公开(公告)号：US20210358631A1

公开(公告)日：2021-11-18

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: 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：US20210338134A1

公开(公告)日：2021-11-04

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 ,  A61B5/316 ,  G16H10/60

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：US12262153B2

公开(公告)日：2025-03-25

申请号：US17215419

申请日：2021-03-29

Applicant: Medtronic, Inc.

Inventor： Rodolphe Katra ,  Amie Bucksa ,  Niranjan Chakravarthy

IPC: H04N7/18 ,  A61B90/00 ,  G06N20/00 ,  G06T7/00 ,  G16H10/60 ,  G16H30/20 ,  G16H30/40 ,  G16H40/40 ,  G16H40/67 ,  G16H50/70 ,  H04N23/62 ,  A61N1/372 ,  G16H15/00 ,  G16H40/20 ,  G16H50/20

Abstract: 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.

公开(公告)号：US12161487B2

公开(公告)日：2024-12-10

申请号：US18304696

申请日：2023-04-21

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：US12112848B2

公开(公告)日：2024-10-08

申请号：US18365748

申请日：2023-08-04

Applicant: Medtronic, Inc.

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

IPC: G16H40/40

CPC classification number: 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：US20240029891A1

公开(公告)日：2024-01-25

申请号：US18479228

申请日：2023-10-02

Applicant: Medtronic, Inc.

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

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

CPC classification number: G16H50/20 ,  G06N20/00 ,  G06N5/04 ,  A61B5/076 ,  A61B5/686 ,  A61B5/339 ,  A61B5/349 ,  A61B5/7267

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：US20230290512A1

公开(公告)日：2023-09-14

申请号：US18320522

申请日：2023-05-19

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

CPC classification number: G16H50/20 ,  A61B5/7264 ,  A61B5/1116 ,  G16H50/30 ,  A61B5/1118 ,  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 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.

公开(公告)号：US11742077B2

公开(公告)日：2023-08-29

申请号：US16876768

申请日：2020-05-18

Applicant: Medtronic, Inc.

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

IPC: G16H40/40

CPC classification number: 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：US20230248319A1

公开(公告)日：2023-08-10

申请号：US18304696

申请日：2023-04-21

Applicant: Medtronic, Inc.

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

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

CPC classification number: A61B5/7267 ,  A61B5/35 ,  A61B5/341 ,  A61B5/0006 ,  A61B5/686

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：US11694804B2

公开(公告)日：2023-07-04

申请号：US16851603

申请日：2020-04-17

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

CPC classification number: G16H50/20 ,  A61B5/1116 ,  A61B5/1118 ,  A61B5/316 ,  A61B5/35 ,  A61B5/7264 ,  G06N5/02 ,  G06N5/04 ,  G06N20/00 ,  G16H50/30

Abstract: 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.