Multi-lead electrocardiogram (ECG) signal classification method based on self-supervised learning

US12290386B1 · kind B1 · utility

Assignees

• QILU UNIVERSITY OF TECHNOLOGY (SHANDONG ACADEMY OF SCIENCES) · CN
• SHANDONG COMPUTER SCIENCE CENTER (NATIONAL SUPERCOMPUTER CENTER IN JINAN) · CN

Inventors

• Yinglong WANG · Jinan, CN
• Wei Liu · Jinan, CN
• Minglei SHU · Jinan, CN
• Pengyao XU · Jinan, CN
• Shuwang ZHOU · Jinan, CN
• Zhaoyang LIU · Jinan, CN

Key dates

• Filing date: May 15, 2024
• Grant date: May 6, 2025
• Expiry date: May 15, 2044

Classification

• Technology area (CPC Y): Emerging Cross-Sectional Technologies
• CPC primary: Y02A90/10
• WIPO field: Medical technology
• WIPO sector: Instruments

Abstract

A multi-lead electrocardiogram (ECG) signal classification method based on self-supervised learning relates to the technical field of ECG signal classification. The method includes: processing an original signal through different data augmentation methods, designing an appropriate encoder module, extracting a feature of an ECG signal through a large amount of easily available unlabeled data such that an encoder learns more class information of the ECG signal, fine-tuning the model encoder with a small amount of labeled data for feature optimization, and continuously optimizing a parameter of a feature extractor by training a model such that a generated feature well reflects a structure and information of input data. Through self-supervised learning, the method reduces obstacles caused by performing ECG signal classification through a large amount of expensive manually labeled data, improving the generalization ability of the model.