Automatic Identification of Abnormalities in 12-lead ECGs Using Expert Features and Convolutional Neural Networks

被引：22

作者：

Liu, Zhongdi ^{[1
]}

Meng, Xiang'ao ^{[1
]}

Cui, Jiajia ^{[1
]}

Huang, Zhipei ^{[1
]}

Wu, Jiankang ^{[1
]}

机构：

[1] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing, Peoples R China

来源：

2018 INTERNATIONAL CONFERENCE ON SENSOR NETWORKS AND SIGNAL PROCESSING (SNSP 2018) | 2018年

关键词：

component; ECG classification; cardiac abnormalities; deep learning; Convolutional Neural Network;

D O I：

10.1109/SNSP.2018.00038

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Automatic identification of the rhythm/morphology abnormalities in ECGs has gained growing attention in various areas and remains a challenge. We propose an algorithm to classify 12-lead ECGs into 9 categories. We extracted expert features including generic features and specific features with statistics and physiology significance. Then a 17-layer Convolutional Neural Network (CNN) was proposed to detect deep features in ECGs. With these features we trained ensemble classifiers to predict labels. Experiment on the training set (5-fold cross-validation) reports 0.81 accuracy score.

引用

页码：163 / 167

页数：5

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