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http://dx.doi.org/10.5392/JKCA.2011.11.4.027

Sleep Disturbance Classification Using PCA and Sleep Stage 2  

Shin, Dong-Kun (삼육대학교 컴퓨터학부)
Publication Information
The Journal of the Korea Contents Association / v.11, no.4, 2011 , pp. 27-32 More about this Journal
Abstract
This paper presents a methodology for classifying sleep disturbance using electroencephalogram (EEG) signal at sleep stage 2 and principal component analysis. For extracting initial features, fast Fourier transforms(FFT) were carried out to remove some noise from EEG signal at sleep stage 2. In the second phase, we used principal component analysis to reduction from EEG signal that was removed some noise by FFT to 5 features. In the final phase, 5 features were used as inputs of NEWFM to get performance results. The proposed methodology shows that accuracy rate, specificity rate, and sensitivity were all 100%.
Keywords
Sleep Stage; FFT; PCA; Fuzzy Neural Network; Sleep Disturbance;
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Times Cited By KSCI : 2  (Citation Analysis)
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