Visualization of arrhythmia detection by machine learning

US11723577B2 · kind B2 · utility

Assignee

• Medtronic, Inc. · US

Inventors

• Lindsay A. Pedalty · Minneapolis, US
• Niranjan Chakravarthy · Eden Prairie, US
• Rodolphe Katra · Blaine, US
• Tarek D. Haddad · Minneapolis, US
• Andrew Radtke · Minneapolis, US
• Siddharth Dani · Minneapolis, US
• Donald R. Musgrove · Minneapolis, US

Key dates

• Filing date: Apr 16, 2020
• Grant date: Aug 15, 2023
• Expiry date: Feb 18, 2041

Classification

• Technology area (CPC A): Human Necessities
• CPC primary: A61B5/353
• WIPO field: Medical technology
• WIPO sector: Instruments

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.