Determination of Sleep Stage Separation Ability of Features Extracted from EEG Signals Using Principle Component Analysis


VURAL C. , YILDIZ M.

JOURNAL OF MEDICAL SYSTEMS, vol.34, no.1, pp.83-89, 2010 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 34 Issue: 1
  • Publication Date: 2010
  • Doi Number: 10.1007/s10916-008-9218-9
  • Title of Journal : JOURNAL OF MEDICAL SYSTEMS
  • Page Numbers: pp.83-89
  • Keywords: EEG, Sleep stage classification, Sleep scoring, Principle component analysis, NEURAL-NETWORK

Abstract

In this study, a method was proposed in order to determine how well features extracted from the EEG signals for the purpose of sleep stage classification separate the sleep stages. The proposed method is based on the principle component analysis known also as the Karhunen-Lo,ve transform. Features frequently used in the sleep stage classification studies were divided into three main groups: (i) time-domain features, (ii) frequency-domain features, and (iii) hybrid features. That how well features in each group separate the sleep stages was determined by performing extensive simulations and it was seen that the results obtained are in agreement with those available in the literature. Considering the fact that sleep stage classification algorithms consist of two steps, namely feature extraction and classification, it will be possible to tell a priori whether the classification step will provide successful results or not without carrying out its realization thanks to the proposed method.