Multi-label electrocardiogram (ECG) signal classification method based on improved attention mechanism

US12251232B2 · kind B2 · 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
• Guoxuan Xu · Jinan, CN
• Minglei SHU · Jinan, CN
• Zhaoyang LIU · Jinan, CN
• Pengyao XU · Jinan, CN

Key dates

• Filing date: Oct 12, 2023
• Grant date: Mar 18, 2025
• Expiry date: Nov 30, 2043

Classification

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

Abstract

A multi-label electrocardiogram (ECG) signal classification method based on an improved attention mechanism is provided. A model is constructed for classifying a multi-label (multi-lead) ECG signal. The model has a strong ECG data learning ability, allowing a computer to fully extract a feature of the ECG signal and construct a data processing channel model. Therefore, the multi-label (multi-lead) ECG signal can be effectively classified, improving the accuracy and precision of classification.