Automatic sleep stage recurrent neural classifier using energy features of EEG signals

This paper presents a recurrent neural classifier for automatically classifying sleep stages based on energy features from the EEG signal of the F_pz-C_z channel. The energy features were extracted from characteristic waves of EEG signals which were then used to classify different sleep stages. The recurrent neural classifier, utilizing energy features extracted from EEG signals, assigned each 30-s epoch to one of five possible sleep stages: wakefulness, NREM 1, NREM 2, SWS, and REM. Eight sleep recordings obtained from the Sleep-EDF database, which is available from the PhysioBank, were utilized to validate the proposed method. Using the features extracted by our research, classification performance of a feedforward neural network (FNN) and a probabilistic neural network (PNN) were compared to that of the proposed recurrent neural classifier. The classification rate of the recurrent neural classifier was found to be better (87.2%) than those of the two neural classifiers (81.1% for FNN and 81.8% for PNN). The result demonstrates that the proposed recurrent neural classifier using the energy features extracted from characteristic waves of EEG signals can classify sleep stages more efficiently and accurately using only a single EEG channel.