Affordable Access

deepdyve-link
Publisher Website

Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model.

Authors
  • Chen, Tsai-Min1
  • Huang, Chih-Han2
  • Shih, Edward S C3
  • Hu, Yu-Feng4
  • Hwang, Ming-Jing5
  • 1 Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan; Taiwan AI Academy, Science and Technology Ecosystem Development Foundation, New Taipei City 24158, Taiwan. , (Taiwan)
  • 2 Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan; Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei 10617, Taiwan. , (Taiwan)
  • 3 Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan. , (Taiwan)
  • 4 Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan; Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan; Institute of Clinical Medicine and Cardiovascular Research Institute, National Yang-Ming University, Taipei 11221, Taiwan. , (Taiwan)
  • 5 Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan; Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei 10617, Taiwan. Electronic address: [email protected] , (Taiwan)
Type
Published Article
Journal
iScience
Publication Date
Mar 27, 2020
Volume
23
Issue
3
Pages
100886–100886
Identifiers
DOI: 10.1016/j.isci.2020.100886
PMID: 32062420
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Electrocardiograms (ECGs) are widely used to clinically detect cardiac arrhythmias (CAs). They are also being used to develop computer-assisted methods for heart disease diagnosis. We have developed a convolution neural network model to detect and classify CAs, using a large 12-lead ECG dataset (6,877 recordings) provided by the China Physiological Signal Challenge (CPSC) 2018. Our model, which was ranked first in the challenge competition, achieved a median overall F1-score of 0.84 for the nine-type CA classification of CPSC2018's hidden test set of 2,954 ECG recordings. Further analysis showed that concurrent CAs were adequately predictive for 476 patients with multiple types of CA diagnoses in the dataset. Using only single-lead data yielded a performance that was only slightly worse than using the full 12-lead data, with leads aVR and V1 being the most prominent. We extensively consider these results in the context of their agreement with and relevance to clinical observations. Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.

Report this publication

Statistics

Seen <100 times