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

VURAL C., YILDIZ M.

JOURNAL OF MEDICAL SYSTEMS, cilt.34, sa.1, ss.83-89, 2010 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34 Sayı: 1
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1007/s10916-008-9218-9
  • Dergi Adı: JOURNAL OF MEDICAL SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.83-89
  • Anahtar Kelimeler: EEG, Sleep stage classification, Sleep scoring, Principle component analysis, NEURAL-NETWORK
  • Marmara Üniversitesi Adresli: Hayır

Özet

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.