Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep

JeeEun Lee; Sun K. Yoo

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Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep

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摘要

The purpose of this paper is to analyze sleep stages accurately using fast and simple classifiers based on the frequency domain of electroencephalography(EEG) signal. To compare and evaluate system performance, the rules of Rechtschaffen and Kales(R&K rule) were used. Parameters were extracted from preprocessing process of EEG signal as feature vectors of each sleep stage analysis system through representatives of back propagation algorithm and support vector machine (SVM). As a result, SVM showed better performance as pattern recognition system for classification of sleep stages. It was found that easier analysis of sleep stage was possible using such simple system. Since accurate estimation of sleep state is possible through combination of algorithms, we could see the potential for the classifier to be used for sleep analysis system.

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《工程（英文）（1947-3931）》 |2013年第5期|88-92|共5页
作者
JeeEun Lee; Sun K. Yoo;
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作者单位

Corresponding Author;

Department of Medical Engineering;

College of Medicine;

Yonsei University;

Seoul;

Korea;

Graduate School of Biomedical Engineering;

Yonsei University;

Seoul;

Korea;

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原文格式 PDF
正文语种 chi
中图分类肿瘤学;
关键词
Sleep; Electroencephalography; Neural; Network; Backpropagation; Algorithm; SVM;

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Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep

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