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WO2025219783A1 - Dispositif médical configuré pour surveiller le risque d'hypertension pulmonaire à l'aide de signaux optiques et d'électrocardiogramme - Google Patents

Dispositif médical configuré pour surveiller le risque d'hypertension pulmonaire à l'aide de signaux optiques et d'électrocardiogramme

Info

Publication number
WO2025219783A1
WO2025219783A1 PCT/IB2025/053081 IB2025053081W WO2025219783A1 WO 2025219783 A1 WO2025219783 A1 WO 2025219783A1 IB 2025053081 W IB2025053081 W IB 2025053081W WO 2025219783 A1 WO2025219783 A1 WO 2025219783A1
Authority
WO
WIPO (PCT)
Prior art keywords
patient
features
processing circuitry
optical signal
signal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/IB2025/053081
Other languages
English (en)
Inventor
Veronica RAMOS
Todd M. Zielinski
Yong K. Cho
Richard J. O'brien
Douglas A. Hettrick
Shantanu Sarkar
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Medtronic Inc
Original Assignee
Medtronic Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Medtronic Inc filed Critical Medtronic Inc
Publication of WO2025219783A1 publication Critical patent/WO2025219783A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0004—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
    • A61B5/0006—ECG or EEG signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/02416—Measuring pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1118—Determining activity level
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6846—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive
    • A61B5/6847—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive mounted on an invasive device
    • A61B5/686—Permanently implanted devices, e.g. pacemakers, other stimulators, biochips
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7221—Determining signal validity, reliability or quality
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0223—Operational features of calibration, e.g. protocols for calibrating sensors

Definitions

  • This disclosure generally relates to medical devices and, more particularly, to disease state monitoring devices.
  • IMD implantable medical devices
  • cardiac, neurological, and/or other conditions of a patient may monitor physiological signals of the patient.
  • IMD may monitor an electrocardiogram (ECG) of the patient to monitor patient conditions.
  • ECG electrocardiogram
  • ECG electrocardiogram
  • such devices are configured to monitor patient conditions based on one or more physiological signals.
  • Such IMDs may process and/or transmit device data to investigate patient health.
  • the data may be transmitted to a cloud computing system, which may process the data for analysis and/or presentation to the user.
  • a patient may have a medical device system including one or more implantable medical devices (IMDs) that continuously monitor one or more physiological signals.
  • IMDs implantable medical devices
  • Processing circuitry of the system detects a change, e.g., worsening or improvement, in PH risk based on one or more features of the one or more physiological signals and provides notifications to one or both of the patient or a clinician to allow the patient and/or the clinician to address the change in PH risk level.
  • the IMD may determine the one or more features of the one or more physiological signals, which may include an optical signal, an electrocardiogram (ECG) signal, a signal indicative of patient activity, such as an accelerometer signal, as examples.
  • Processing circuitry of the IMD may use the one or more features of the optical signal and ECG signal to determine a risk of PH.
  • the one or more features of the optical signal may be indicative of one or more of systemic vascular resistance, cardiac output (CO), or blood pressure (BP).
  • the IMD may output estimates of systemic vascular resistance, CO, and/or BP.
  • the IMD may output an indication of the risk of PH.
  • the IMD may output an indication of the risk of PH if the risk of PH has changed.
  • the medical device system may generate a notification of the risk of PH and may send the notification to the patient and/or the clinician.
  • PH is a serious condition that affects arteries in the lungs and the heart and is defined by high blood pressure in the pulmonary arteries. PH leads to oxygen-deficient blood flow from the heart to all organs. PH is the most common cause of right heart failure (HF) and is often caused by left HF. PH is primarily diagnosed via multi-lead ECG and right heart catheterization.
  • HF right heart failure
  • PH is a health issue that affects all age groups, but the condition is becoming increasingly prevalent as the elder population increases, as the risk of PH increases 10-fold in subjects over 65 years of age. PH has serious potential consequences, e.g., right HF, but proactive monitoring and treatment can slow or prevent disease progression, thereby increasing a patient’s quality of life.
  • the techniques of this disclosure may be implemented by one or more IMDs that can continuously (e.g., on a periodic or triggered basis without human intervention) sense signals while subcutaneously implanted in a patient over months or years and perform numerous operations per second on the signals to enable the systems herein to determine PH risk of a patient.
  • the techniques of this disclosure may facilitate the monitoring and subsequent treatment of PH or other disease states.
  • the patient may be more likely to receive treatment in a timely manner, which may lead to better patient outcomes and quality of life.
  • the techniques of this disclosure include generating an indication of disease state based on continuously monitored patient parameters, which may initiate timely examination of a patient to determine if intervention is needed, improvements to the current methods of PH risk monitoring may improve patient outcomes.
  • determining risk of PH of patients using physiological signals sensed by one or more IMDs may reduce a need for patient compliance.
  • some current techniques of monitoring PH e.g., a patient or clinician initiating a PH screen based on patient symptoms, may be dependent on a patient identifying a need to see a clinician and subsequently scheduling a visit with the clinician.
  • Some patients may be asymptomatic, even as disease state worsens. Other patients may become symptomatic but attribute the symptoms to another illness or normal progression of aging or may choose not to visit the clinician.
  • the techniques of this disclosure are therefore more objective and less dependent on patient compliance than other methods, which may improve patient outcomes.
  • the one or more IMDs include a single lead implantable cardiac monitor (ICM).
  • ICM implantable cardiac monitor
  • the ICM may be implanted via minimally invasive surgery.
  • the single lead ICM may be facilitate unobtrusive monitoring, e.g., relative to external/wearable devices.
  • the techniques of this disclosure may allow patients to perform daily activities more easily than with a larger IMD and/or an external device.
  • the techniques of this disclosure may additionally include filtering out data that is deemed to be low quality or that may otherwise lead to inaccurate determinations of PH risk. By filtering out data that is low quality, the techniques of this disclosure may advantageously result in more accurate determinations of PH risk, which may improve patient outcomes.
  • a medical system comprises: at least one implantable medical device (IMD) configured to sense an electrocardiogram (ECG) signal and an optical signal of a patient; and processing circuitry configured to: based on one or more features of the optical signal and one or more features of the ECG signal, determine a risk of pulmonary hypertension (PH) for the patient; and generate a communication for presentation to a user based on the risk of PH.
  • IMD implantable medical device
  • ECG electrocardiogram
  • PH pulmonary hypertension
  • a method comprises: sensing, by sensing circuitry of an implantable medical device (IMD) of a medical system, an electrocardiogram (ECG) signal and an optical signal of a patient; determining, by processing circuitry of the medical system, a risk of pulmonary hypertension (PH) for the patient based on one or more features of the optical signal and one or more features of the ECG signal; and generating, by the processing circuitry, a communication for presentation to a user based on the risk of PH.
  • IMD implantable medical device
  • ECG electrocardiogram
  • PH pulmonary hypertension
  • a non-transitory computer-readable medium stores instructions that when executed by processing circuitry, cause the processing circuitry to: based on one or more features of an optical signal and one or more features of an ECG signal sensed by sensing circuitry of an implantable medical device (IMD), determine a risk of pulmonary hypertension (PH) for the patient; and generate a communication for presentation to a user based on the risk of PH.
  • IMD implantable medical device
  • FIG. 1 illustrates the environment of an example medical device system in conjunction with a patient, in accordance with one or more techniques of this disclosure.
  • FIG. 2 is a block diagram illustrating an example system configured to determine the disease state of a patient in accordance with one or more techniques of this disclosure.
  • FIGS. 3A and 3B are conceptual diagrams illustrating example implantable medical devices that operate in accordance with one or more techniques of this disclosure.
  • FIG. 4 is a block diagram illustrating an example configuration of an implantable medical device that operates in accordance with one or more techniques of this disclosure.
  • FIG. 5 is a block diagram illustrating an example configuration of an external device that operates in accordance with one or more techniques of this disclosure.
  • FIG. 6 is a flow diagram illustrating an example operation for determining a risk of pulmonary hypertension (PH) of a patient, in accordance with one or more techniques of this disclosure.
  • FIG. 7 is a graph illustrating an optical signal, in accordance with one or more techniques of this disclosure.
  • FIG. 8 is a graph illustrating an example optical signal including features, in accordance with one or more techniques of this disclosure.
  • FIG. 9 is a graph illustrating an example optical signal including features, in accordance with one or more techniques of this disclosure.
  • FIG. 10 is a graph illustrating an ECG signal, in accordance with one or more techniques of this disclosure.
  • FIG. 11 is a flow diagram illustrating an example operation for determining whether a filtered portion of the ECG signal is indicative of low quality or a patient state and whether to output an indication to the user based on the determination, in accordance with one or more techniques of this disclosure.
  • FIG. 12 is a flow diagram illustrating an example operation for determining baseline signal values for patient monitoring, in accordance with one or more techniques of this disclosure.
  • FIG. 13 is a conceptual diagram illustrating an example machine learning model configured to determine a risk of PH, in accordance with one or more techniques of this disclosure.
  • FIG. 14 is a conceptual diagram illustrating an example training process for a machine learning model, in accordance with one or more techniques of this disclosure.
  • a variety of types of implantable and external devices are configured to monitor health based on sensed electrocardiograms (ECGs) and, in some cases, other physiological signals.
  • ECGs electrocardiograms
  • External devices that may be used to non-invasively sense and monitor ECGs and other physiological signals include wearable devices with electrodes configured to contact the skin of the patient, such as patches, watches, rings, necklaces, hearing aids, a wearable cardiac monitor or automated external defibrillator (AED), clothing, car seats, or bed linens.
  • Such external devices may facilitate relatively longer- term monitoring of patient health during normal daily activities.
  • Implantable medical devices also sense and monitor ECGs, optical signals, and/or other physiological signals and detect changes in patient health and/or health events.
  • Example IMDs include pacemakers and implantable cardioverterdefibrillators, 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. Some IMDs do not provide therapy, such as implantable patient monitors.
  • One example of such an IMD is the Reveal LINQTM or LINQ IITM insertable cardiac monitors (ICMs), available from Medtronic, Inc., which may be inserted subcutaneously.
  • Such IMDs may facilitate relatively longer-term continuous monitoring of patients during normal daily activities, and may periodically or on demand transmit collected data, e.g., data collected on a periodic schedule and/or responsive to identifying one or more flags in one or more of the signals, to a remote patient monitoring system, such as the Medtronic CareLinkTM Network via a home monitoring system or a smart phone application.
  • collected data e.g., data collected on a periodic schedule and/or responsive to identifying one or more flags in one or more of the signals
  • a remote patient monitoring system such as the Medtronic CareLinkTM Network via a home monitoring system or a smart phone application.
  • FIG. 1 illustrates the environment of an example medical device 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 one or more patient sensing devices, e.g., including an IMD 10, which may be in wireless communication with one or more computing devices, e.g., external device 12.
  • IMD 10 includes electrodes, optical sensors, and/or other sensors to sense physiological signals, e.g., an accelerometer signal, of patient 4 and may collect and store one or more features of the sensed signals.
  • optical sensor(s) 458 of IMD 10 is a photoplethysmography (PPG) sensor.
  • PPG photoplethysmography
  • One or more elements of system 2 may determine a PH risk of patient 4 based on the collected data.
  • IMD 10 may be 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, and be configured to sense an ECG, an optical signal, and/or other physiological signals from that position. In some examples, IMD 10 takes the form of the Reveal LINQTM or LINQ IITM ICM. In some examples, IMD 10 includes additional sensors, such as one or more sensors configured to sense patient activity, e.g., one or more accelerometers.
  • IMD 10 may alternatively be implanted outside of an abdominal cavity of patient 4.
  • more than one IMD 10 may be implanted in patient 4, e.g., an IMD 10 implanted outside of the thoracic cavity and an IMD 10 implanted outside of the abdominal cavity of patient 4.
  • each IMD 10 may collect different information.
  • the IMD 10 implanted outside the thoracic cavity may sense signals indicative of an increase in thoracic impedance
  • the IMD 10 implanted outside the abdominal cavity may sense signals indicative of an increase in abdominal impedance.
  • system 2 may identify a volume shift of blood from the thorax to the abdominal venous reservoir.
  • IMD 10 is a single lead ICM
  • the ICM may be implanted via minimally invasive surgery.
  • the single lead ICM may be facilitate unobtrusive monitoring.
  • the techniques of this disclosure may be implemented in systems including any one or more implantable or external medical devices, including monitors, pacemakers, defibrillators (e.g., subcutaneous or substernal), wearable external defibrillators (WAEDs), neurostimulators, drug pumps, patch monitors, or wearable physiological monitors, e.g., wrist or head wearable devices. Examples with multiple IMDs or other sensing devices may be able to collect different data useable by system 2 to determine a PH risk of patient 4.
  • 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). External device 12 is configured for wireless communication with IMD 10. External device 12 retrieves sensed physiological data from IMD 10 that was collected and stored by the IMD. In some examples, external device 12 takes the form of a personal computing device of patient 4. For example, external device 12 may take the form of a smartphone of patient 4. In some examples, external device 12 may be any computing device configured for wireless communication with IMD 10, such as a desktop, laptop, or tablet computer.
  • External device 12 may communicate with IMD 10 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., radiofrequency telemetry according to the Bluetooth® or Bluetooth® Low Energy (BLE) protocols, or other communication technologies operable at ranges greater than near-field communication technologies.
  • near-field communication technologies e.g., inductive coupling, NFC or other communication technologies operable at ranges less than 10-20 cm
  • far-field communication technologies e.g., radiofrequency telemetry according to the Bluetooth® or Bluetooth® Low Energy (BLE) protocols, or other communication technologies operable at ranges greater than near-field communication technologies.
  • BLE Bluetooth® Low Energy
  • external device 12 may be used to transmit instructions to IMD 10.
  • the clinician may also configure and store operational parameters for IMD 10 with the aid of external device 12.
  • external device 12 assists the clinician in the configuration of IMD 10 by providing a system for identifying potentially beneficial operational parameter values.
  • External device 12 may be used to retrieve data from IMD 10.
  • the retrieved data may include signal data and/or one or more features of the signal data determined by IMD 10 based on signals sensed by IMD 10.
  • external device 12 may retrieve physiological signal data, e.g., ECG signal data, optical signal data, and/or activity/posture signal data, on a regular transmission schedule, e.g., daily, due to IMD 10 determining that the patient is at a particular activity level or posture, or in response to a request to record the segment from patient 4 or another user.
  • IMD 10 may include a sensor configured to sense motion, vibration, and/or orientation with respect to earth gravity, such as an accelerometer configured to sense activity of patient 4.
  • external device 12 may retrieve data indicative of one or more features and/or a risk of PH determined by IMD 10 based on the signals.
  • Processing circuitry of system 2 may be configured to perform the example techniques described herein for determining PH risk based on data collected by IMD 10.
  • one or more of the sensors, e.g., of IMD 10 may be implanted within patient 4, that is, implanted at least subcutaneously.
  • one or more of the sensors of IMD 10 may be located externally to patient 4, for example as part of a cuff or as a wearable device.
  • IMD 10 transmits data to an external device 12, e.g., a smartphone of the patient, configured with an application, e.g., the Medtronic My CareLink Heart application, which may include an artificial intelligence (Al) application, to process the data, track any changes, and notify the clinician and the patient when a visit to clinic is advised or recommended.
  • the Al application comprises a machine learning (ML) model.
  • the ML model may be configured to process device data, e.g., ECG signal data, optical signal data, and/or activity signal data. The ML model may process the data and compare the results to previous data to determine the PH risk.
  • FIG. 2 is a block diagram illustrating an example system that includes an access point 210, a network 220, external computing devices, such as server 230, and one or more other computing devices 240A-240N, which may be coupled to IMD 10, and external device 12 via network 220, in accordance with one or more techniques described herein.
  • IMD 10 may communicate with external device 12 via a first wireless connection and may communicate with an access point 210 via a second wireless connection.
  • access point 220, external device 12, server 230, and computing devices 240A-240N are interconnected and may communicate with each other through network 220.
  • Access point 210 may include a device that connects to network 220 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 210 may be coupled to network 220 through different forms of connections, including wired or wireless connections.
  • 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 physiological data to external device 12.
  • access point 210 may interrogate IMD 10, such as periodically or in response to a command from the patient or network 220, in order to retrieve patient data from IMD 10. Access point 210 may be communicate the retrieved data to server 230 via network 220.
  • server 230 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 230 may assemble data for viewing by clinicians via computing devices 240 A- 240N.
  • One or more aspects of the illustrated system of FIG. 2 may be implemented with general network technology and functionality, which may be similar to that provided by the Medtronic CareLinkTM Network developed by Medtronic, Inc.
  • Server 230 may include processing circuitry 234.
  • Processing circuitry 234 may include fixed function circuitry and/or programmable processing circuitry.
  • Processing circuitry 234 may include any one or more of a microprocessor, a controller, digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), graphics processing unit (GPU), tensor processing unit (TPU), or equivalent discrete or analog logic circuitry.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA field-programmable gate array
  • GPU graphics processing unit
  • TPU tensor processing unit
  • processing circuitry 234 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 FGPAs, one or more GPUs, one or more TPUs, as well as other discrete or integrated logic circuitry.
  • the functions attributed to processing circuitry 234 may be embodied as software, firmware, hardware, or any combination thereof.
  • processing circuitry 234 may perform one or more techniques described herein to determine PH risk based on an ECG signal, an optical signal, and, in some examples, an accelerometer signal received from IMD 10, and/or one or more features determined based on such signals and received from IMD 10, as examples.
  • Server 230 may include storage device 232.
  • Storage device 232 includes computer-readable instructions that, when executed by processing circuitry 234, cause IMD 10 and processing circuitry 234 to perform various functions attributed to IMD 10 and processing circuitry 234 herein.
  • Storage device 232 may include any volatile, nonvolatile, magnetic, optical, or electrical media, such as random access memory (RAM), read only memory (ROM), non-volatile RAM (NVRAM), electronically erasable programmable ROM (EEPROM), flash memory, or any other digital media.
  • RAM random access memory
  • ROM read only memory
  • NVRAM non-volatile RAM
  • EEPROM electronically erasable programmable ROM
  • flash memory or any other digital media.
  • one or more of computing devices 240A-240N 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.
  • the clinician may access data corresponding to an ECG signal, an optical signal, an activity signal, or features based on the signals collected by IMD 10, or disease state updates determined by IMD 10 based on such features and/or signals, through device 240A, such as when patient 4 is in between clinician visits, to check on a status of a medical condition, e.g., of PH.
  • the clinician may enter instructions for medical intervention for patient 4 into an application in computing device 240A, such as based on a status of a patient condition determined by IMD 10, external device 12, or the combination thereof, or based on other patient data known to the clinician.
  • Computing device 240A may then transmit the instructions for medical intervention to another of computing devices 240, e.g., computing device 240B, located with patient 4 or a caregiver of patient 4.
  • such instructions for medical intervention may include an instruction to change a drug dosage, timing, or selection, to schedule a visit with the clinician, e.g., to schedule a PH screening, or to seek medical attention.
  • 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.
  • FIG. 3 A is a perspective drawing illustrating an IMD 10A, which may be an example configuration of IMD 10 of FIG. 1 as an ICM.
  • IMD 10A may be embodied as a monitoring device having housing 312, proximal electrode 316A, distal electrode 316B, and optical sensor(s) 458.
  • the optical sensor(s) 458 may be positioned at various locations on IMD 10 A.
  • Housing 312 may further comprise first major surface 314, second major surface 318, proximal end 320, and distal end 322. Housing 312 encloses electronic circuitry located inside the IMD 10A and protects the circuitry contained therein from body fluids. Housing 312 may be hermetically sealed and configured for subcutaneous implantation. Electrical feedthroughs provide electrical connection of electrodes 316A and 316B.
  • 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.
  • 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 insertion.
  • the device shown in FIG. 3 A includes radial asymmetries (notably, the rectangular shape) along the longitudinal axis that maintains the device in the proper orientation following insertion.
  • the spacing between proximal electrode 316A and distal electrode 316B may range from 5 millimeters (mm) to 55 mm, 30 mm to 55 mm, 35 mm to 55 mm, and from 40 mm to 55 mm and may be any range or individual spacing from 5 mm to 60 mm.
  • IMD 10A may have a length L that ranges from 30 mm to about 70 mm. In other examples, the length L may range from 5 mm to 60 mm, 40 mm to 60 mm, 45 mm to 60 mm and may be any length or range of lengths between about 30 mm and about 70 mm.
  • the width W of major surface 314 may range from 3 mm to 15, mm, from 3 mm to 10 mm, or from 5 mm to 15 mm, and may be any single or range of widths between 3 mm and 15 mm.
  • the thickness of depth D of IMD 10A may range from 2 mm to 15 mm, from 2 mm to 9 mm, from 2 mm to 5 mm, from 5 mm to 15 mm, and may be any single or range of depths between 2 mm and 15 mm.
  • 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 centimeters.
  • the first major surface 314 faces outward, toward the skin of the patient while the second major surface 318 is located opposite the first major surface 314.
  • proximal end 320 and distal end 322 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 10A is described, for example, in U.S. Patent Publication No. 2014/0276928, incorporated herein by reference in its entirety.
  • Proximal electrode 316A is at or proximate to proximal end 320, and distal electrode 16B is at or proximate to distal end 322.
  • Proximal electrode 316A and distal electrode 316B are used to sense ECG signals thoracically outside the ribcage, which may be implanted sub-muscularly or subcutaneously.
  • ECG signals may be stored in a memory of IMD 10 A, and data may be transmitted via integrated antenna 330A to another device, which may be another implantable device or an external device, such as external device 312.
  • electrodes 316A and 316B may additionally or alternatively be used for sensing any bio-potential signal of interest, which may be, for example, an electroencephalogram (EEG), electromyogram (EMG), or a nerve signal, or for measuring impedance, from any implanted location.
  • EEG electroencephalogram
  • EMG electromyogram
  • nerve signal or for measuring impedance, from any implanted location.
  • proximal electrode 316A is at or in close proximity to the proximal end 320 and distal electrode 316B is at or in close proximity to distal end 322.
  • distal electrode 316B is not limited to a flattened, outward facing surface, but may extend from first major surface 314 around rounded edges 324 and/or end surface 326 and onto the second major surface 318 so that the electrode 316B has a three-dimensional curved configuration.
  • electrode 316B is an uninsulated portion of a metallic, e.g., titanium, part of housing 312.
  • proximal electrode 316A is located on first major surface 314 and is substantially flat, and outward facing.
  • proximal electrode 316A may utilize the three-dimensional curved configuration of distal electrode 316B, providing a three dimensional proximal electrode (not shown in this example).
  • distal electrode 316B may utilize a substantially flat, outward facing electrode located on first major surface 314 similar to that shown with respect to proximal electrode 316A.
  • proximal electrode 316A and distal electrode 316B are located on both first major surface 314 and second major surface 318.
  • proximal electrode 316A and distal electrode 316B are located on both first major surface 314 and second major surface 318.
  • distal electrode 316B are located on both first major surface 314 and second major surface 318.
  • IMD 10A may include electrodes on both major surface 314 and 318 at or near the proximal and distal ends of the device, such that a total of four electrodes are included on IMD 10A.
  • Electrodes 316A and 316B 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.
  • 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.
  • proximal end 320 includes a header assembly 328 that includes one or more of proximal electrode 316A, integrated antenna 330A, anti-migration projections 332, and/or suture hole 334.
  • Integrated antenna 330A is located on the same major surface (i.e., first major surface 314) as proximal electrode 316A and is also included as part of header assembly 328.
  • Integrated antenna 330A allows IMD 10A to transmit and/or receive data.
  • integrated antenna 330A may be formed on the opposite major surface as proximal electrode 316A or may be incorporated within the housing 312 of IMD 10A. In the example shown in FIG.
  • antimigration projections 332 are located adjacent to integrated antenna 330A and protrude away from first major surface 314 to prevent longitudinal movement of the device.
  • anti-migration projections 332 include a plurality of small bumps or protrusions (e.g., nine) extending away from first major surface 314.
  • anti-migration projections 332 may be located on the opposite major surface as proximal electrode 316A and/or integrated antenna 330A.
  • header assembly 328 includes suture hole 334, which provides another means of securing IMD 10A to the patient to prevent movement following insertion.
  • header assembly 328 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.
  • FIG. 3B is a perspective drawing illustrating another IMD 10B, which may be another example configuration of IMD 10 from FIG. 1 as an ICM.
  • IMD 10B of FIG. 3B may be configured substantially similarly to IMD lOA of FIG. 3 A, with differences between them discussed herein.
  • IMD 10B may include a leadless, subcutaneously-implantable monitoring device, e.g., an ICM.
  • IMD 10B includes housing having a base 340 and an insulative cover 342.
  • Proximal electrode 316C, distal electrode 316D, and optical sensor(s) 458 may be formed or placed on an outer surface of cover 342.
  • Various circuitries and components of IMD 10B may be formed or placed on an inner surface of cover 342, or within base 340.
  • a battery or other power source of IMD 10B may be included within base 340.
  • antenna 330B is formed or placed on the outer surface of cover 342 but may be formed or placed on the inner surface in some examples.
  • insulative cover 342 may be positioned over an open base 340 such that base 340 and cover 342 enclose the circuitries and other components and protect them from fluids such as body fluids.
  • the housing including base 370 and insulative cover 372 may be hermetically sealed and configured for subcutaneous implantation.
  • Circuitries and components may be formed on the inner side of insulative cover 342, such as by using flip-chip technology.
  • Insulative cover 342 may be flipped onto a base 340. When flipped and placed onto base 340, the components of IMD 10B formed on the inner side of insulative cover 342 may be positioned in a gap 344 defined by base 340. Electrodes 316C and 316D and antenna 330B may be electrically connected to circuitry formed on the inner side of insulative cover 342 through one or more vias (not shown) formed through insulative cover 342.
  • Insulative cover 342 may be formed of sapphire (i.e., corundum), glass, parylene, and/or any other suitable insulating material.
  • Base 340 may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes 316C and 316D may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes 316C and 316D 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.
  • a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.
  • 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 IF, which in turn is larger than the depth D, similar to IMD 10A of FIG. 3 A.
  • the spacing between proximal electrode 316C and distal electrode 316D may range from 5 mm to 50 mm, from 30 mm to 50 mm, from 35 mm to 45 mm, and may be any single spacing or range of spacings from 5 mm to 50 mm, such as approximately 40 mm.
  • IMD 10B may have a length L that ranges from 5 mm to about 70 mm.
  • the length L may range from 30 mm to 70 mm, 40 mm to 60 mm, 45 mm to 55 mm, and may be any single length or range of lengths from 5 mm to 50 mm, such as approximately 45 mm.
  • the width may range from 3 mm to 15 mm, 5 mm to 15 mm, 5 mm to 10 mm, and may be any single width or range of widths from 3 mm to 15 mm, such as approximately 8 mm.
  • the thickness or depth D of IMD 10B may range from 2 mm to 15 mm, from 5 mm to 15 mm, or from 3 mm to 5 mm, and may be any single depth or range of depths between 2 mm and 15 mm, such as approximately 4 mm.
  • 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.
  • outer surface of cover 342 faces outward, toward the skin of the patient.
  • proximal end 346 and distal end 348 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient.
  • edges of IMD 10B may be rounded.
  • FIG. 4 is a block diagram illustrating an example configuration of an IMD 10 in accordance with one or more techniques described herein.
  • IMD 10 may correspond to either of IMDs 10A and 10B, or another configuration of an IMD.
  • IMD 10 includes electrodes 316 (which may correspond to any of electrodes 316A-316D), processing circuitry 450, sensing circuitry 454, sensor(s) 456, communication circuitry 460, power source 482, and memory 452.
  • the illustrated example includes two electrodes 316, IMDs including or coupled to more than two electrodes may implement the techniques of this disclosure in some examples.
  • Power source 482 provides operational power for processing circuitry 450, sensing circuitry 454, sensor(s) 456, communication circuitry 460, and memory 452.
  • Processing circuitry 450 may include fixed function circuitry and/or programmable processing circuitry. Processing circuitry 450 may include any one or more of a microprocessor, a controller, a DSP, an ASIC, a FPGA, a GPU, a TPU, or equivalent discrete or analog logic circuitry. In some examples, processing circuitry 450 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, one or more GPUs, one or more TPUs, as well as other discrete or integrated logic circuitry. The functions attributed to processing circuitry 450 herein may be embodied as software, firmware, hardware or any combination thereof.
  • Sensing circuitry 454 may be coupled to electrodes 316 to sense electrical signals of the heart of patient 4, for example by selecting electrodes 316 and polarity, used to sense an ECG as controlled by processing circuitry 450. Sensing circuitry 454 may sense the ECG from electrodes 316 in order to facilitate monitoring the electrical activity of the heart. In some examples, sensing circuitry 454 may include one or more filters and amplifiers for filtering and amplifying signals received from electrodes 316 and/or sensor(s) 456. Sensing circuitry 454 and processing circuitry 450 may store ECG data in memory 452.
  • Sensing circuitry 454 may additionally be coupled to optical sensor(s) 458 of sensor(s) 456 to sense an optical signal of patient 4, e.g., via emitter(s) 462 and detector(s) 464.
  • One or more optical sensor(s) 458 may include one or more light detector(s) 464 configured to receive and/or detect light signals, such as reflected light signals originating from light emitter(s) 462. Light signals received and/or detected by optical sensor(s) 458 may be referred to as optical signals.
  • one or more of optical sensor(s) 458 may be configured as a PPG sensor, e.g., by being configured to receive light reflected by blood in one or more blood vessels.
  • an optical sensor(s) 458 may be included in a same sensor package and/or may be implemented using the same transducer(s).
  • IMD 10 may, in some cases, include one or more optical sensor(s) 458 including two or more light emitters 464 and one or more light detectors 464.
  • Sensing circuitry 454 may also monitor additional signals from sensor(s) 456, which may include one or more accelerometers, or other vibration or motion sensors, as examples. Sensing circuitry 454 may capture sensor signals from any one of sensor(s) 456, e.g., to produce other patient data, in order to facilitate monitoring of patient activity and detecting changes in PH risk.
  • sensor(s) 456 may include one or more accelerometers, or other vibration or motion sensors, as examples.
  • Sensing circuitry 454 may capture sensor signals from any one of sensor(s) 456, e.g., to produce other patient data, in order to facilitate monitoring of patient activity and detecting changes in PH risk.
  • Communication circuitry 460 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 450, communication circuitry 460 may receive downlink telemetry from, as well as send uplink telemetry to external device 12. In addition, processing circuitry 450 may communicate with a networked computing device via an external device (e.g., external device 12) and a computer network, such as the Medtronic CareLinkTM Network. Communication circuitry 460 may be configured to transmit and/or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth, Wi-Fi, or other proprietary or non-proprietary wireless communication schemes.
  • NFC Near Field Communication
  • RF Radio Frequency
  • One or more memory devices 452 may be configured to store information within IMD 10 during operation.
  • Memory device 452 in some examples, is described as a computer-readable storage medium.
  • memory device 452 is a temporary memory, meaning that a primary purpose of memory device 452 is not longterm storage.
  • Memory device 452, in some examples, is described as a volatile memory, meaning that memory device 452 does not maintain stored contents when the computer is turned off. Examples of volatile memories include RAM, dynamic RAM (DRAM), static RAM (SRAM), and other forms of volatile memories known in the art.
  • memory device 452 is used to store program instructions for execution by processing circuitry 450.
  • Memory device 452 in one example, is used by software or applications running on IMD 10 to temporarily store information during program execution.
  • Memory device 452 also includes one or more non- transitory computer-readable storage media.
  • Memory device 452 may be configured to store larger amounts of information than volatile memory. Memory devices 452 may further be configured for long-term storage of information.
  • memory devices 452 includes non-volatile storage elements. Examples of such non-volatile storage elements include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or EEPROM memories.
  • EPROM electrically programmable memories
  • memory 452 may be configured to store physiological data, e.g., ECG data, optical signal data, and/or accelerometer data.
  • Memory 452 may additionally be configured to store one or more applications including program instructions and/or data that are executable by processing circuitry 450.
  • the one or more applications may be configured to determine one or more features of the physiological signals, and, in some examples, the risk of PH based on the one or more features.
  • the one or more applications may implement a machine learning model to determine the one or more features and/or to determine the risk of PH.
  • Other devices or systems such as external device 12 or server 230, e.g., of a cloud computing system, may additionally or alternatively implement the techniques described herein.
  • processing circuitry 450 may control communication circuitry 460 to transmit signal data, e.g., the one or more features, used to determine PH risk to another device, e.g., external device 12 or a cloud computing system comprising one or more computing devices, for analysis to determine PH risk according to the techniques of this disclosure.
  • signal data e.g., the one or more features
  • another device e.g., external device 12 or a cloud computing system comprising one or more computing devices
  • the one or more features of the physiological signals may include one or more features of the ECG signal, the optical signal, and/or the accelerometer signal.
  • Processing circuitry 450 may use ECG signals to determine PH risk. Electrodes 316 acquire ECG signals.
  • Processing circuitry 450 determines ECG signal features, e.g., R-R interval, QT interval, T-wave morphology, e.g., a binary output indicative of a presence of overpronounced T-waves, QRS complex duration, P-wave presence and/or AF burden can all be indicative of PH risk.
  • Processing circuitry 450 may additionally determine optical signal features based on the optical signal.
  • sensing circuitry 454 may include a low pass Butterworth filter to extract optical signal morphology information.
  • Processing circuitry 450 may determine one or more features of the optical signal based on the filtered optical signal.
  • the one or more features may include, as examples, a time between peaks of the optical signal, a time from a start of cardiac cycle to systolic peak, a presence of dicrotic notch, an index of dicrotic notch, a time between systolic peak and dicrotic notch, a peak amplitude of the optical signal, a peak amplitude of the dicrotic notch, and a ratio of the dicrotic notch peak amplitude to the systolic peak amplitude.
  • Processing circuitry 450 may determine patient activity information and/or features based on the accelerometer signal. In some examples, processing circuitry 450 may determine trends in patient activity level, e.g., to indicate whether the patient is becoming more or less active, which could be indicative of a change in PH risk. In some examples, processing circuitry 450 may use a patient activity level for calibration. Patient activities, such as exercise or breath holding, can cause perturbations in the ECG signal and/or optical signal. Processing circuitry 450 may identify patient values during one or more patient activities associated with perturbations during a calibration period and may determine PH risk during a monitoring period by comparing the one or more features to the one or more features associated with the perturbations.
  • FIG. 5 is a block diagram illustrating an example configuration of external device 12.
  • external device 12 takes the form of a smartphone, a laptop, a tablet computer, a personal digital assistant (PDA), a smartwatch or other wearable computing device.
  • computing devices 240 and/or server 230 may be configured similarly to the configuration of external device 12 illustrated in FIG. 5.
  • external device 12 may be logically divided into user space 502, kernel space 504, and hardware 506.
  • Hardware 506 may include one or more hardware components that provide an operating environment for components executing in user space 502 and kernel space 504.
  • User space 502 and kernel space 504 may represent different sections or segmentations of memory, where kernel space 504 provides higher privileges to processes and threads than user space 502.
  • kernel space 504 may include operating system 520, which operates with higher privileges than components executing in user space 502.
  • hardware 506 includes processing circuitry 530, memory 532, one or more input devices 534, one or more output devices 536, one or more sensors 538, and communication circuitry 540.
  • external device 12 may be any component or system that includes processing circuitry or other suitable computing environment for executing software instructions and, for example, need not necessarily include one or more elements shown in FIG. 5.
  • Processing circuitry 530 is configured to implement functionality and/or process instructions for execution within external device 12.
  • processing circuitry 530 may be configured to receive and process instructions stored in memory 532 that provide functionality of components included in kernel space 504 and user space 502 to perform one or more operations in accordance with techniques of this disclosure.
  • processing circuitry 530 may include, any one or more microprocessors, controllers, GPUs, TPUs, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry.
  • Memory 532 may be configured to store information within external device 12, for processing during operation of external device 12.
  • Memory 532 in some examples, is described as a computer-readable storage medium.
  • memory 532 includes a temporary memory or a volatile memory. Examples of volatile memories include RAM, DRAM, SRAM, and other forms of volatile memories known in the art.
  • Memory 532 in some examples, also includes one or more memories configured for longterm storage of information, e.g. including non-volatile storage elements. Examples of such non-volatile storage elements include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories.
  • EPROM electrically programmable memories
  • EEPROM electrically erasable and programmable
  • memory 532 includes cloud-associated storage.
  • One or more input devices 534 of external device 12 may receive input, e.g., from patient 4, a clinician, or another user. Examples of input are tactile, audio, kinetic, and optical input. Input devices 534 may include, as examples, a mouse, keyboard, voice responsive system, camera, buttons, control pad, microphone, presence-sensitive or touch- sensitive component (e.g., screen), or any other device for detecting input from a user or a machine.
  • One or more output devices 536 of external device 12 may generate output, e.g., to patient 4 or another user. Examples of output are tactile, haptic, audio, and visual output.
  • Output devices 536 of external device 12 may include a presence-sensitive screen, sound card, video graphics adapter card, speaker, cathode ray tube monitor, liquid crystal display (LCD), light emitting diodes (LEDs), or any type of device for generating tactile, audio, and/or visual output.
  • a presence-sensitive screen sound card, video graphics adapter card, speaker, cathode ray tube monitor, liquid crystal display (LCD), light emitting diodes (LEDs), or any type of device for generating tactile, audio, and/or visual output.
  • One or more sensors 538 of external device 12 may sense physiological parameters or signals of patient 4.
  • Sensor(s) 538 may include electrodes, an optical sensor, accelerometers (e.g., 3-axis accelerometers), impedance sensors, temperature sensors, pressure sensors, heart sound sensors (e.g., microphones or accelerometers), and other sensors, and sensing circuitry (e.g., including an ADC), similar to those described above with respect to IMD 10.
  • accelerometers e.g., 3-axis accelerometers
  • impedance sensors e.g., impedance sensors
  • temperature sensors e.g., pressure sensors
  • heart sound sensors e.g., microphones or accelerometers
  • other sensors e.g., microphones or accelerometers
  • Communication circuitry 540 of external device 12 may communicate with other devices by transmitting and receiving data.
  • Communication circuitry 540 may receive physiological data from IMD 10, such as ECG signal data, optical signal data, and/or accelerometer signal data, from communication circuitry in IMD 10.
  • Communication circuitry 540 may include a network interface card, such as an Ethernet card, an optical transceiver, a radio frequency transceiver, or any other type of device that can send and receive information.
  • communication circuitry 540 may include a radio transceiver configured for communication according to standards or protocols, such as 3G, 4G, 5G, WiFi (e.g., 802.11 or 802.15 ZigBee), Bluetooth®, or Bluetooth® Low Energy (BLE).
  • standards or protocols such as 3G, 4G, 5G, WiFi (e.g., 802.11 or 802.15 ZigBee), Bluetooth®, or Bluetooth® Low Energy (BLE).
  • health monitoring application 550 executes in user space 502 of external device 12.
  • Health monitoring application 550 may be logically divided into presentation layer 552, application layer 554, and data layer 556.
  • Presentation layer 552 may include a user interface (UI) component 560, which generates and renders user interfaces of health monitoring application 550.
  • UI user interface
  • Data layer 556 may include feature data 590 and physiological signal data 592, which may be received from IMD 10 via communication circuitry 540 and stored in memory 532 by processing circuitry 530.
  • Application layer 554 may include, but is not limited to, status analyzer 570, and model configuration application 572.
  • Status analyzer 570 may determine a PH risk using feature data 590 based on physiological signal data 592, which may be generated by IMD 10, as described herein.
  • Status analyzer 570 may determine the health condition status based on application of the data as inputs one or more model(s) 594, which may include one or more machine learning models, algorithms, decision trees, and/or thresholds. In examples in which models 594 include one or more machine learning models, status analyzer 570 may apply feature vectors derived from the data to the model(s) 594.
  • Model configuration component 572 may be configured to develop models 594 based on machine learning.
  • Example machine learning techniques that may be employed to generate model(s) 594 can include various learning styles, such as supervised learning, unsupervised learning, and semi-supervised learning.
  • Example types of algorithms include Bayesian algorithms, Markov models, Hawkes processes, Clustering algorithms, decisiontree algorithms, regularization algorithms, regression algorithms, instance-based algorithms, artificial neural network algorithms, deep learning algorithms, dimensionality reduction algorithms and the like.
  • Various examples of specific algorithms include Bayesian Linear Regression, Boosted Decision Tree Regression, and Neural Network Regression, Back Propagation Neural Networks, Convolution Neural Networks (CNN), Long Short Term Networks (LSTM), the Apriori algorithm, K-Means Clustering, k- Nearest Neighbour (kNN), Learning Vector Quantization (LVQ), Self-Organizing Map (SOM), Locally Weighted Learning (LWL), Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, and Least-Angle Regression (LARS), Principal Component Analysis (PCA) and Principal Component Regression (PCR).
  • Bayesian Linear Regression Boosted Decision Tree Regression
  • Neural Network Regression Back Propagation Neural Networks
  • CNN Convolution Neural Networks
  • LSTM Long Short Term Networks
  • K-Means Clustering K-Means Clustering
  • kNN Learning Vector Quantization
  • SOM Self-Organizing Map
  • FIG. 6 is a flow diagram illustrating an example operation for determining a risk of PH of patient 4, in accordance with one or more techniques of this disclosure.
  • the example operation of FIG. 6 is described primarily as being performed by server 230, e.g., by processing circuitry 234 of server 230. In some examples, the operation may be performed in whole or part by additional/altemative devices, e.g., IMD 10 (including processing circuitry 450) and/or external device 12 (including processing circuitry 530).
  • processing circuitry 234 receives an ECG signal and an optical signal (602). In some examples, processing circuitry 234 receives the signals from communication circuitry 460 of IMD 10 or communication circuitry 540 of external device 12.
  • the physiological signal data may comprise a segment of captured, i.e., stored, signal data.
  • Processing circuitry 450 of IMD 10 may capture signal data periodically, e.g., monthly, for transmission to server 234 and/or external device 12 and storage as physiological data 592. Additionally, or alternatively, processing circuitry 450 may continuously monitor the ECG signal for flags indicative of a potential change in PH risk. Processing circuitry may identify flags in the ECG signal by comparing the ECG signal to a baseline signal, e.g., a template signal, and/or baseline features of a signal. T-wave inversion, ST segment depression, and/or prolonged QT intervals may be flags indicative of a potential change in PH risk. Responsive to identifying the flags, processing circuitry 450 may capture ECG signal data and optical signal data and transmit the data to server 234 and/or external device 12 to be stored as physiological data 592.
  • the captured physiological signal data includes a specified number of samples.
  • the captured physiological data includes data corresponding to a specified amount of time, e.g., 10 seconds or 30 seconds.
  • processing circuitry may select a shorter period of time within the time window, e.g., 10 seconds of the 30 seconds, based on the shorter period of time meeting a quality standard.
  • Processing circuitry e.g., processing circuitry 234 of server 230, determines a PH risk of based on one or more features of the optical signal and one or more features of the ECG signal (604).
  • processing circuitry 450 of IMD 10 may identify the one or more features of the optical signal and the one or more features of the ECG signal and transmits the one or more features to processing circuitry 234 to determine the PH risk. In some examples processing circuitry 234 determines the one or more features to determine the PH risk. In some examples, processing circuitry 234 additionally receives a patient activity signal, e.g., an accelerometer signal, and may determine PH risk based on the accelerometer signal.
  • a patient activity signal e.g., an accelerometer signal
  • the one or more features of the ECG signal include one or more of a QT interval, T-wave morphology, QRS complex duration, or AF burden.
  • the one or more features of the optical signal include one or more of a time between peaks of the optical signal, a time from a start of cardiac cycle to systolic peak, a presence of dicrotic notch, an index of dicrotic notch, a time between systolic peak and dicrotic notch, a peak amplitude of the optical signal, a peak amplitude of the dicrotic notch, or a ratio of the dicrotic notch peak amplitude to the systolic peak amplitude.
  • a dicrotic notch is a nadir point of a waveform, such as a pressure waveform, that indicates the end of systole.
  • a dicrotic notch may occur after the closure of aortic valves and precedes the secondary dicrotic wave.
  • a systole wave is a result of the direct pressure wave traveling from the left ventricle to the periphery of the body and a systolic peak is a maximum point of the systole wave.
  • a diastolic wave is a result of reflection of the pressure wave by arteries of the lower body and a diastolic peak is a maximum point of the diastole wave.
  • the one or more features of the optical signal and/or ECG signal may be indicative of one or more of blood pressure (BP), cardiac output (CO), or systemic vascular resistance.
  • processing circuitry may estimate one or more of BP, CO, or systemic vascular resistance based on the one or more features as an intermediate step in determining PH risk.
  • BP, CO, and systemic vascular resistance can be indicators of PH.
  • BP, CO, and systemic vascular resistance are related to wedge pressure, which can be an indicator of cardiac function and fluid status in PH patients.
  • wedge pressure can cause high blood pressure, can cause changes in CO, e.g., can cause increases or decreases in CO, depending on compensatory mechanisms, and can additionally influence systemic vascular resistance, e.g., can increase or decrease systemic vascular resistance, depending on compensatory mechanisms.
  • processing circuitry determines PH risk without estimating one or more of BP, CO, or systemic vascular resistance.
  • Processing circuitry generates a communication for presentation to a user, e.g., patient 4 or the clinician based on the risk of PH (608).
  • processing circuitry may request clinician permission before presenting the risk of PH to the patient.
  • processing circuitry may output a PH risk score.
  • processing circuitry may output a PH risk level, e.g., low PH risk, moderate PH risk, or high PH risk.
  • processing circuitry may additionally output an indication to schedule a PH screening appointment or to otherwise seek medical attention.
  • processing circuitry may additionally output an indication of a change in PH risk, e.g., increased risk, decreased risk, or same risk.
  • processing circuitry may output different indications to different users. For example, processing circuitry may output a PH risk level to patient 4 and may output a PH risk score and other patient health information to the clinician. In some examples, processing circuitry additionally outputs additional information to the user, e.g., based on a HF risk.
  • FIG. 7 is a graph illustrating an optical signal, in accordance with one or more techniques of this disclosure.
  • Waveform 702 is an optical signal waveform containing respiration information and cardiac pulse information.
  • Waveform 704 is a filtered, e.g., low pass Butterworth filtered, version of waveform 702, containing respiration information, e.g., the troughs and peaks of waveform 704 may be indicative of the beginnings of inspiration and expiration.
  • Waveform 706 is another filtered, e.g., low pass Butterworth filtered, version of waveform 702 containing cardiac pulse information.
  • FIG. 8 is a graph illustrating an example optical signal including cardiac pulse features, in accordance with one or more techniques of this disclosure.
  • Optical signal 800 is an optical waveform.
  • Optical signal 800 may be substantially similar to or the same as a portion of waveform 706 of FIG. 7.
  • Processing circuitry e.g., processing circuitry 450 of IMD 10, processing circuitry 530 of external device 12, or processing circuitry of computing devices 240, may be configured to identify one or more features based on optical signal 800.
  • Processing circuitry may be configured to determine a time between peaks 810 in optical signal 800.
  • Processing circuitry may determine time between peaks 810 by identifying local maxima in the signal and determining a corresponding interval of time between a first local maximum and a second local maximum of the local maxima.
  • time between peaks 810 may be an average value of time between peaks for a plurality of optical signal peaks.
  • Processing circuitry may additionally be configured to determine a time from the start of a cardiac cycle to a systolic peak, e.g., a cardiac cycle to systolic peak interval 802.
  • Processing circuitry may determine cardiac cycle to systolic peak interval 802 by identifying a local minimum in the signal, identifying a local maximum in the signal, and determining the time between the identified local minimum and the identified local maximum.
  • Processing circuitry may determine one or more peak optical signal amplitudes, such as peak amplitude 804, e.g., by identifying local maxima in the signal. Additionally, processing circuitry may determine one or more peak dicrotic notch amplitudes, such as notch amplitude 806. Processing circuitry may determine notch amplitude 806 by identifying local maxima occurring subsequently to peak optical signal amplitudes, such as peak amplitude 806. Based on an interval between peak amplitude 804 and notch amplitude 806, processing circuitry may determine peak to notch interval 808.
  • FIG. 9 is a graph illustrating an example optical signal including cardiac pulse features, in accordance with one or more techniques of this disclosure.
  • Optical signal 900 is an optical waveform.
  • Optical signal 900 may be substantially similar to or the same as optical signal 800 and/or a portion of waveform 706 of FIG. 7.
  • processing circuitry may determine whether dicrotic notches are present in optical signal 900, e.g., by identifying dicrotic peaks 902.
  • processing circuitry may additionally identify dicrotic notch indices 904, e.g., by identifying a zero crossing corresponding to the dicrotic peak.
  • FIG. 10 is a graph illustrating an ECG signal, in accordance with one or more techniques of this disclosure.
  • ECG signal 1000 may correspond to, for example, waveform 706 of FIG. 7, i.e., ECG signal 1000 and waveform 706 may each correspond to a same period of time.
  • processing circuitry may determine one or more features, such as R-wave to R-wave interval 1004, QT interval 1010, T-wave morphology, e.g., an indication of a presence of overpronounced T-waves, QRS complex duration 1002, P-wave to R-wave amplitude ratio based on P-wave amplitude 1008 and R- wave amplitude 1006, and/or AF burden.
  • processing circuitry may determine a maximum, minimum, average, and/or other statistical characterization of each feature based on ECG signal 1000. For example, processing circuitry may determine an average QT interval based on each QT interval in ECG signal 1000. In some examples, processing circuitry may use additional data to determine the one or more features. For example, processing circuitry may determine AF burden based on a 2 minute segment of ECG signal data.
  • processing circuitry may distinguish between normal sinus rhythm (NSR) ECG signal data and AF ECG signal data, e.g., ECG signal data that includes an AF episode or a portion of an AF episode.
  • NSR normal sinus rhythm
  • processing circuitry may determine the risk of PH differently, e.g., processing circuitry may determine to use signal features, e.g., ECG signal features and/or optical signal features, differently, or processing circuitry may select not to determine the risk of PH based on, e.g.
  • FIG. 11 is a flow diagram illustrating an example operation for determining whether a filtered portion of the ECG signal is indicative of low quality or a patient state and whether to output an indication to the user based on the determination, in accordance with one or more techniques of this disclosure.
  • processing circuitry e.g., processing circuitry 450 of IMD 10, processing circuitry 530 of external device 12, or processing circuitry of computing devices 240, may determine a portion of the ECG signal and/or optical signal does not meet one or more quality criteria (1102). Based on the portion not meeting the one or more quality criteria, processing circuitry filters the portion (1104).
  • processing circuitry may determine to capture new physiological signal data to determine patient 4’s risk of PH.
  • processing circuitry determines whether the filtered portion is indicative of low signal quality or of a patient state, e.g., a patient health condition (1106). Responsive to determining the filtered portion is indicative of the patient state, processing circuitry generates an indication of the patient state for output to the user (1108). In some examples, processing circuitry my determine to use the filtered portion of data indicative of the patient state in the PH risk determination. In some examples, responsive to determining the filtered portion is indicative of low signal quality, processing circuitry may output an indication to the user to adjust a configuration or placement of IMD 10.
  • a threshold percentage e.g. 10% or 20%
  • FIG. 12 is a flow diagram illustrating an example operation for determining baseline signal values for patient monitoring, in accordance with one or more techniques of this disclosure.
  • processing circuitry e.g., processing circuitry 450 of IMD 10, processing circuitry 530 of external device 12, or processing circuitry of computing devices 240, may instruct patient 4 to perform one or more activities, e.g., a ball squeeze, a Stroop color test, standing up from a seated position, e.g., to identify orthostasis, a Valsalva maneuver, a Mueller maneuver, performing mental math, or a cold pressor test, associated with perturbations in the optical signal and the ECG signal.
  • activities e.g., a ball squeeze, a Stroop color test, standing up from a seated position, e.g., to identify orthostasis, a Valsalva maneuver, a Mueller maneuver, performing mental math, or a cold pressor test, associated with perturbations in the optical signal and the ECG signal.
  • processing circuitry may determine the patient has initiated the one or more activities, e.g., sleeping, snoring, speaking, walking, standing from a seated position, eating, or drinking, based on an activity signal, e.g., via an accelerometer. Processing circuitry determines baseline values of the one or more features of the ECG signal and the one or more features of the optical signal during each of the one or more activities (1204). In some examples, one or more of memory 452 of IMD 10, memory 532 of external device 12, or memory of computing devices 240 may store the baseline values of the one or more features for each of the one or more activities.
  • processing circuitry compares the one or more features to the stored baseline values of the one or more features (1206).
  • processing circuitry may determine a patient activity type associated with the captured ECG signal and optical signal and may compare the one or more features to the baseline values of the one or more features associated with the particular activity type.
  • the baseline values may be indicative of a range of the one or more feature values, and processing circuitry may compare the one or more features to the range of baseline values.
  • FIG. 13 is a conceptual diagram illustrating an example machine learning model 1300 configured to determine a risk of PH.
  • Machine learning model 1300 is an example of a deep learning model, or deep learning algorithm, trained to determine a risk of PH.
  • One or more of IMD 10, external device 12, server 230, and/or other computing device(s) may train, store, and/or utilize machine learning model 1300, but other devices may apply inputs associated with a particular patient to machine learning model 1300 in other examples.
  • other types of machine learning and deep learning models or algorithms may be utilized in other examples.
  • a CNN model of ResNet-18 may be used, or a LSTM model may be used.
  • models that may be used for transfer learning include AlexNet, VGGNet, GoogleNet, ResNet50, or DenseNet, etc.
  • machine learning techniques include Support Vector Machines, K-Nearest Neighbor algorithm, and Multi-layer Perceptron.
  • machine learning model 1300 may include three layers. These three layers include input layer 1302, hidden layer 1304, and output layer 1306. Output layer 1306 comprises the output from the transfer function 1305 of output layer 1306. Input layer 1302 represents each of the input values XI through X4 provided to machine learning model 1300.
  • the input values may be any of the of values input into the machine learning model, as described above.
  • the input values may be one or more features extracted from the received optical signal and ECG signal, as described above.
  • input values of machine learning model 1300 may include additional data, such as accelerometer signal data relating to activity of patient 4.
  • Each of the input values for each node in the input layer 1302 is provided to each node of hidden layer 1304.
  • hidden layers 1304 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 1302 is multiplied by a weight and then summed at each node of hidden layers 1304.
  • the weights for each input are adjusted to establish the relationship between the one or more features and the risk of PH.
  • one hidden layer may be incorporated into machine learning model 1300, or three or more hidden layers may be incorporated into machine learning model 1300, where each layer includes the same or different number of nodes.
  • the result of each node within hidden layers 1304 is applied to the transfer function of output layer 1306.
  • the transfer function may be liner or non-linear, depending on the number of layers within machine learning model 1300.
  • Example non-linear transfer functions may be a sigmoid function or a rectifier function.
  • the output 1307 of the transfer function may be a classification of the risk of PH, that is generated by a computing device or computing system, such as by processing circuitry 530, in response to applying the extracted cardiac features from the received optical signal to machine learning model 1300.
  • processing circuitry 530 is able to determine the risk of PH of a patient with great accuracy, specificity, and sensitivity. This may facilitate determinations of cardiac wellness, and risk of sudden cardiac death, and may lead to clinical interventions to suppress PH such as medications and renal denervation procedures.
  • FIG. 14 is an example of the machine learning model 1402 being trained using supervised and/or reinforcement learning techniques.
  • the machine learning model 1402 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.
  • one or more of IMD 10, external device 12, server 230, and/or other computing device(s) initially trains the machine learning model 1402 based on a training set of features and corresponding to a received optical signal and ECG signal.
  • the training set 1400 may include a set of feature vectors, where each feature in the feature vector represents a value for a particular feature.
  • One or more of IMD 10, external device 12, server 230, and/or other computing device(s) may select a training set comprising a set of training instances, each training instance comprising an association between one or more respective optical signal features of a respective cardiac feature and a respective optical signal.
  • a prediction or classification by the machine learning model 1402 may be compared 1404 to the target output 1403.
  • the processing circuitry implementing a learning/training function 1405 may send or apply a modification to weights of machine learning model 1402 or otherwise modify/update the machine learning model 1402.
  • one or more of IMD 10, external device 12, server 230, and/or other computing device(s) may, for each training instance in the training set, modify, based on the respective cardiac features and the respective optical signal of the training instance, the machine learning model 1402 to change a score generated by the machine learning model 1402 in response to subsequent optical signals applied to the machine learning model 1402.
  • Example 1 A medical system comprising: at least one implantable medical device (IMD) configured to sense an electrocardiogram (ECG) signal and an optical signal of a patient; and processing circuitry configured to: based on one or more features of the optical signal and one or more features of the ECG signal, determine a risk of pulmonary hypertension (PH) for the patient; and generate a communication for presentation to a user based on the risk of PH.
  • IMD implantable medical device
  • ECG electrocardiogram
  • PH pulmonary hypertension
  • Example 2 The system of example 1, wherein the one or more features of the optical signal comprise one or more morphological features of the optical signal.
  • Example 3 The system of any one or more of examples 1-2, wherein the one or more features of the optical signal comprise one or more of: a time between peaks, a time between initiation of a cardiac cycle and a systolic peak, a presence of a dicrotic notch, a dicrotic notch index, a time between the systolic peak and the dicrotic notch, a peak amplitude of the optical signal, a peak amplitude of the dicrotic notch, or a ratio between the dicrotic notch peak amplitude and the optical signal peak amplitude.
  • Example 4 The system of any one or more of examples 1-3, wherein the one or more features of the ECG comprise one or more morphological features.
  • Example 5 The system of any one or more of examples 1-4, wherein the processing circuitry is further configured to: determine a portion of the ECG signal or the optical signal does not meet one or more quality criteria; and based on the determination, filter the portion.
  • Example 6 The system of example 5, wherein the processing circuity is further configured to: determine whether the filtered portion is low quality or indicative of a patient state; and responsive to the filtered portion being indicative of a patient state, output an indication of the patient state.
  • Example 7 The system of any one or more of examples 1-6, wherein the implantable medical device comprises an insertable cardiac monitor comprising a housing configured for subcutaneous implantation, the housing having a length, a width, and a depth, wherein the length is greater than the width, and the width is greater than the depth, and the length is within a range from 40 millimeters (mm) to 60 mm, and wherein the insertable cardiac monitor comprises an optical sensor and a plurality of electrodes on the housing and is configured to sense the ECG via the plurality of electrodes and the optical signal via the optical sensor.
  • the implantable medical device comprises an insertable cardiac monitor comprising a housing configured for subcutaneous implantation, the housing having a length, a width, and a depth, wherein the length is greater than the width, and the width is greater than the depth, and the length is within a range from 40 millimeters (mm) to 60 mm
  • the insertable cardiac monitor comprises an optical sensor and a plurality of electrodes on the housing and is configured to sense the ECG
  • Example 8 The system of any one or more of examples 1-7, wherein the IMD is further configured to sense a signal indicative of patient activity associated with perturbations in the optical signal and the ECG signal and determine the risk of PH additionally based on the signal indicative of patient activity.
  • Example 9 The system of any one or more of examples 1-7 wherein the processing circuitry is further configured to: during a calibration period, instruct the patient to perform one or more activities associated with perturbations in the optical signal and the ECG signal; determine baseline perturbation values of the one or more features of the optical signal and the ECG signal while the patient is performing the one or more activities associated with perturbations; and during a monitoring period, compare the one or more features of the optical signal and the ECG signal to the baseline perturbation values of the one or more features of the optical signals and the ECG signal.
  • Example 10 The system of any one or more of examples 1-9, wherein to determine the risk of PH the processing circuitry is configured to apply the one or more features of the optical signal and the one or more features of the ECG to a machine learning (ML model).
  • ML model machine learning
  • Example 11 The system of any one or more of examples 1-10, wherein the user comprises the patient or a clinician.
  • Example 12 A method comprising: sensing, by sensing circuitry of an implantable medical device (IMD) of a medical system, an electrocardiogram (ECG) signal and an optical signal of a patient; determining, by processing circuitry of the medical system, a risk of pulmonary hypertension (PH) for the patient based on one or more features of the optical signal and one or more features of the ECG signal; and generating, by the processing circuitry, a communication for presentation to a user based on the risk of PH.
  • IMD implantable medical device
  • ECG electrocardiogram
  • PH pulmonary hypertension
  • Example 13 The method of example 12, wherein the one or more features of the optical signal comprise one or more morphological features of the optical signal.
  • Example 14 The method of any one or more of examples 12-13, wherein the one or more features of the optical signal comprise one or more of: a time between peaks, a time between initiation of a cardiac cycle and a systolic peak, a presence of a dicrotic notch, a dicrotic notch index, a time between the systolic peak and the dicrotic notch, a peak amplitude of the optical signal, a peak amplitude of the dicrotic notch, or a ratio between the dicrotic notch peak amplitude and the optical signal peak amplitude.
  • Example 15 The method of any one or more of examples 12-14, wherein the one or more features of the ECG comprise one or more morphological features.
  • Example 16 The method of any one or more of examples 12-15, further comprising: determining, by the processing circuitry, a portion of the ECG signal or the optical signal does not meet one or more quality criteria; and filtering, by the processing circuitry, the portion of the ECG signal based on the determination.
  • Example 17 The method of example 16, further comprising: determining, by the processing circuitry, whether the filtered portion low quality or indicative of a patient state; and outputting, by the processing circuitry, an indication of the patient state responsive to the filtered portion being indicative of a patient state.
  • Example 18 The method of any one or more of examples 12-17, wherein the implantable medical device comprises an insertable cardiac monitor comprising a housing configured for subcutaneous implantation, the housing having a length, a width, and a depth, wherein the length is greater than the width, and the width is greater than the depth, and the length is within a range from 40 millimeters (mm) to 60 mm, and wherein the insertable cardiac monitor comprises an optical sensor and a plurality of electrodes on the housing and is configured to sense the ECG via the plurality of electrodes and the optical signal via the optical sensor.
  • the implantable medical device comprises an insertable cardiac monitor comprising a housing configured for subcutaneous implantation, the housing having a length, a width, and a depth, wherein the length is greater than the width, and the width is greater than the depth, and the length is within a range from 40 millimeters (mm) to 60 mm
  • the insertable cardiac monitor comprises an optical sensor and a plurality of electrodes on the housing and is configured to sense the ECG
  • Example 19 The method of any one or more of examples 12-18, further comprising: sensing, by the sensing circuitry, a signal indicative of patient activity associated with perturbations in the optical signal and the ECG signal; and determining, by the processing circuitry, the risk of PH additionally based on the signal indicative of patient activity.
  • Example 20 The method of any one or more of examples 12-18, wherein the processing circuitry is further configured to: instructing, by the processing circuitry, the patient to perform one or more activities associated with perturbations in the optical signal and the ECG signal during a calibration period; determining, by the processing circuitry, baseline perturbation values of the one or more features of the optical signal and the ECG signal while the patient is performing the one or more activities associated with perturbations; and comparing, by the processing circuitry, the one or more features and the ECG signal to the baseline perturbation values of the one or more features of the optical signal and the ECG signal during a monitoring period.
  • Example 21 The method of any one or more of examples 12-20, wherein determining the risk of PH comprises applying the one or more features of the optical signal and the one or more features of the ECG to a machine learning (ML model).
  • ML model machine learning
  • Example 22 The method of any one or more of examples 12-21, wherein the user comprises the patient or a clinician.
  • Example 23 A non-transitory computer-readable medium storing instructions that when executed by processing circuitry, cause the processing circuitry to: based on one or more features of an optical signal and one or more features of an ECG signal sensed by sensing circuitry of an implantable medical device (IMD), determine a risk of pulmonary hypertension (PH) for the patient; and generate a communication for presentation to a user based on the risk of PH.
  • IMD implantable medical device

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Abstract

Des techniques sont divulguées pour surveiller un risque d'hypertension pulmonaire (HP) chez un patient. Dans un exemple, un système médical comprend : au moins un dispositif médical implantable (DMI) configuré pour détecter un signal d'électrocardiogramme (ECG) et un signal optique d'un patient ; et une circuiterie de traitement configurée pour : sur la base d'une ou plusieurs caractéristiques du signal optique et d'une ou plusieurs caractéristiques du signal ECG, déterminer un risque de HP pour le patient ; et générer une communication pour présentation à un utilisateur sur la base du risque de HP.
PCT/IB2025/053081 2024-04-16 2025-03-24 Dispositif médical configuré pour surveiller le risque d'hypertension pulmonaire à l'aide de signaux optiques et d'électrocardiogramme Pending WO2025219783A1 (fr)

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Citations (4)

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Publication number Priority date Publication date Assignee Title
US20140142443A1 (en) * 2012-11-19 2014-05-22 Pacesetter, Inc. Systems and methods for exploiting pulmonary artery pressure obtained from an implantable sensor to detect cardiac rhythm irregularities
US20140276928A1 (en) 2013-03-15 2014-09-18 Medtronic, Inc. Subcutaneous delivery tool
US20210093220A1 (en) * 2019-09-27 2021-04-01 Medtronic, Inc. Determining health condition statuses using subcutaneous impedance measurements
US20230075634A1 (en) * 2021-08-23 2023-03-09 Analytics For Life Inc. Methods and systems for engineering conduction deviation features from biophysical signals for use in characterizing physiological systems

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140142443A1 (en) * 2012-11-19 2014-05-22 Pacesetter, Inc. Systems and methods for exploiting pulmonary artery pressure obtained from an implantable sensor to detect cardiac rhythm irregularities
US20140276928A1 (en) 2013-03-15 2014-09-18 Medtronic, Inc. Subcutaneous delivery tool
US20210093220A1 (en) * 2019-09-27 2021-04-01 Medtronic, Inc. Determining health condition statuses using subcutaneous impedance measurements
US20230075634A1 (en) * 2021-08-23 2023-03-09 Analytics For Life Inc. Methods and systems for engineering conduction deviation features from biophysical signals for use in characterizing physiological systems

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