Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Journal of Medical Systems
Automatic classification of sleep stages on a EEG signal by artificial neural networks
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
Automatic classification of sleep stages based on the time-frequency image of EEG signals
Computer Methods and Programs in Biomedicine
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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---Loéve 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.