Systems and methods for training machine learning models using unlabeled electrocardiogram data

US12361327B1 · kind B1 · utility

Assignee

• Anumana, Inc. · US

Inventors

• Uddeshya Upadhyay · Bengaluru, IN
• Mayank Sharma · Noida, IN
• Sairam Bade · Suryapet, IN
• Ashim Prasad · Kanchinakote, IN
• Rakesh Barve · Bengaluru, IN

Key dates

• Filing date: May 16, 2024
• Grant date: Jul 15, 2025
• Expiry date: May 16, 2044

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/045
• WIPO field: Medical technology
• WIPO sector: Instruments

Abstract

A system for training machine learning models with unlabeled electrocardiogram signals, the system including a memory containing instructions configurating a processor to receive a plurality of electrocardiogram (ECG) data in a textual format, create one or more overlapping temporal patches from the plurality of ECG data, mask at least one temporal patch from the one or more overlapping temporal patches, pretrain an ECG machine learning model to predict the at least one masked temporal patch from the one or more overlapping temporal patches, adjust one or more parameter values of the ECG machine learning model as a function of the at least one predicted masked temporal patch and the at least one masked temporal patch and train the ECG machine learning model as a function of the one or more parameter values and a labeled set of ECG training data.