Fast and high accuracy classification of sleep EEG using PLSR method

The aim of this study is to classify the status of sleep from electroencefelography (EEG) data recorded from seven different healthy individuals. The twenty two autoregressive (AR) model coefficent are computed and used as features. Three classification algorithms, namely k-NN, Bayes and PLSR methods are trained and tested. The results show that the PLSR algorithm yielded highest accuracy and short classification times. Furthermore, all utilizies just a single channel. Based on these results we propose that method can be used in clinical applications.