Automatic Sleep Stage Classification Using Frequency Analysis of Eeg Signals
نویسنده
چکیده
An automated sleep stage classification system relying only on the frequency analysis of the EEG signal is developed and analyzed in this paper. The classification system consists of the feature extraction algorithm and a neural network classifier. We investigate two different feature extraction methods: a classical FFT frequency analysis and a novel LMS based feature extraction. The same two-layer neural network is used as the sleep stage classifier for both feature extraction methods. We compare performance and complexity of the FFT and LMS methods and present classification results obtained using patient data scored manually by medical experts. The two methods achieve similar overall and per-stage classification accuracy.
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