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：US11756681B2

公开(公告)日：2023-09-12

申请号：US16863590

申请日：2020-04-30

Applicant: Medtronic, Inc.

Inventor： Rodolphe Katra ,  Andrew C. Frye ,  Michael Jordan

IPC: G16H30/20 ,  G16H40/63 ,  G16H15/00

CPC classification number: G16H40/63 ,  G16H15/00 ,  G16H30/20

Abstract: Techniques for remote monitoring of a patient and corresponding medical device(s) are described. The remote monitoring comprises providing an interactive session configured to allow a user to navigate a plurality of sub sessions, determining a first set of data items in accordance with a first subsession, the first set of data items including the image data, determining a second set of data items in accordance with a second subsession of the interactive session, determining, based at least in part on the first set of data items and the second set of data items, an abnormality, and outputting a post-implant report of the interactive session.

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.

公开(公告)号：US20230149726A1

公开(公告)日：2023-05-18

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 ,  G16H50/50 ,  G16H10/60 ,  G16H50/20 ,  G16H40/63 ,  A61N1/365

CPC classification number: A61N1/3956 ,  G16H50/50 ,  G16H10/60 ,  G16H50/20 ,  G16H40/63 ,  A61N1/36592

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：US20210344880A1

公开(公告)日：2021-11-04

申请号：US17215419

申请日：2021-03-29

Applicant: Medtronic, Inc.

Inventor： Rodolphe Katra ,  Amie Bucksa ,  Niranjan Chakravarthy

IPC: H04N7/18 ,  G16H10/60 ,  G16H40/67 ,  G16H30/20 ,  G16H30/40 ,  G16H50/70 ,  G16H40/40 ,  G06N20/00 ,  G06T7/00 ,  H04N5/232 ,  A61B90/00

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：US20210338138A1

公开(公告)日：2021-11-04

申请号：US17377785

申请日：2021-07-16

Applicant: Medtronic, Inc.

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

IPC: A61B5/361 ,  A61B5/00 ,  A61B5/316 ,  A61B5/322 ,  A61B5/352 ,  A61B5/363

Abstract: Techniques are disclosed for explaining and visualizing an output of a machine learning system that detects cardiac arrythmia 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.