Sleep Disturbance Classification Using PCA and Sleep Stage 2

주성분 분석과 수면 2기를 이용한 수면 장애 분류

  • 신동근 (삼육대학교 컴퓨터학부)
  • Received : 2011.03.28
  • Accepted : 2011.04.11
  • Published : 2011.04.28


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%.


Sleep Stage;FFT;PCA;Fuzzy Neural Network;Sleep Disturbance


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