Medtronic, Inc.

Medtronic Monitoring, Inc.

Niranjan Chakravarthy

Rodolphe Katra

Andrew Radtke

Tarek D Haddad

Donald R Musgrove

Lindsay A Pedalty

Siddharth Dani

Eduardo N Warman

Arthur Lai

Scott Williams

Brion C Finlay

Shantanu Sarkar

Jerry D Reiland

Eric M Christensen

Deborah Ann Jaye

John C Doerfler

Geert Morren

Amie Bucksa

Techniques are disclosed for using both feature delineation and machine learning to detect cardiac arrhythmia. A computing device receives cardiac electrogram data of a patient sensed by a medical device. The computing device obtains, via feature-based delineation of the cardiac electrogram data, a first classification of arrhythmia in the patient. The computing device applies a machine learning model to the received cardiac electrogram data to obtain a second classification of arrhythmia in the patient. As one example, the computing device uses the first and second classifications to determine whether an episode of arrhythmia has occurred in the patient. As another example, the computing device uses the second classification to verify the first classification of arrhythmia in the patient. The computing device outputs a report indicating that the episode of arrhythmia has occurred and one or more cardiac features that coincide with the episode of arrhythmia.

Arrhythmia detection with feature delineation and machine learning

A method of determining signal quality in a patient monitoring device includes acquiring one or more signals using the patient monitoring device. One or more signal quality metrics are determined based on the one or more acquired signals. A noise condition is detected based on the one or more signal quality metrics, and a determination is made whether the noise condition should be classified as intermittent or persistent. One or more actions are taken based on the classification of detected noise as intermittent or persistent.

System and method of patient monitoring and signal quality analysis

Techniques are disclosed for detecting arrhythmia episodes for a patient. A medical device may receive one or more sensor values indicative of motion of a patient. The medical device may determine, based at least in part on the one or more sensor values, an activity level of the patient. The medical device may determine a heart rate threshold for triggering detection of an arrhythmia episode based at least in part on the activity level of the patient. The medical device may determine whether to trigger detection of the arrhythmia episode for the patient based at least in part on comparing a heart rate of the patient with the heart rate threshold. The medical device may, in response to triggering detection of the arrhythmia episode, collect information associated with the arrhythmia episode.

Triggering arrhythmia episodes for heart failure and chronotropic incompetence diagnosis and monitoring

Techniques are disclosed for using feature delineation to reduce the impact of machine learning cardiac arrhythmia detection on power consumption of medical devices. In one example, a medical device performs feature-based delineation of cardiac electrogram data sensed from a patient to obtain cardiac features indicative of an episode of arrhythmia in the patient. The medical device determines whether the cardiac features satisfy threshold criteria for application of a machine learning model for verifying the feature-based delineation of the cardiac electrogram data. In response to determining that the cardiac features satisfy the threshold criteria, the medical device applies the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred or determine a classification of the episode of arrhythmia.

Reduced power machine learning system for arrhythmia detection

Techniques that include applying machine learning models to episode data, including a cardiac electrogram, stored by a medical device are disclosed. In some examples, based on the application of one or more machine learning models to the episode data, processing circuitry derives, for each of a plurality of arrhythmia type classifications, class activation data indicating varying likelihoods of the classification over a period of time associated with the episode. The processing circuitry may display a graph of the varying likelihoods of the arrhythmia type classifications over the period of time. In some examples, processing circuitry may use arrhythmia type likelihoods and depolarization likelihoods to identify depolarizations, e.g., QRS complexes, during the episode.

Machine learning based depolarization identification and arrhythmia localization visualization

Techniques for remote monitoring of a patient and corresponding medical device(s) are described. The remote monitoring comprises determining identification data and identifying implantable medical device (IMD) information, initiating an imaging device and determining an imaging program, receiving one or more frames of image data including image(s) of an implantation site, identifying an abnormality at the implantation site, triggering a supplemental image capture mode, receiving one or more supplemental images of the implantation site, and outputting the one or more supplemental images of the implantation site.

Post operative implantation site monitoring

Techniques are disclosed for monitoring a patient for the occurrence of a cardiac arrhythmia. A computing system generates sample probability values by applying a machine learning model to sample patient data. The machine learning model determines a respective probability value that indicates a probability that the cardiac arrhythmia occurred during each respective temporal window. The computing system outputs a user interface comprising graphical data based on the sample probability values and receives, via the user interface, an indication of user input to select a probability threshold for a patient. The computing system receives patient data for the patient and applies the machine learning model to the patient data to determine a current probability value. In response to the determination that the current probability exceeds the probability threshold for the patient, the computing system generates an alert indicating the patient has likely experienced the occurrence of the cardiac arrhythmia.

Selection of probability thresholds for generating cardiac arrhythmia notifications

Techniques are disclosed for monitoring a patient for the occurrence of cardiac arrhythmias. A computing system obtains a cardiac electrogram (EGM) strip for a current patient. Additionally, the computing system may apply a first cardiac rhythm classifier (CRC) with a segment of the cardiac EGM strip as input. The first CRC is trained on training cardiac EGM strips from a first population. The first CRC generates first data regarding an aspect of a cardiac rhythm of the current patient. The computing system may also apply a second CRC with the segment of the cardiac EGM strip as input. The second CRC is trained on training cardiac EGM strips from a smaller, second population. The second CRC generates second data regarding the aspect of the cardiac rhythm of the current patient. The computing system may generate output data based on the first and/or second data.

Personalization of artificial intelligence models for analysis of cardiac rhythms

In some examples, a computing device may receive diagnostic data of a medical device implanted in a patient. The computing device may determine a use case associated with analyzing the diagnostic data out of a plurality of use cases for analyzing the diagnostic data. The computing device may determine, based at least in part on the use case, one or more device characteristics data to be compared against the diagnostic data. The computing device may analyze, based at least in part on comparing the diagnostic data with the one or more device characteristics data, the diagnostic data to determine an operating status of the medical device.

System and method for core-device monitoring

This disclosure is directed to devices, systems, and techniques for identifying a respiration rate based on an impedance signal. In some examples, a medical device system includes a medical device including a plurality of electrodes. The medical device is configured to perform, using the plurality of electrodes, an impedance measurement to collect a set of impedance values, where the set of impedance values is indicative of a respiration pattern of a patient. Additionally, the medical device system includes processing circuitry configured to identify a set of positive zero crossings based on the set of impedance values, identify a set of negative zero crossings based on the set of impedance values, and determine, for the impedance measurement, a value of a respiration metric using both the set of negative zero crossings and the set of positive zero crossings.

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Singapore, Singapore

Niranjan Chakravarthy