Donald Eric Musgrove

US Patent:

20210338134, Nov 4, 2021

Filed:

Jul 12, 2021

Appl. No.:

17/373480

Inventors:

- Minneapolis MN, US
Siddharth Dani - Minneapolis MN, US
Tarek D. Haddad - Minneapolis MN, US
Donald R. Musgrove - Minneapolis MN, US
Andrew Radtke - Minneapolis MN, US
Eduardo N. Warman - Maple Grove MN, US
Rodolphe Katra - Blaine MN, US
Lindsay A. Pedalty - Minneapolis MN, US

International Classification:

A61B 5/349
A61B 5/316
G16H 10/60

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.

US Patent:

20210338138, Nov 4, 2021

Filed:

Jul 16, 2021

Appl. No.:

17/377785

Inventors:

- Minneapolis MN, US
Niranjan Chakravarthy - Singapore, SG
Rodolphe Katra - Blaine MN, US
Tarek D. Haddad - Minneapolis MN, US
Andrew Radtke - Minneapolis MN, US
Siddharth Dani - Minneapolis MN, US
Donald R. Musgrove - Minneapolis MN, US

International Classification:

A61B 5/361
A61B 5/00
A61B 5/316
A61B 5/322
A61B 5/352
A61B 5/363

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.

US Patent:

20210343416, Nov 4, 2021

Filed:

Jul 16, 2021

Appl. No.:

17/377763

Inventors:

- Minneapolis MN, US
Siddharth Dani - Minneapolis MN, US
Tarek D. Haddad - Minneapolis MN, US
Donald R. Musgrove - Minneapolis MN, US
Andrew Radtke - Minneapolis MN, US
Rodolphe Katra - Blaine MN, US
Lindsay A. Pedalty - Minneapolis MN, US

International Classification:

G16H 50/20
A61B 5/00
A61B 5/11
G16H 50/30
G06N 20/00
G06N 5/04
G06N 5/02
A61B 5/35
A61B 5/316

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.

US Patent:

20200352462, Nov 12, 2020

Filed:

Apr 16, 2020

Appl. No.:

16/850749

Inventors:

- Minneapolis MN, US
Niranjan Chakravarthy - Singapore, SG
Rodolphe Katra - Blaine MN, US
Tarek D. Haddad - Minneapolis MN, US
Andrew Radtke - Minneapolis MN, US
Siddharth Dani - Minneapolis MN, US
Donald R. Musgrove - Minneapolis MN, US

International Classification:

A61B 5/046
A61B 5/00
A61B 5/04
A61B 5/0464
A61B 5/0456
A61B 5/0402

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.

US Patent:

20200352466, Nov 12, 2020

Filed:

Apr 16, 2020

Appl. No.:

16/850699

Inventors:

- Minneapolis MN, US
Siddharth Dani - Minneapolis MN, US
Tarek D. Haddad - Minneapolis MN, US
Donald R. Musgrove - Minneapolis MN, US
Andrew Radtke - Minneapolis MN, US
Eduardo N. Warman - Maple Grove MN, US
Rodolphe Katra - Blaine MN, US
Lindsay A. Pedalty - Minneapolis MN, US

International Classification:

A61B 5/0452
A61B 5/04
G16H 10/60

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.

US Patent:

20200352522, Nov 12, 2020

Filed:

Apr 16, 2020

Appl. No.:

16/850618

Inventors:

- Minneapolis MN, US
Siddharth Dani - Minneapolis MN, US
Tarek D. Haddad - Minneapolis MN, US
Rodolphe Katra - Blaine MN, US
Donald R. Musgrove - Minneapolis MN, US
Lindsay A. Pedalty - Minneapolis MN, US
Andrew Radtke - Minneapolis MN, US

International Classification:

A61B 5/00
A61B 5/0452
A61B 5/04

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.

US Patent:

20200353271, Nov 12, 2020

Filed:

Apr 16, 2020

Appl. No.:

16/850833

Inventors:

- Minneapolis MN, US
Tarek D. Haddad - Minneapolis MN, US
Donald R. Musgrove - Minneapolis MN, US
Andrew Radtke - Minneapolis MN, US
Niranjan Chakravarthy - Singapore, SG
Rodolphe Katra - Blaine MN, US
Lindsay A. Pedalty - Minneapolis MN, US

International Classification:

A61N 1/39
A61N 1/365
G16H 10/60
G16H 40/63
G16H 50/50
G16H 50/20

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.

US Patent:

20200357517, Nov 12, 2020

Filed:

Apr 10, 2020

Appl. No.:

16/845996

Inventors:

- Minneapolis MN, US
Niranjan Chakravarthy - Singapore, SG
Donald R. Musgrove - Minneapolis MN, US
Andrew Radtke - Minneapolis MN, US
Eduardo N. Warman - Maple Grove MN, US
Rodolphe Katra - Blaine MN, US
Lindsay A. Pedalty - Minneapolis MN, US

International Classification:

G16H 50/20
G06N 20/00
G06N 5/04
A61B 5/00
A61B 5/07
A61B 5/044
A61B 5/0452

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.

Address:

4038 John Johnston Rd, Mc Intosh, AL 36553

VIN:

2G1FC1EV8A9183868

Make:

CHEVROLET

Model:

CAMARO

Year:

2010

Address:

4038 John Johnston Rd, Mc Intosh, AL 36553

VIN:

1ZVFT80N575278026

Make:

FORD

Model:

MUSTANG

Year:

2007