Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model.
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ABSTRACT:
SUBMITTER: Chen TM
PROVIDER: S-EPMC7031313 | biostudies-literature | 2020 Mar
REPOSITORIES: biostudies-literature
Publications
Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model.
Chen Tsai-Min TM Huang Chih-Han CH Shih Edward S C ESC Hu Yu-Feng YF Hwang Ming-Jing MJ
iScience 20200204 3
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 ...[more]
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