MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG

• Published in: IEEE journal of translational engineering in health and medicine Vol. 9; pp. 1 - 11
• Main Authors: Zhang, Jing, Liang, Deng, Liu, Aiping, Gao, Min, Chen, Xiang, Zhang, Xu, Chen, Xun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Arrhythmia; Arrhythmia classification; Artificial neural networks; Cardiac arrhythmia; Classification; co-optimization; Data integration; Data mining; deep learning; Electrocardiography; Feature extraction; Feature maps; Heart; Heart beat; Integrity; Lead; Machine learning; multi-lead ECG analysis; Optimization; Training; deep learning; Arrhythmia classification; co-optimization; multi-lead ECG analysis

Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matri...