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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.
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22.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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Patent Agency Ranking
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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26.发明申请公开(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
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：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
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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