An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection
Liong-Rung Liu,
Ming-Yuan Huang,
Shu-Tien Huang,
Lu-Chih Kung,
Chao-hsiung Lee,
Wen-Teng Yao,
Ming-Feng Tsai,
Cheng-Hung Hsu,
Yu-Chang Chu,
Fei-Hung Hung,
Hung-Wen Chiu
Affiliations
Liong-Rung Liu
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan; Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
Ming-Yuan Huang
Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan; Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
Shu-Tien Huang
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan; Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
Lu-Chih Kung
Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan; Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
Chao-hsiung Lee
Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan; Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
Wen-Teng Yao
Division of Plastic Surgery, Department of Surgery, Mackay Memorial Hospital, Taipei, Taiwan; Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
Ming-Feng Tsai
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Division of Plastic Surgery, Department of Surgery, Mackay Memorial Hospital, Taipei, Taiwan; Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
Cheng-Hung Hsu
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
Yu-Chang Chu
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
Fei-Hung Hung
Health Data Analytics and Statistics Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
Hung-Wen Chiu
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan; Bioinformatics Data Science Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; Corresponding author. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias. However, in this study, we employed a convolutional neural network (CNN) to classify distinct arrhythmias without QRS wave detection step. The ECG data utilized in this study were sourced from the publicly accessible PhysioNet databases. Taking into account the impact of the duration of ECG signal on accuracy, this study trained one-dimensional CNN models with 5-s and 10-s segments, respectively, and compared their results. In the results, the CNN model exhibited the capability to differentiate between Normal Sinus Rhythm (NSR) and various arrhythmias, including Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Wolff-Parkinson-White syndrome (WPW), Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Ventricular Flutter (VFL), Mobitz II AV Block (MII), and Sinus Bradycardia (SB). Both 10-s and 5-s ECG segments exhibited comparable results, with an average classification accuracy of 97.31%. It reveals the feasibility of utilizing even shorter 5-s recordings for detecting arrhythmias in everyday scenarios. Detecting arrhythmias with a single lead aligns well with the practicality of wearable devices for daily use, and shorter detection times also align with their clinical utility in emergency situations.
