Fast and accurate PLS-based classification of EEG sleep using single channel data

Since speed of classification is important to real-time applications, this study proposed fast classification of sleep and wake stages using a single electroencephalograph (EEG) channel. Changes in the sleep and wake stages are accompanied by changes in the frequency spectrum of the EEG signals; so, the features extracted from the 5-s epoch of the EEG using auto-regressive (AR) coefficients were used to represent EEG signals of different sleep and wake stages. The proposed fast classification method was based on partial least squares regression (PLS), which was used to classify these features by finding an optimum beta using K-fold cross validation. The Physionet database was used to confirm accuracy and speed of the proposed classification system. This system could be used in real-time implementations because of its high classification rate, speed and capability to be implemented on hardware owing to be very comfortable. Finally, results of the PLS were compared with those of other classifiers such as k-nearest neighborhood (k-NN), linear discriminant classifier (LDC) and Bayes. We achieved 91% classification accuracy by selecting PLS as the classifier. These comparisons revealed that the proposed algorithm could recognize an emergency situation in less than 1 s with high accuracy.