Patents by Inventor Niranjan Chakravarthy
Niranjan Chakravarthy has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240029891Abstract: 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.Type: ApplicationFiled: October 2, 2023Publication date: January 25, 2024Inventors: Tarek D. Haddad, Niranjan Chakravarthy, Donald R. Musgrove, Andrew Radtke, Eduardo N. Warman, Rodolphe Katra, Lindsay A. Pedalty
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Publication number: 20240000362Abstract: A medical device is utilized to monitor physiological parameters of a patient and capture segments of the monitored physiological parameters. The medical device includes circuitry configured to monitor one or more physiological parameters associated with the patient and an analysis module that includes a buffer and a processor. The buffer stores monitored physiological parameters and the processor analyzes the monitored physiological parameters and triggers capture of segments from the buffer in response to a triggering criteria being satisfied. The analysis module selects a pre-trigger duration based at least in part on the triggering criteria.Type: ApplicationFiled: September 18, 2023Publication date: January 4, 2024Inventors: Rodolphe Katra, Scott Williams, Niranjan Chakravarthy
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Publication number: 20230377737Abstract: 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.Type: ApplicationFiled: August 4, 2023Publication date: November 23, 2023Inventors: John C. Doerfler, Rodolphe Katra, Niranjan Chakravarthy
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Publication number: 20230329624Abstract: 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.Type: ApplicationFiled: June 16, 2023Publication date: October 19, 2023Inventors: Lindsay A. Pedalty, Niranjan Chakravarthy, Rodolphe Katra, Tarek D. Haddad, Andrew Radtke, Siddharth Dani, Donald R. Musgrove
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Publication number: 20230320648Abstract: 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.Type: ApplicationFiled: June 8, 2023Publication date: October 12, 2023Inventors: Niranjan Chakravarthy, Siddharth Dani, Tarek D. Haddad, Donald R. Musgrove, Andrew Radtke, Eduardo N. Warman, Rodolphe Katra, Lindsay A. Pedalty
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Patent number: 11776691Abstract: 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.Type: GrantFiled: April 10, 2020Date of Patent: October 3, 2023Assignee: Medtronic, Inc.Inventors: Tarek D. Haddad, Niranjan Chakravarthy, Donald R. Musgrove, Andrew Radtke, Eduardo N. Warman, Rodolphe Katra, Lindsay A. Pedalty
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Patent number: 11759139Abstract: A medical device is utilized to monitor physiological parameters of a patient and capture segments of the monitored physiological parameters. The medical device includes circuitry configured to monitor one or more physiological parameters associated with the patient and an analysis module that includes a buffer and a processor. The buffer stores monitored physiological parameters and the processor analyzes the monitored physiological parameters and triggers capture of segments from the buffer in response to a triggering criteria being satisfied. The analysis module selects a pre-trigger duration based at least in part on the triggering criteria.Type: GrantFiled: April 5, 2019Date of Patent: September 19, 2023Assignee: Medtronic Monitoring, Inc.Inventors: Rodolphe Katra, Scott Williams, Niranjan Chakravarthy
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Publication number: 20230290512Abstract: 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.Type: ApplicationFiled: May 19, 2023Publication date: September 14, 2023Inventors: Niranjan Chakravarthy, Siddharth Dani, Tarek D. Haddad, Donald R. Musgrove, Andrew Radtke, Rodolphe Katra, Lindsay A. Pedalty
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Patent number: 11742077Abstract: 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.Type: GrantFiled: May 18, 2020Date of Patent: August 29, 2023Assignee: Medtronic, Inc.Inventors: John C. Doerfler, Rodolphe Katra, Niranjan Chakravarthy
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Patent number: 11723577Abstract: 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.Type: GrantFiled: April 16, 2020Date of Patent: August 15, 2023Assignee: Medtronic, Inc.Inventors: Lindsay A. Pedalty, Niranjan Chakravarthy, Rodolphe Katra, Tarek D. Haddad, Andrew Radtke, Siddharth Dani, Donald R. Musgrove
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Publication number: 20230248319Abstract: 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.Type: ApplicationFiled: April 21, 2023Publication date: August 10, 2023Inventors: Niranjan Chakravarthy, Siddharth Dani, Tarek D. Haddad, Rodolphe Katra, Donald R. Musgrove, Lindsay A. Pedalty, Andrew Radtke
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Patent number: 11696718Abstract: 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.Type: GrantFiled: July 12, 2021Date of Patent: July 11, 2023Assignee: Medtronic, Inc.Inventors: Niranjan Chakravarthy, Siddharth Dani, Tarek D. Haddad, Donald R. Musgrove, Andrew Radtke, Eduardo N. Warman, Rodolphe Katra, Lindsay A. Pedalty
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Patent number: 11694804Abstract: 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.Type: GrantFiled: April 17, 2020Date of Patent: July 4, 2023Assignee: Medtronic, Inc.Inventors: Niranjan Chakravarthy, Siddharth Dani, Tarek D. Haddad, Donald R. Musgrove, Andrew Radtke, Rodolphe Katra, Lindsay A. Pedalty
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Publication number: 20230149726Abstract: 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.Type: ApplicationFiled: January 18, 2023Publication date: May 18, 2023Inventors: Siddharth Dani, Tarek D. Haddad, Donald R. Musgrove, Andrew Radtke, Niranjan Chakravarthy, Rodolphe Katra, Lindsay A. Pedalty
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Patent number: 11633159Abstract: 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.Type: GrantFiled: April 16, 2020Date of Patent: April 25, 2023Assignee: MEDTRONIC, INC.Inventors: Niranjan Chakravarthy, Siddharth Dani, Tarek D. Haddad, Rodolphe Katra, Donald R. Musgrove, Lindsay A. Pedalty, Andrew Radtke
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Patent number: 11617533Abstract: 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.Type: GrantFiled: July 16, 2021Date of Patent: April 4, 2023Assignee: Medtronic, Inc.Inventors: Lindsay A. Pedalty, Niranjan Chakravarthy, Rodolphe Katra, Tarek D. Haddad, Andrew Radtke, Siddharth Dani, Donald R. Musgrove
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Publication number: 20230075140Abstract: Techniques are disclosed for detecting arrhythmia episodes for a patient. A medical device may receive one or more sensor values indicative of motion of a patient. The medical device may determine, based at least in part on the one or more sensor values, an activity level of the patient. The medical device may determine a heart rate threshold for triggering detection of an arrhythmia episode based at least in part on the activity level of the patient. The medical device may determine whether to trigger detection of the arrhythmia episode for the patient based at least in part on comparing a heart rate of the patient with the heart rate threshold. The medical device may, in response to triggering detection of the arrhythmia episode, collect information associated with the arrhythmia episode.Type: ApplicationFiled: November 11, 2022Publication date: March 9, 2023Inventors: Niranjan Chakravarthy, Rodolphe Katra
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Patent number: 11583687Abstract: 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.Type: GrantFiled: April 16, 2020Date of Patent: February 21, 2023Assignee: Medtronic, Inc.Inventors: Siddharth Dani, Tarek D. Haddad, Donald R. Musgrove, Andrew Radtke, Niranjan Chakravarthy, Rodolphe Katra, Lindsay A. Pedalty
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Patent number: 11576605Abstract: A medical device is utilized to monitor physiological parameters of a patient and capture segments of the monitored physiological parameters. The medical device includes circuitry configured to monitor one or more physiological parameters associated with the patient and an analysis module that includes a buffer and a processor. The buffer stores monitored physiological parameters and the processor analyzes the monitored physiological parameters and triggers capture of segments from the buffer in response to a triggering criteria being satisfied. The analysis module selects a pre-trigger duration based at least in part on the triggering criteria.Type: GrantFiled: June 8, 2022Date of Patent: February 14, 2023Assignee: Medtronic Monitoring, Inc.Inventors: Rodolphe Katra, Scott Williams, Niranjan Chakravarthy
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Patent number: 11504048Abstract: Techniques are disclosed for detecting arrhythmia episodes for a patient. A medical device may receive one or more sensor values indicative of motion of a patient. The medical device may determine, based at least in part on the one or more sensor values, an activity level of the patient. The medical device may determine a heart rate threshold for triggering detection of an arrhythmia episode based at least in part on the activity level of the patient. The medical device may determine whether to trigger detection of the arrhythmia episode for the patient based at least in part on comparing a heart rate of the patient with the heart rate threshold. The medical device may, in response to triggering detection of the arrhythmia episode, collect information associated with the arrhythmia episode.Type: GrantFiled: June 10, 2020Date of Patent: November 22, 2022Assignee: Medtronic Monitoring, Inc.Inventors: Niranjan Chakravarthy, Rodolphe Katra