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.

引用

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页数：6

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