selection. In some examples, in response to receipt of the cardiac event second probability threshold, processing circuitry 80 may cause the user interface 186 to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the second cardiac event probability threshold.

[0087] In some examples, processing circuitry 80 may utilize machine learning, such as a deep learning algorithm or model (e.g., a neural network or deep belief network), to determine a cardiac event probability that a cardiac data sample is associated with a particular cardiac event. Processing circuitry 80 may be configured to execute an artificial intelligence (Al) engine that operates according to one or more models, such as machine learning models. Machine learning models may include any number of different types of machine learning models, such as neural networks, deep neural networks, convolution neural networks, recurrent neural networks, such as long short term memory networks, dense neural networks, and the like. In some examples, various feature inputs to the Al engine may be fed as direct inputs to different layers in a network and not necessarily prior to the convolution layers. Although described with respect to machine learning models, the techniques described in this disclosure are also applicable to other types of Al models, including rule-based models, finite state machines, and the like. For example, the techniques described in this disclosure are also applicable to Bayesian Belief Networks (BBN) or Bayesian machine learning models (these sometimes referred to as Bayesian Networks or Bayesian frameworks herein), Markov random fields, graphical models, Al models (e.g., Naive Bayes classifiers or deep learning models), and/or other belief networks, such as sigmoid belief networks, deep belief networks (DBNs), etc. In other examples, the disclosed technology may leverage non-Bayesian prediction or probability modeling, such as frequentist inference modeling or other statistical models.

[0088] Machine learning may generally enable a computing device to analyze input data and identify an action to be performed responsive to the input data. Each machine learning model may be trained using training data that reflects likely input data. The training data may be labeled or unlabeled (meaning that the correct action to be taken based on a sample of training data is explicitly stated or not explicitly stated, respectively). [0089] The training of the machine learning model may be guided (in that a designer, such as a computer programmer, may direct the training to guide the machine learning model to identify the correct action in view of the input data) or unguided (in that the machine learning model is not guided by a designer to identify the correct action in view of the input data). In some instances, the machine learning model is trained through a combination of labeled and unlabeled training data, a combination of guided and unguided training, or possibly combinations thereof. Examples of machine learning include nearest neighbor, naive Bayes, decision trees, linear regression, support vector machines, neural networks, k-Means clustering, Q-leaming, temporal difference, deep adversarial networks, evolutionary algorithms or other supervised, unsupervised, semi-supervised, or reinforcement learning algorithms to train one or more models.

[0090] Processing circuitry 80 may utilize machine learning, such as a deep learning algorithm or model (e.g., a neural network or deep belief network), to determine a cardiac event probability (e.g., cardiac event probability score) of the cardiac data sample being associated with a particular cardiac event based on extracted cardiac data samples. In some examples, processing circuitry 80 may utilize neural network architectures, such as convolutional u-nets and/or transformer (attention) based models, to determine a cardiac event probability (e.g., cardiac event probability score) of the cardiac data sample being associated with a particular cardiac event based on extracted cardiac data samples. In some examples, neural network architectures, such as convolutional u-nets and transformer (attention) based models, may be trained via backpropagation. In some examples, neural network architectures may include single or multichannel inputs and/or single or multichannel outputs. In some examples, additional metadata corresponding to patient 4, such as IMD markers, may be input to a deep learning algorithm or model to determine a cardiac event probability. Processing circuitry 80 may train a deep learning model to represent a relationship of the extracted cardiac data samples to a cardiac event probability score of the cardiac data sample being associated with a particular cardiac event. For example, processing circuitry 80 may train the deep learning model using cardiac data, such as cardiac data samples, from other patients. In some examples, processing circuitry 80 may train the deep leaning model by adjusting the weights of a hidden layer of a neural network model to balance the contribution of each input (e.g., characteristics of the cardiac sound features) according to determining a cardiac event probability score. Once the deep learning model is trained, processing circuitry 80 may obtain and apply data, such as the extracted plurality of sets of cardiac data, to the trained deep learning model. [0091] Further examples of patient parameters and artificial intelligence techniques that may be used to determine cardiac event probability with the cardiac data described herein are described in commonly assigned U.S. Patent Nos. 11,776,691 and 11,355,244, the entire contents of each of which are incorporated herein by reference.

[0092] FIG. 9 is an example of a machine learning model 902 and/or machine learning model 1100 being trained using supervised and/or reinforcement learning techniques, such as machine learning model 1100. Machine learning model 902 may correspond to any machine learning model described herein. The machine learning model 902 may be implemented using any number of models for supervised and/or reinforcement learning, such as but not limited to, an artificial neural network, a decision tree, naive Bayes network, support vector machine, or k-nearest neighbor model, to name only a few examples. In some examples, one or more of IMD 10, external device 12, server 94, and/or computing device(s) 100 initially trains the machine learning model 902 based on a training set of metrics and corresponding to a particular cardiac event. The training set 900 may include a set of feature vectors, where each feature in the feature vector represents a value for a particular metric. One or more of IMD 10, external device 12, server 94, and/or computing device(s) 100 may select a training set comprising a set of training instances, each training instance comprising an association between one or more cardiac data samples and a particular cardiac event. A prediction or classification by the machine learning model 902 may be compared 904 to the target output 903, and an error signal and/or machine learning model weights modification may sent/applied to the machine learning model 902 based on the comparison to learn/train 905 the machine learning model to modify/update the machine learning model 902. For example, one or more of IMD 10, external device 12, server 94, and/or computing device(s) 100 may, for each training instance in the training set, modify, based on the respective cardiac data samples, and/or particular cardiac event of the training instance, the machine learning model 902 to change a score generated by the machine learning model 902 in response to subsequent cardiac data samples applied to the machine learning model 902.

[0093] FIG. 10 is a conceptual diagram illustrating an example machine learning model 1100 configured to generate one or more values indicative of a cardiac event probability score a cardiac data sample is associated with a particular cardiac event, e.g., a particular arrhythmia, PVC, the cardiac data sample comprises at least part of a particular feature of the ECG signal, etc. Machine learning model 1100 is an example of a deep learning model, or deep learning algorithm. One or more of IMD 10, external device 12, or server 94 may train, store, and/or utilize machine learning model 1100, but other devices may apply inputs associated with a particular patient to machine learning model 1100 in other examples. Some non-limiting examples of machine learning techniques include Bayesian probability models, Support Vector Machines, K-Nearest Neighbor algorithms, and Multi-layer Perceptron.

[0094] As shown in the example of FIG. 10, machine learning model 1100 may include three layers. These three layers include input layer 1102, hidden layer 1104, and output layer 1106. Output layer 1106 comprises the output from the transfer function 1105 of output layer 1106. Input layer 1102 represents each of the input values XI through X4 provided to machine learning model 1100. The number of inputs may be less than or greater than 4, including much greater than 4, e.g., hundreds or thousands. In some examples, the input values may be any of the of physiological or other patient parameter values described herein.

[0095] Each of the input values for each node in the input layer 1102 is provided to each node of hidden layer 1104. In the example of FIG. 10, hidden layers 1104 include two layers, one layer having four nodes and the other layer having three nodes, but fewer or greater number of nodes may be used in other examples. Each input from input layer 1102 is multiplied by a weight and then summed at each node of hidden layers 1104. During training of machine learning model 1100, the weights for each input are adjusted to establish the relationship between input physiological parameter values and one or more output values indicative of a risk level of a health event for the patient. In some examples, one hidden layer may be incorporated into machine learning model 1100, or three or more hidden layers may be incorporated into machine learning model 1100, where each layer includes the same or different number of nodes.

[0096] The result of each node within hidden layers 1104 is applied to the transfer function of output layer 1106. The transfer function may be liner or non-linear, depending on the number of layers within machine learning model 1100. Example non-linear transfer functions may be a sigmoid function or a rectifier function. The output 1107 of the transfer function may be a value or values indicative of a cardiac event probability score a cardiac data sample is associated with a particular cardiac event of the patient. By applying the cardiac data to a machine learning model, such as machine learning model 1100, processing circuitry of system 2 is able to determine the cardiac event probability score a cardiac data sample is associated with a particular cardiac event with great accuracy, specificity, and sensitivity.

[0097] The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the techniques may be implemented within one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic QRS circuitry, as well as any combinations of such components, embodied in external devices, such as physician or patient programmers, stimulators, or other devices. The terms “processor” and “processing circuitry” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry, and alone or in combination with other digital or analog circuitry.

[0098] For aspects implemented in software, at least some of the functionality ascribed to the systems and devices described in this disclosure may be embodied as instructions on a computer-readable storage medium such as RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. The instructions may be executed to support one or more aspects of the functionality described in this disclosure. [0099] In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and/or software modules. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware or software components, or integrated within common or separate hardware or software components. Also, the techniques could be fully implemented in one or more circuits or logic elements. The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including an IMD, an external programmer, a combination of an IMD and external programmer, an integrated circuit (IC) or a set of ICs, and/or discrete electrical circuitry, residing in an IMD and/or external programmer.

[0100] Various aspects of the techniques may enable the following examples. [0101] Example 1 : A system includes a memory configured to store a plurality of sets of cardiac data, wherein each set of cardiac data of the plurality of sets of cardiac data is associated with a patient; and processing circuitry in communication with the memory, wherein the processing circuitry is configured to: cause a user interface to display a set of cardiac data of the plurality of sets of cardiac data, wherein the set of cardiac data includes a plurality of cardiac data samples, and wherein each cardiac data sample of the plurality of cardiac data samples is associated with a cardiac event probability that the cardiac data sample is associated with a cardiac event; receive, from the user interface, a user selection of a cardiac event probability threshold corresponding to the cardiac event; and cause the user interface to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the cardiac event probability threshold.

[0102] Example 2: The system of example 1, wherein the cardiac data comprises an electrocardiogram (ECG) signal.

[0103] Example 3: The system of any of examples 1-2, wherein the cardiac event comprises a particular arrhythmia.

[0104] Example 4: The system of example 2, wherein the cardiac event indicates a respective cardiac data sample comprises at least part of a particular feature of the ECG signal, and the particular feature of the ECG signal is a P-wave.

[0105] Example 5: The system of example 2, wherein the cardiac event indicates a respective cardiac data sample comprises at least part of a particular feature of the ECG signal, and the particular feature of the ECG signal is a beginning of a P-wave.

[0106] Example 6: The system of example 2, wherein the cardiac event indicates a respective cardiac data sample comprises at least part of a particular feature of the ECG signal, and the particular feature of the ECG signal is an end of a P-wave.

[0107] Example 7: The system of example 2, wherein the cardiac event indicates a respective cardiac data sample comprises at least part of a particular feature of the ECG signal, and the particular feature of the ECG signal is a T-wave.

[0108] Example 8: The system of example 3, wherein the particular arrhythmia is premature ventricular contraction. [0109] Example 9: The system of any of examples 1-8, wherein the processing circuitry is configured to apply the set of a cardiac data to a machine learning model to generate the cardiac event probability of each cardiac data sample.

[0110] Example 10: The system of any of examples 1-9, wherein the cardiac event probability threshold is a first cardiac event probability threshold, and the processing circuitry is further configured to: receive, from the user interface, a second user selection of a second cardiac event probability threshold corresponding to the cardiac event, the second cardiac event probability threshold being different than the first cardiac event probability threshold and being received after the first cardiac event probability threshold; and in response to receipt of the second cardiac event probability threshold, cause the user interface to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the second cardiac event probability threshold.

[0111] Example 11 : A method includes storing a plurality of sets of cardiac data, wherein each set of cardiac data of the plurality of sets of cardiac data is associated with a patient; causing a user interface to display a set of cardiac data of the plurality of sets of cardiac data, wherein the set of cardiac data includes a plurality of cardiac data samples, and wherein each cardiac data sample of the plurality of cardiac data samples is associated with a cardiac event probability that the cardiac data sample is associated with a cardiac event; receiving, from the user interface, a user selection of a cardiac event probability threshold corresponding to the cardiac event; and causing the user interface to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the cardiac event probability threshold.

[0112] Example 12: The method of example 11, wherein the cardiac data comprises an electrocardiogram (ECG) signal.

[0113] Example 13: The method any of examples 11-12, wherein the cardiac event comprises a particular arrhythmia.

[0114] Example 14: The method of example 12, wherein the cardiac event indicates a respective cardiac data sample comprises at least part of a particular feature of the ECG signal, and the particular feature of the ECG signal is a P-wave. [0115] Example 15: The method of example 12, wherein the cardiac event indicates a respective cardiac data sample comprises at least part of a particular feature of the ECG signal, and the particular feature of the ECG signal is a beginning of a P-wave.

[0116] Example 16: The method of example 12, wherein the cardiac event indicates a respective cardiac data sample comprises at least part of a particular feature of the ECG signal, and the particular feature of the ECG signal is an end of a P-wave.

[0117] Example 17: The method of example 12, wherein the cardiac event indicates a respective cardiac data sample comprises at least part of a particular feature of the ECG signal, and the particular feature of the ECG signal is a T-wave.

[0118] Example 18: The method of example 13, the particular arrhythmia is premature ventricular contraction.

[0119] Example 19: The method of any of examples 11-18, wherein the method further comprises: applying the set of a cardiac data to a machine learning model to generate the cardiac event probability of each cardiac data sample.

[0120] Example 20: The method of any of examples 11-19, wherein the cardiac event probability threshold is a first cardiac event probability threshold, and the method further comprises: receiving, from the user interface, a second user selection of a second cardiac event probability threshold corresponding to the cardiac event, the second cardiac event probability threshold being different than the first cardiac event probability cardiac event threshold and being received after the first cardiac event probability threshold; and in response to receipt of the second cardiac event probability threshold, causing the user interface to indicate each cardiac data sample of the plurality of cardiac data samples.

[0121] Example 21 : A non-transitory computer-readable storage medium storing instructions, which when executed, cause processing circuitry to execute any of the methods recited in examples 11-20.

[0122] Various examples have been described. These and other examples are within the scope of the following claims.

Claims

CLAIMS What is claimed is:

1. A system comprising: a memory configured to store a plurality of sets of cardiac data, wherein each set of cardiac data of the plurality of sets of cardiac data is associated with a patient; and processing circuitry in communication with the memory, wherein the processing circuitry is configured to: cause a user interface to display a set of cardiac data of the plurality of sets of cardiac data, wherein the set of cardiac data includes a plurality of cardiac data samples, and wherein each cardiac data sample of the plurality of cardiac data samples is associated with a cardiac event probability that the cardiac data sample is associated with a cardiac event; receive, from the user interface, a user selection of a cardiac event probability threshold corresponding to the cardiac event; and cause the user interface to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the cardiac event probability threshold.

2. The system of claim 1, wherein the cardiac data comprises an electrocardiogram (ECG) signal.

3. The system of any of claims 1-2, wherein the cardiac event comprises a particular arrhythmia.

4. The system of claim 2, wherein the cardiac event indicates a respective cardiac data sample comprises at least part of a particular feature of the ECG signal, and the particular feature of the ECG signal is a P-wave.

5. The system of claim 2, wherein the cardiac event indicates a respective cardiac data sample comprises at least part of a particular feature of the ECG signal, and the particular feature of the ECG signal is a beginning of a P-wave.

6. The system of claim 2, wherein the cardiac event indicates a respective cardiac data sample comprises at least part of a particular feature of the ECG signal, and the particular feature of the ECG signal is an end of a P-wave.

7. The system of claim 2, wherein the cardiac event indicates a respective cardiac data sample comprises at least part of a particular feature of the ECG signal, and the particular feature of the ECG signal is a T-wave.

8. The system of claim 3, wherein the particular arrhythmia is premature ventricular contraction.

9. The system of any of claims 1-8, wherein the processing circuitry is configured to apply the set of a cardiac data to a machine learning model to generate the cardiac event probability of each cardiac data sample.

10. The system of any of claims 1-9, wherein the cardiac event probability threshold is a first cardiac event probability threshold, and the processing circuitry is further configured to: receive, from the user interface, a second user selection of a second cardiac event probability threshold corresponding to the cardiac event, the second cardiac event probability threshold being different than the first cardiac event probability threshold and being received after the first cardiac event probability threshold; and in response to receipt of the second cardiac event probability threshold, cause the user interface to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the second cardiac event probability threshold.

11. A method comprising: storing a plurality of sets of cardiac data, wherein each set of cardiac data of the plurality of sets of cardiac data is associated with a patient; causing a user interface to display a set of cardiac data of the plurality of sets of cardiac data, wherein the set of cardiac data includes a plurality of cardiac data samples, and wherein each cardiac data sample of the plurality of cardiac data samples is associated with a cardiac event probability that the cardiac data sample is associated with a cardiac event; receiving, from the user interface, a user selection of a cardiac event probability threshold corresponding to the cardiac event; and causing the user interface to indicate each cardiac data sample of the plurality of cardiac data samples associated with a cardiac event probability that is greater than the cardiac event probability threshold.

12. The method of claim 11, wherein the cardiac data comprises an electrocardiogram (ECG) signal.

13. The method of any of claims 11-12, wherein the cardiac event comprises a particular arrhythmia.

14. The method of claim 12, wherein the cardiac event indicates a respective cardiac data sample comprises at least part of a particular feature of the ECG signal, and the particular feature of the ECG signal is a P-wave or a T-wave.

15. The method of any of claims 11-14, wherein the cardiac event probability threshold is a first cardiac event probability threshold, and the method further comprises: receiving, from the user interface, a second user selection of a second cardiac event probability threshold corresponding to the cardiac event, the second cardiac event probability threshold being different than the first cardiac event probability cardiac event threshold and being received after the first cardiac event probability threshold; and in response to receipt of the second cardiac event probability threshold, causing the user interface to indicate each cardiac data sample of the plurality of cardiac data samples.