An Automatic Detection of Sleep using Different Statistical Parameters of Single Channel EEG Signals

The present work deals with the automatic detection of the sleep stages from the singlechannel EEG data. Various stages of sleep are Awake, sleep stage 1, 2, 3 and 4 and rapid eye movement. Statistical attributes are extracted with the help of Ensemble Empirical Mode Decomposition, Hjorth parameter and zero-crossing rate. Ten-cross fold classification process is followed after best ranked attribute selection. After attributes are selected, the data is classified using bagging classifier. Accuracies of 98.46%, 95.62%, 93.87%, 93.17% and 91.93% for two-stages, three-stages, four-stages, five-stages and six-stages classification respectively. This classifier can be used for the real life application due to higher accuracies. Keyword: EEG, sleep stages, Bagging, EEMD, Hjorth parameter

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