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公开(公告)号：US11723577B2
公开(公告)日：2023-08-15
申请号：US16850749
申请日：2020-04-16
Applicant: Medtronic, Inc.
Inventor： Lindsay A. Pedalty , Niranjan Chakravarthy , Rodolphe Katra , Tarek D. Haddad , Andrew Radtke , Siddharth Dani , Donald R. Musgrove
CPC classification number: A61B5/361 , A61B5/316 , A61B5/322 , A61B5/346 , A61B5/352 , A61B5/363 , A61B5/7264 , A61B5/742 , A61B5/7405
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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公开(公告)号：US11583687B2
公开(公告)日：2023-02-21
申请号：US16850833
申请日：2020-04-16
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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公开(公告)号：US11443852B2
公开(公告)日：2022-09-13
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：US20220160310A1
公开(公告)日：2022-05-26
申请号：US17103432
申请日：2020-11-24
Applicant: Medtronic, Inc.
Inventor： Pranam Shetty , Niranjan Chakravarthy , Maneesh Shrivastav , Rodolphe Katra , Thomas Piaget , Arthur K. Lai
Abstract: This disclosure is directed to techniques for recording and recognizing physiological parameter patterns associated with symptoms. A medical device system includes a medical device including one or more sensors configured to generate a signal that indicates a parameter of a patient. Additionally, the medical device system includes processing circuitry configured to receive data indicative of a user indication of an experienced symptom; determine a plurality of parameter values of the parameter based on a portion of the signal corresponding to a period of time including a time before the user indication and a period of time after the user indication. Additionally, the processing circuitry is configured to identify, based on a reference set of parameter values of the plurality of parameter values, the experienced symptom. Additionally, the processing circuitry is configured to save, to a database in memory, a set of data including the experienced symptom and patient parameters.
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公开(公告)号：US11998303B2
公开(公告)日：2024-06-04
申请号：US16450250
申请日：2019-06-24
Applicant: Medtronic, Inc.
Inventor： Shantanu Sarkar , Eric M. Christensen , Deborah Ann Jaye , Niranjan Chakravarthy , Geert Morren , Jerry D. Reiland
CPC classification number: A61B5/0205 , A61B5/1118 , A61B5/364 , A61B5/4561 , A61B5/024 , A61B5/0809 , A61B5/0816 , A61B2562/0204
Abstract: This disclosure is directed to devices, systems, and techniques for identifying a respiration rate based on an impedance signal. In some examples, a medical device system includes a medical device including a plurality of electrodes. The medical device is configured to perform, using the plurality of electrodes, an impedance measurement to collect a set of impedance values, where the set of impedance values is indicative of a respiration pattern of a patient. Additionally, the medical device system includes processing circuitry configured to identify a set of positive zero crossings based on the set of impedance values, identify a set of negative zero crossings based on the set of impedance values, and determine, for the impedance measurement, a value of a respiration metric using both the set of negative zero crossings and the set of positive zero crossings.
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Patent Agency Ranking
公开(公告)号：US20230377737A1
公开(公告)日：2023-11-23
申请号：US18365748
申请日：2023-08-04
Applicant: Medtronic, Inc.
Inventor： John C. Doerfler , Rodolphe Katra , Niranjan Chakravarthy
IPC: G16H40/40
CPC classification number: G16H40/40
Abstract: In some examples, a computing device may receive diagnostic data of a medical device implanted in a patient. The computing device may determine a use case associated with analyzing the diagnostic data out of a plurality of use cases for analyzing the diagnostic data. The computing device may determine, based at least in part on the use case, one or more device characteristics data to be compared against the diagnostic data. The computing device may analyze, based at least in part on comparing the diagnostic data with the one or more device characteristics data, the diagnostic data to determine an operating status of the medical device.
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17.发明授权公开(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
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.
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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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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
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.
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