Sleep Stage Classification based on EEG Hilbert-Huang Transform

The aim of this work is to propose an automatic sleep stage classification technique of electroencephalogram Is signals(EEG) using Hilbert-Huang Transform. EEG signals are analyzed with lite Hilbert-Huang Transform, instantaneous frequency with the physical meaning is obtained, The energy-frequency distribution of EEG was used as features parameters for each sleep stage, Ultimately using nearest neighbor method for pattern classification complete classifying sleep stage. According to experimental results of 560 samples of sleep EEG, average accuracy rate of the method achieved 81.7%. In a word, The EEG Hilbert-Huang transform based method can be used as an effective sleep staging classification.