Medtronic Monitoring, Inc.

Medtronic, Inc.

Greatbatch Ltd.

Corventis, Inc.

Greatbach Ltd.

Neurowave Systems Inc.

Niranjan Chakravarthy

Rodolphe Katra

Tarek D Haddad

Andrew Radtke

Donald R Musgrove

Lindsay A Pedalty

Imad Libbus

Siddharth Dani

Scott Williams

Abhi Chavan

Eduardo N Warman

Brion C Finlay

Tyler Stigen

Rao Gudivada

Tatjana Zikov

Stéphane Bibian

Arthur Lai

John C Doerfler

Matthew Edelman

Stèphane Bibian

Amie K Bucksa

The present invention relates to a physiological monitor and system, more particularly to an electroencephalogram (EEG) monitor and system, and a method of detecting the presence and absence of artifacts and possibly removing artifacts from an EEG, other physiological signal or sensor signal without corrupting or compromising the signal. The accurate and real-time detection of the presence or absence of artifacts and removal of artifacts in an EEG or other signal allows for increased reliability in the efficacy of those signals. The strategy of rejecting artifact-corrupted EEG can result in unacceptable data loss, and asking subjects to minimize movements in order to minimize artifacts is not always feasible. The present invention allows for increased accuracy in detection and removal of artifacts from physiological signals, substantially in real time, and without the loss or corruption of signal or data in order to increase the accuracy of such signals for diagnosis and treatment purposes.

Detector for identifying physiological artifacts from physiological signals and method

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

A medical device is utilized to monitor physiological parameters of a patient and capture segments of the monitored physiological parameters. The medical device includes circuitry configured to monitor one or more physiological parameters associated with the patient and an analysis module that includes a buffer and a processor. The buffer stores monitored physiological parameters and the processor analyzes the monitored physiological parameters and triggers capture of segments from the buffer in response to a triggering criteria being satisfied. The analysis module selects a pre-trigger duration based at least in part on the triggering criteria.

System and method for onset/offset capture

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

Techniques are disclosed for explaining and visualizing an output of a machine learning system that detects cardiac arrhythmia in a patient. In one example, a computing device receives cardiac electrogram data sensed by a medical device. The computing device applies a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the received cardiac electrogram data to determine, based on the machine learning model, that an episode of arrhythmia has occurred in the patient and a level of confidence in the determination that the episode of arrhythmia has occurred in the patient. In response to determining that the level of confidence is greater than a predetermined threshold, the computing device displays, to a user, a portion of the cardiac electrogram data, an indication that the episode of arrhythmia has occurred, and an indication of the level of confidence that the episode of arrhythmia has occurred.

Visualization of arrhythmia detection by machine learning

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

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 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

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 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 preparing data for use in artificial intelligence (AI)-based cardiac arrhythmia detection. In accordance with the techniques of this disclosure, a computing system may obtain a cardiac electrogram (EGM) strip that represents a waveform of a cardiac rhythm of a same patient. Additionally, the computing system may preprocess the cardiac EGM strip. The computing system may then apply a deep learning model to the preprocessed cardiac EGM strip to generate arrhythmia data indicating whether the cardiac EGM strip represents one or more occurrences of one or more cardiac arrhythmias.

Data preparation for artificial intelligence-based cardiac arrhythmia detection

Techniques are disclosed for using feature delineation to reduce the impact of machine learning cardiac arrythmia 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 arrythmia 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 arrythmia.

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.

Growing community of inventors

Eden Prairie, MN, United States of America

Niranjan Chakravarthy