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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
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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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
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.
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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
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.
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4.发明授权公开(公告)号：US10583293B2
公开(公告)日：2020-03-10
申请号：US14481379
申请日：2014-09-09
Applicant: Medtronic, Inc.
Inventor： Ashutosh Chaturvedi , Siddharth Dani , Timothy J. Denison , William F. Kaemmerer , Shahram Malekkhosravi , Eric J. Panken , Brandon Zingsheim
Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using an efficacy map comprising a plurality of voxels that are each assigned a value. In some examples, the efficacy map is selected from a plurality of stored efficacy maps based on a patient condition, one or more patient symptoms, or both the patient condition and one or more patient symptoms. In addition, in some examples, voxels of the efficacy map are assigned respective values that are associated with a clinical rating scale.
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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
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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Patent Agency Ranking
6.发明授权公开(公告)号：US11648398B2
公开(公告)日：2023-05-16
申请号：US16774418
申请日：2020-01-28
Applicant: Medtronic, Inc.
Inventor： Ashutosh Chaturvedi , Siddharth Dani , Timothy J. Denison , William F. Kaemmerer , Shahram Malekkhosravi , Eric J. Panken , Brandon Zingsheim
IPC: G16H50/50 , G16H20/40 , G16H20/30 , A61B5/291 , G16H50/30 , A61B5/24 , A61N1/36 , A61N1/372 , A61B5/00
CPC classification number: A61N1/36128 , A61B5/291 , A61B5/4064 , A61B5/4833 , A61N1/3605 , A61N1/36067 , A61N1/36082 , A61N1/36096 , A61N1/36132 , A61N1/36182 , A61N1/37247 , G16H20/30 , G16H20/40 , G16H50/50 , A61B5/24 , A61B5/4076 , G16H50/30
Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using an efficacy map comprising a plurality of voxels that are each assigned a value. In some examples, the efficacy map is selected from a plurality of stored efficacy maps based on a patient condition, one or more patient symptoms, or both the patient condition and one or more patient symptoms. In addition, in some examples, voxels of the efficacy map are assigned respective values that are associated with a clinical rating scale.
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公开(公告)号：US20210343416A1
公开(公告)日：2021-11-04
申请号：US17377763
申请日：2021-07-16
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
Abstract: Techniques are disclosed for using feature delineation to reduce the impact of machine learning cardiac arrythmia 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 arrythmia 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 arrythmia.
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公开(公告)号：US20160144186A1
公开(公告)日：2016-05-26
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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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
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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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
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.
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