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：US11596342B2

公开(公告)日：2023-03-07

申请号：US16909778

申请日：2020-06-23

Applicant: Medtronic, Inc.

Inventor： Andrew Radtke ,  Tarek D. Haddad ,  Michelle M. Galarneau ,  Vinod Sharma ,  Jeffrey D. Wilkinson ,  Brian B. Lee ,  Eduardo N. Warman

IPC: A61B5/341 ,  A61B5/11 ,  G06K9/62 ,  A61B5/0215 ,  A61B5/00

Abstract: Techniques are disclosed for automatically calibrating a reference orientation of an implantable medical device (IMD) within a patient. In one example, sensors of an IMD sense a plurality of orientation vectors of the IMD with respect to a gravitational field. Processing circuitry of the IMD processes the plurality of orientation vectors to identify an upright vector that corresponds to an upright posture of the patient. The processing circuitry classifies the plurality of orientation vectors with respect to the upright vector to define a sagittal plane of the patient and a transverse plane of the patient. The processing circuitry determines, based on the upright vector, the sagittal plane, and the transverse plane, a reference orientation of the IMD within the patient. As the orientation of the IMD within the patient changes over time, the processing circuitry may recalibrate its reference orientation and accurately detect a posture of the patient.

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.

公开(公告)号：US12083345B2

公开(公告)日：2024-09-10

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

CPC classification number: A61N1/365 ,  A61N1/3621 ,  A61N1/37223 ,  A61N1/37235

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.

公开(公告)号：US20240252123A1

公开(公告)日：2024-08-01

申请号：US18421457

申请日：2024-01-24

Applicant: Medtronic, Inc.

Inventor： Keara A. Berlin ,  Brandon D. Stoick ,  Andrew P. Radtke ,  Kelvin Mei ,  Brett D. Jackson ,  Donald R. Musgrove ,  Tarek D. Haddad ,  Wade M. Demmer ,  Shantanu Sarkar ,  Yong K. Cho

IPC: A61B5/00 ,  A61B5/0538

CPC classification number: A61B5/746 ,  A61B5/0538 ,  A61B5/6847 ,  A61B5/7275 ,  A61B2560/0252

Abstract: A method uses internal patient data from an implantable medical device (IMD) and environmental factor information associated with cardiovascular diseases as input to a predictive cardiovascular disease software tool for enabling alerts, e.g., generated by a computing system configured to receive data from the IMD. A computing system may receive diagnostic metric data from the IMID and time correlated location data of the patient, e.g., from a smartphone, smartwatch, or other computing device of the user. The computing system may use the patient's location, such as from a user's device such a programmer or patient's computing device, to determine a particulate matter exposure level corresponding to the diagnostic metric data that may then be used as an input to a predictive cardiovascular disease software tool to refine the risk score or risk stratification.

公开(公告)号：US20240062856A1

公开(公告)日：2024-02-22

申请号：US18261019

申请日：2022-02-07

Applicant: Medtronic, Inc.

Inventor： Tarek D. Haddad ,  Lawrence C. Johnson ,  Chris K. Reedy ,  Joey 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 ,  Jan Audrey Loleng San Diego ,  Hannah Rose Griebel ,  Vivian Wing See To ,  Evan J. Stanelle ,  Rahul Kanwar ,  Dana M. Soderlund

IPC: G16H10/20 ,  A61B5/361 ,  A61B5/00

CPC classification number: G16H10/20 ,  A61B5/361 ,  A61B5/0006 ,  A61B5/0022 ,  A61B5/686 ,  A61B5/74 ,  A61B2560/0468 ,  A61B2560/0487

Abstract: A method for processing patient data includes prompting a patient to complete a survey based on one or more of data received from an implantable medical device, a first time from an enrollment in a study related to the implantable medical device, a second time since a last survey, a medical event, or a detection of the patient in a geofenced area. The method may further include receiving input from the patient in response to the survey, and sending the input from the patient to a database.

公开(公告)号：US11776691B2

公开(公告)日：2023-10-03

申请号：US16845996

申请日：2020-04-10

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 ,  A61B5/076 ,  A61B5/339 ,  A61B5/349 ,  A61B5/686 ,  G06N5/04 ,  G06N20/00 ,  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：US11633159B2

公开(公告)日：2023-04-25

申请号：US16850618

申请日：2020-04-16

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

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.