自动化学报2016，Vol.42Issue(9)：1339-1346,8.DOI:10.16383/j.aas.2016.c150817
基于卷积神经网络的T波形态分类
T Wave Shape Classification Based on Convolutional Neural Network
摘要
Abstract
T wave shape classification which is helpful for the diagnosing of many cardiovascular diseases such as my-ocardial ischemia, acute pericarditis and sudden cardiac death, is an important research topic in electrocardiogram remote monitoring. The method of traditional T wave shape classification is based on the accurate detection of the T wave. It is implemented after the T wave delineation and feature extraction. However, T wave detection is difficult because of the position shift, morphologic variation and multi-noise. To resolve this problem, this paper proposes to classify T wave shape based on convolutional neural network. In the new method, firstly, a candidate data segment which contains the T wave is intercepted based on the location of the QRS wave and the medical statistical knowledge. Then the T wave is classified directly based on the convolutional neural network. Due to the advantages of sparse connection and weight share, the convolutional neural network can extract T wave feature by data training and it is robust to the poison shift and noise. So the convolutional neural network can resolve the T wave shape classification problem efficiently. The new method is tested on the MIT-BIH QT database; the experimental results show that the new method performs well in T wave shape classification without T wave delineation and the classification accuracy is 99.1%.关键词
心血管病/T波形态/卷积神经网络/分类Key words
Cardiovascular disease/T wave morphology/convolutional neural network/classification引用本文复制引用
刘明,李国军,郝华青,侯增广,刘秀玲..基于卷积神经网络的T波形态分类[J].自动化学报,2016,42(9):1339-1346,8.基金项目
国家自然科学基金(61473112),河北省杰出青年基金(F2016201186),河北省自然科学基金(F2015201112),河北省高等学校科学技术研究项目(ZD2015067)资助Supported by National Natural Science Foundation of China (61473112), Foundation for Distinguished Young Scholars of Hebei Province (F2016201186), Natural Science Foundation of Hebei Province (F2015201112), and Science and Technology Re-search Project for Universities and Colleges in Hebei Province (ZD2015067) （61473112）
