RECOGNITION OF SLEEP STAGES BASED ON A COMBINED NEURAL NETWORK AND FUZZY SYSTEM USING WAVELET TRANSFORM FEATURES

Recognition of sleep stages is an important task in the assessment of the quality of sleep. Several biomedical signals, such as EEG, ECG, EMG and EOG are used extensively to classify the stages of sleep, which is very important for the diagnosis of sleep disorders. Many sleep studies have been conducted that focused on the automatic classification of sleep stages. In this research, a new classification method is presented that uses an Elman neural network combined with fuzzy rules to extract sleep features based on wavelet decompositions. The nine subjects who participated in this study were recruited from Cheng-Ching General Hospital in Taichung, Taiwan. The sampling frequency was 250 Hz, and a single-channel (C-3-A(1)) EEG signal was acquired for each subject. The system consisted of a combined neural network and fuzzy system that was used to recognize sleep stages based on epochs (10-second segments of data). The classification results relied on the strong points of combined neural network and fuzzy system, which achieved an average specificity of approximately 96% and an average accuracy of approximately 94%.