DeepECG: Image-based electrocardiogram interpretation with deep convolutional neural networks

被引：12
|
作者：
Li, Changling [1 ]
Zhao, Hang [2 ]
Lu, Wei [2 ]
Leng, Xiaochang [2 ]
Wang, Li [2 ]
Lin, Xintan [2 ]
Pan, Yibin [3 ]
Jiang, Wenbing [4 ]
Jiang, Jun [1 ]
Sun, Yong [1 ]
Wang, Jianan [1 ]
Xiang, Jianping [2 ]
机构：
[1] Zhejiang Univ, Affiliated Hosp 2, Dept Cardiol, Sch Med, Hangzhou, Peoples R China
[2] ArteryFlow Technol Co Ltd, Hangzhou, Peoples R China
[3] Jinhua Municipal Cent Hosp, Dept Cardiol, Hangzhou, Peoples R China
[4] Wenzhou Peoples Hosp, Dept Cardiol, Hangzhou, Peoples R China
来源：
BIOMEDICAL SIGNAL PROCESSING AND CONTROL | 2021年 / 69卷
关键词：
Electrocardiogram; Arrhythmia; Deep learning; Convolutional neural networks; Transfer learning; HEARTBEAT CLASSIFICATION;
D O I：
10.1016/j.bspc.2021.102824
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
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.
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收藏
页数：6
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