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An approach of sleep stage classification based on Time–Frequency analysis and Random Forest (TFRF) on a single channel is presented in the paper. Before classifying sleep stages, representative features are extracted by feature extraction method, such as FFT in the frequency domain of EEG signal and Hilbert transform in time domain, to reduce dimensionality and channel numbers. Other parameters are also used, e.g. Hjorth parameters. Then a Random Forest is trained by classifying the sleep stages. The TFRF is evaluated by means of Dreams database provided by University of MONS – TCTS Laboratory. The new standard of the American Academy of S leep Medicine about the sleep stage classification is obeyed. 16 objects are considered as the training samples, while the other 4 objects are regarded as the test samples. A best result of 90.4% sensitivity is got with the signals from only one channel. Its application prospect is very extensive. Keywords—sleep stage classification; EEG; time-frequency analysis; Random Forest

An Approach of Sleep Stage Classification Based on Time-frequency Analysis and Random Forest on a Single Channel