公开(公告)号：US11617533B2

公开(公告)日：2023-04-04

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: A61B5/346 ,  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 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.

公开(公告)号：US11475998B2

公开(公告)日：2022-10-18

申请号：US16851500

申请日：2020-04-17

Applicant: Medtronic, Inc.

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

IPC: G16H50/20 ,  G16H40/60

Abstract: 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.

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：US20150320996A1

公开(公告)日：2015-11-12

申请号：US14696242

申请日：2015-04-24

Applicant: Medtronic, Inc.

Inventor： Michael D. Eggen ,  Tarek D. Haddad ,  Paul A. Iaizzo ,  Zhongping Yang

IPC: A61N1/05 ,  A61N1/08

CPC classification number: A61N1/056 ,  A61N1/0573 ,  A61N1/08 ,  A61N2001/083

Abstract: Method and systems of determining adequacy of fixation of a medical lead type having a fixation helix are disclosed. The lead of the medical lead type is placed at a desired location within a patient's body and the fixation helix is screwed into tissue at that location. One or more parameters, associated with the lead, are measured at the location. Based upon the measured one or more parameters, determining a number of turns that the helix is embedded into the tissue at the location.

Abstract translation: 公开了确定具有固定螺旋线的医用引线类型的固定充分性的方法和系统。 医疗引线类型的引线放置在患者体内的期望位置，并且固定螺旋被拧入该位置的组织中。 在该位置测量与引线相关联的一个或多个参数。 基于测量的一个或多个参数，确定螺旋被嵌入到该位置的组织中的匝数。