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：US20220266036A1

公开(公告)日：2022-08-25

申请号：US17587275

申请日：2022-01-28

Applicant: Medtronic, Inc.

Inventor： Wade M. Demmer ,  Tarek D. Haddad

IPC: A61N1/37 ,  A61N1/372 ,  G16H40/60

Abstract: A medical system including processing circuitry configured to operably couple to an imaging device configured to generate an image of an apparatus within a heart of a patient. The apparatus may be configured to establish conduction system pacing (CSP) of the heart using the electrode. The processing circuitry is configured to receive image data representative of the generated image from the imaging device and generate an output readable by a clinician indicating a likelihood of successful conduction system pacing based on electrode position data indicative of a position of the electrode within the heart. The processing circuitry is configured to determine the likelihood of success using a machine learning algorithm trained with a training data set indicative of successful electrode placements within an anatomical heart.

公开(公告)号：US20210128925A1

公开(公告)日：2021-05-06

申请号：US17086127

申请日：2020-10-30

Applicant: Medtronic, Inc.

Inventor： Subham Ghosh ,  Tarek D. Haddad ,  Marc C. Steckler ,  Karen J. Kleckner ,  Elizabeth A. Schotzko

IPC: A61N1/365 ,  A61N1/368 ,  A61N1/37

Abstract: Systems and methods are described herein for evaluation and adjustment cardiac therapy. The systems and methods may initially evaluate a first pacing parameter while other pacing parameters are fixed to, for example, nominal values, and determine an effective setting for the first pacing parameter. Then, a second pacing parameter may be evaluated while the first pacing parameter is fixed to the previously-determined effective setting. Each evaluation may not test every possible setting for the pacing parameters, and instead, may utilize various processes to limit the settings to a subset of settings to test.

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：US20240047072A1

公开(公告)日：2024-02-08

申请号：US18264507

申请日：2022-02-07

Applicant: Medtronic, Inc.

Inventor： Tarek D. Haddad ,  Lawrence C. Johnson ,  Chris K. Reedy ,  Joe J. Hendrickson ,  Manish K. Singh ,  Kevin Joseph Pochatila ,  Nirav A. Patel ,  Linda Z. Massie ,  Noreli C. Franco ,  Michael Erich Jordan ,  Adam V. Dewing ,  Vamshi Poornima Yerrapragada Durga ,  Katy A. Muckala ,  Sairaghunath B. Godithi ,  Evan J. Stanelle ,  Rahul Kanwar ,  Dana M. Soderlund ,  Jeff Lande

IPC: G16H50/30 ,  A61B5/11 ,  A61B5/00

CPC classification number: G16H50/30 ,  A61B5/1118 ,  A61B5/686 ,  A61B5/7267 ,  A61B5/7282

Abstract: A system comprises processing circuitry configured to receive parametric data for a plurality of parameters of a patient. The parametric data is generated by one or more sensing devices of the patient based on physiological signals of the patient sensed by the one or more sensing devices. The plurality of parameters comprise AF burden. The processing circuitry is configured to derive one or more features based on the parametric data for the plurality of parameters, wherein the one or more features comprise at least one AF burden pattern feature, apply the one or more features to a model, and determine a risk level of a health event for the patient based on the application of the one or more features to the model.

公开(公告)号：US11642533B2

公开(公告)日：2023-05-09

申请号：US17086127

申请日：2020-10-30

Applicant: Medtronic, Inc.

Inventor： Subham Ghosh ,  Tarek D. Haddad ,  Marc C. Steckler ,  Karen J. Kleckner ,  Elizabeth A. Schotzko

IPC: A61N1/365 ,  A61N1/368 ,  A61N1/37 ,  A61N1/04

CPC classification number: A61N1/36592 ,  A61N1/3682 ,  A61N1/371 ,  A61N1/3702

Abstract: Systems and methods are described herein for evaluation and adjustment cardiac therapy. The systems and methods may initially evaluate a first pacing parameter while other pacing parameters are fixed to, for example, nominal values, and determine an effective setting for the first pacing parameter. Then, a second pacing parameter may be evaluated while the first pacing parameter is fixed to the previously-determined effective setting. Each evaluation may not test every possible setting for the pacing parameters, and instead, may utilize various processes to limit the settings to a subset of settings to test.

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：US20220023626A1

公开(公告)日：2022-01-27

申请号：US17383170

申请日：2021-07-22

Applicant: Medtronic, Inc.

Inventor： Tarek D. Haddad ,  Donald R. Musgrove ,  Andrew Radtke ,  Eric D. Corndorf ,  Paul J. DeGroot

IPC: A61N1/365 ,  A61N1/362 ,  A61N1/372

Abstract: Techniques are disclosed for a multi-tier system for delivering therapy to a patient. In one example, a first device senses parametric data for a patient and determines, based on a first analysis of the parametric data, that the patient is experiencing a treatable event. In response, the first device establishes wireless communication with a second device and transmits the parametric data to the second device. The second device verifies, based on a second analysis of the parametric data, whether the patient is experiencing the treatable event. The second device selects, based on the second analysis of the parametric data, an instruction for responding to the treatable event and transmits the instruction for responding to the treatable event to the first device. In some examples, in response to receiving the instruction, the first device aborts delivery of therapy for the treatable event or proceeds with delivering therapy for the treatable event.