Sleep Stages Recognition Based on Combined Artificial Neural Network and Fuzzy System Using Wavelet Transform Features

Improving the quality of sleep is an important issue for many researches. A number of biomedical signals, such as EEG, EMG, and EOG were used to classify sleep stages. Based on those signals, one can detect and diagnose the sleep related disorders. There were many researches focused on automatic sleep stages classification. In this research, a new classification method is presented by applying Elman neuron network combined with fuzzy rules and features are extracted by wavelets packets. Nine subjects were recruited from Cheng-Ching General Hospital, Taichung, Taiwan. The sampling frequency is 250Hz and the single channel (C

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) EEG signal was acquired for each subject. Combined network was used to recognize the sleep stages in each epoch (a 10 second segment data). The classification results relied on the strong points of neural network and fuzzy logic with average sensitivity is 88.48%, average specificity achieves 95.96%, and average accuracy is 93.79%. The data samples and the length of sleep intervals will be increased for experiment in the future to improve the accuracy.