A novel electrocardiogram arrhythmia classification method based on stacked sparse auto-encoders and softmax regression

• Main Authors: Yang, Jianli, Bai, Yang, Lin, Feng, Liu, Ming, Hou, Zengguang, Liu, Xiuling
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Stacked Sparse Auto-encoders; ECG Arrhythmia Classification
• Online Access: https://hdl.handle.net/10356/139611
• Institution: Nanyang Technological University

Arrhythmia classification is crucial in electrocardiogram (ECG) based automatic cardiovascular disease diagnosis, e.g., to help prevent stroke or sudden cardiac death. However, the complex individual differences in ECG morphology make it challenging in accurately categorizing arrhythmia heartbeats....