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An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection.
Liu, Liong-Rung; Huang, Ming-Yuan; Huang, Shu-Tien; Kung, Lu-Chih; Lee, Chao-Hsiung; Yao, Wen-Teng; Tsai, Ming-Feng; Hsu, Cheng-Hung; Chu, Yu-Chang; Hung, Fei-Hung; Chiu, Hung-Wen.
Afiliación
  • Liu LR; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
  • Huang MY; Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan.
  • Huang ST; Department of Medicine, Mackay Medical College, New Taipei City, Taiwan.
  • Kung LC; Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan.
  • Lee CH; Department of Medicine, Mackay Medical College, New Taipei City, Taiwan.
  • Yao WT; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
  • Tsai MF; Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan.
  • Hsu CH; Department of Medicine, Mackay Medical College, New Taipei City, Taiwan.
  • Chu YC; Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan.
  • Hung FH; Department of Medicine, Mackay Medical College, New Taipei City, Taiwan.
  • Chiu HW; Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan.
Heliyon ; 10(5): e27200, 2024 Mar 15.
Article en En | MEDLINE | ID: mdl-38486759
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2024 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2024 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Reino Unido