公开(公告)号：US20250072841A1

公开(公告)日：2025-03-06

申请号：US18951026

申请日：2024-11-18

Applicant: Medtronic, Inc.

Inventor： Niranjan Chakravarthy ,  Siddharth Dani ,  Tarek D. Haddad ,  Rodolphe Katra ,  Donald R. Musgrove ,  Lindsay A. Pedalty ,  Andrew Radtke

IPC: A61B5/00 ,  A61B5/341 ,  A61B5/35

Abstract: 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.

公开(公告)号：US20230320648A1

公开(公告)日：2023-10-12

申请号：US18331756

申请日：2023-06-08

Applicant: Medtronic, Inc.

Inventor： Niranjan Chakravarthy ,  Siddharth Dani ,  Tarek D. Haddad ,  Donald R. Musgrove ,  Andrew Radtke ,  Eduardo N. Warman ,  Rodolphe Katra ,  Lindsay A. Pedalty

IPC: A61B5/349 ,  G16H10/60 ,  A61B5/316

CPC classification number: A61B5/349 ,  A61B5/316 ,  G16H10/60 ,  A61B2560/0214

Abstract: 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.

公开(公告)号：US11696718B2

公开(公告)日：2023-07-11

申请号：US17373480

申请日：2021-07-12

Applicant: Medtronic, Inc.

Inventor： Niranjan Chakravarthy ,  Siddharth Dani ,  Tarek D. Haddad ,  Donald R. Musgrove ,  Andrew Radtke ,  Eduardo N. Warman ,  Rodolphe Katra ,  Lindsay A. Pedalty

IPC: A61B5/349 ,  G16H10/60 ,  A61B5/316

CPC classification number: A61B5/349 ,  A61B5/316 ,  G16H10/60 ,  A61B2560/0214

Abstract: 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.

公开(公告)号：US20210358606A1

公开(公告)日：2021-11-18

申请号：US16876768

申请日：2020-05-18

Applicant: Medtronic, Inc.

Inventor： John C. Doerfler ,  Rodolphe Katra ,  Niranjan Chakravarthy

IPC: G16H40/40

Abstract: 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.

公开(公告)号：US11013436B2

公开(公告)日：2021-05-25

申请号：US16122289

申请日：2018-09-05

Applicant: Medtronic, Inc.

Inventor： John E. Burnes ,  James K. Carney ,  Jonathan L. Kuhn ,  Mark J. Phelps ,  Jesper Svenning Kristensen ,  Rodolphe Katra

IPC: A61B5/1459 ,  A61B5/145 ,  A61B5/0205 ,  A61B5/1473 ,  A61B5/00 ,  A61B5/20 ,  A61N1/375 ,  G01N21/64 ,  A61B5/07 ,  A61B5/08 ,  A61B5/024 ,  G01N21/01 ,  A61M5/172 ,  A61M5/142

Abstract: In some examples, a medical system includes a medical device. The medical device may include a housing configured to be implanted in a target site of a patient, a light emitter configured to emit a signal configured to cause a fluorescent marker to emit a fluoresced signal into the target site, and a light detector that may be configured to detect the fluoresced signal. The medical system may include processing circuitry configured to determine a characteristic of the fluorescent marker based on the emitted signal and the fluoresced signal. The characteristic of the fluorescent marker may be indicative of a presence of a compound in the patient, and the processing circuitry may be configured to track the presence of the compound of the patient based on the characteristic of the fluorescent marker.

公开(公告)号：US12246188B2

公开(公告)日：2025-03-11

申请号：US18155803

申请日：2023-01-18

Applicant: Medtronic, Inc.

Inventor： Siddharth Dani ,  Tarek D. Haddad ,  Donald R. Musgrove ,  Andrew Radtke ,  Niranjan Chakravarthy ,  Rodolphe Katra ,  Lindsay A. Pedalty

IPC: A61N1/39 ,  A61N1/365 ,  G16H10/60 ,  G16H40/63 ,  G16H50/20 ,  G16H50/50

Abstract: 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.

公开(公告)号：US12171554B2

公开(公告)日：2024-12-24

申请号：US18469257

申请日：2023-09-18

Applicant: Medtronic, Inc.

Inventor： John E. Burnes ,  James K. Carney ,  Jonathan L. Kuhn ,  Mark J. Phelps ,  Jesper Svenning Kristensen ,  Rodolphe Katra

IPC: A61B5/1459 ,  A61B5/00 ,  A61B5/0205 ,  A61B5/024 ,  A61B5/07 ,  A61B5/08 ,  A61B5/145 ,  A61B5/1473 ,  A61B5/20 ,  A61M5/142 ,  A61M5/172 ,  A61N1/375 ,  G01N21/01 ,  G01N21/64

Abstract: In some examples, a medical system includes a medical device. The medical device may include a housing configured to be implanted in a target site of a patient, a light emitter configured to emit a signal configured to cause a fluorescent marker to emit a fluoresced signal into the target site, and a light detector that may be configured to detect the fluoresced signal. The medical system may include processing circuitry configured to determine a characteristic of the fluorescent marker based on the emitted signal and the fluoresced signal. The characteristic of the fluorescent marker may be indicative of a presence of a compound in the patient, and the processing circuitry may be configured to track the presence of the compound of the patient based on the characteristic of the fluorescent marker.

公开(公告)号：US20230075140A1

公开(公告)日：2023-03-09

申请号：US18054819

申请日：2022-11-11

Applicant: Medtronic, Inc.

Inventor： Niranjan Chakravarthy ,  Rodolphe Katra

IPC: A61B5/363 ,  A61B5/316 ,  A61B5/0245 ,  A61B5/11 ,  A61B5/00

Abstract: 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.

公开(公告)号：US11355244B2

公开(公告)日：2022-06-07

申请号：US17389831

申请日：2021-07-30

Applicant: Medtronic, Inc.

Inventor： Tarek D. Haddad ,  Niranjan Chakravarthy ,  Donald R. Musgrove ,  Andrew Radtke ,  Eduardo N. Warman ,  Rodolphe Katra ,  Lindsay A. Pedalty

IPC: G06Q50/00 ,  G06T7/00 ,  G16H50/20 ,  G06N20/00 ,  G06N5/04 ,  A61B5/07 ,  A61B5/00 ,  A61B5/339 ,  A61B5/349

Abstract: 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.

公开(公告)号：US20210343416A1

公开(公告)日：2021-11-04

申请号：US17377763

申请日：2021-07-16

Applicant: Medtronic, Inc.

Inventor： Niranjan Chakravarthy ,  Siddharth Dani ,  Tarek D. Haddad ,  Donald R. Musgrove ,  Andrew Radtke ,  Rodolphe Katra ,  Lindsay A. Pedalty

IPC: G16H50/20 ,  A61B5/00 ,  A61B5/11 ,  G16H50/30 ,  G06N20/00 ,  G06N5/04 ,  G06N5/02 ,  A61B5/35 ,  A61B5/316

Abstract: 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.