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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.
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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
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.
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Patent Agency Ranking
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.
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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
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.
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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
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.
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公开(公告)号：US20170056663A1
公开(公告)日：2017-03-02
申请号：US15342167
申请日：2016-11-03
Applicant: Medtronic, Inc.
Inventor： William F. Kaemmerer , Siddharth Dani , Allison T. Connolly
CPC classification number: A61N1/36185 , A61N1/0534 , A61N1/36064 , A61N1/36067 , A61N1/36075 , A61N1/36096 , A61N1/36139 , A61N1/37235 , A61N1/37247
Abstract: In one example, a method includes selecting, by one or more processors and based on a representation of sensed electrical signals for a particular patient and a plurality of representations of sensed electrical signals for a plurality of other patients, a combination of electrodes of a plurality of combinations of one or more implantable electrodes for delivery of electrical stimulation to the particular patient.
Abstract translation: 在一个示例中，一种方法包括由一个或多个处理器和基于特定患者的感测电信号的表示以及多个其他患者的感测电信号的多个表示来选择多个电极的组合 一个或多个可植入电极的组合，用于向特定患者传递电刺激。
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公开(公告)号：US09498628B2
公开(公告)日：2016-11-22
申请号：US14724327
申请日：2015-05-28
Applicant: Medtronic, Inc.
Inventor： William F. Kaemmerer , Siddharth Dani , Allison T. Connolly
CPC classification number: A61N1/36185 , A61N1/0534 , A61N1/36064 , A61N1/36067 , A61N1/36075 , A61N1/36096 , A61N1/36139 , A61N1/37235 , A61N1/37247
Abstract: In one example, a method includes selecting, by one or more processors and based on a representation of sensed electrical signals for a particular patient and a plurality of representations of sensed electrical signals for a plurality of other patients, a combination of electrodes of a plurality of combinations of one or more implantable electrodes for delivery of electrical stimulation to the particular patient.
Abstract translation: 在一个示例中，一种方法包括由一个或多个处理器和基于特定患者的感测电信号的表示以及多个其他患者的感测电信号的多个表示来选择多个电极的组合 一个或多个可植入电极的组合，用于向特定患者传递电刺激。
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