DeepECG: Image-based electrocardiogram interpretation with deep convolutional neural networks. (August 2021)

• Record Type: Journal Article
• Authors: Li, Changling; Zhao, Hang; Lu, Wei; Leng, Xiaochang; Wang, Li; Lin, Xintan; Pan, Yibin; Jiang, Wenbing; Jiang, Jun; Sun, Yong; Wang, Jianan; Xiang, Jianping
• Is Part Of: Biomedical signal processing and control. Volume 69(2021)
• Publication Date: 2021-08
• Subjects: Electrocardiogram -- Arrhythmia -- Deep learning -- Convolutional neural networks -- Transfer learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102824
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Electrocardiogram (ECG) plays a critical role in the diagnosis of cardiovascular disease (CVDs). In this paper, we develop DeepECG, a system that diagnoses 7 kinds of arrhythmia from 51, 579 ECGs. DeepECG takes ECG images as inputs, and performs arrhythmia classification using deep convolutional neural network models (DCNN) and transfer learning. We conduct a comprehensive study of different neural network architectures, where the best model Inception-V3 achieves mean balanced accuracy of 98.46 %, recall of 95.43 %, and specificity of 96.75 %. The experimental results have successfully validated that our system can achieve excellent multi-label classification based on image formats, making it possible for cardiologists to use image-based ECG interpretation with DCNN to aid diagnosis and reduce misdiagnosis rates.