Classification of the ECG Signal Using Artificial Neural Network

被引：4

作者：

Weems, Andrew ^{[1
]}

Harding, Mike ^{[1
]}

Choi, Anthony ^{[2
]}

机构：

[1] Mercer Univ, Dept Biomed Engn, Macon, GA 31207 USA

[2] Mercer Univ, Dept Elect & Comp Engn, Macon, GA 31207 USA

来源：

PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT TECHNOLOGIES AND ENGINEERING SYSTEMS (ICITES2014) | 2016年 / 345卷

关键词：

Artificial neural network; ECG; MATLAB; Signal classification; Cardiac abnormalities; Cardiac arrhythmias; ACUTE MYOCARDIAL-INFARCTION; 12-LEAD ECG;

D O I：

10.1007/978-3-319-17314-6_70

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recording of electrocardiogram (ECG) signals and the correlation to cardiovascular diseases are a major problem in today's society. A common abnormality is arrhythmia, which is unexpected variation in cardiac rhythm. The goal of this study is to analyze these types of signals and find a more efficient way to classify these signals. Currently, medical devices for detecting ECG signals are at least 85 % accurate in analyzing the data. Neural networks have progressed quickly over the past few years, and have the capability of recognizing many types of variation in these signals. The pattern recognition power of Artificial Neural Networks (ANNs) is a valuable tool when classifying ECG signals in cardiac patients. Data obtained from the PhysioBank ATM was used to analyze the structure of an ANN and the effect that it has on pattern recognition. The results show that only one misclassification occurred resulting in an accuracy of 96 %.

引用

页码：545 / 555

页数：11

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