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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article numbere27200
JournalHeliyon
Volume10
Issue number5
DOIs
Publication statusPublished - Mar 15 2024

Keywords

  • Arrhythmia
  • Computer-aided diagnosis (CAD)
  • Convolution neural network (CNN)
  • Deep learning
  • Single-lead electrocardiogram (ECG)

ASJC Scopus subject areas

  • General

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