Sleep Stages Classification Using Spectral Based Statistical Moments as Features

Abstract

In the pursuit of highly effective and efficient portable sleep classification systems, researchers have been testing a massive number of combinations of EEG features and classifiers.  State of art sleep classification ensembles achieve accuracy in the order of 90%.  However, there is presently no consensus regarding the best setof features for sleep staging with single channel EEG, leading researchers to modify feature selection according to the number of classification stages. This paper introduces a reduced set of frequency-domain features capable of yielding high classification accuracy (90.9%, 91.8%, 92.4%, 94.3% and 97.1%) for all 6- to 2-state sleep stages.  The proposed system uses fast Fourier transform (FFT) to convert data from Pz-Oz EEG channel into the frequency domain. Afterwards, eight statistical features are extracted from specific frequency ranges and fed into a random forest classifier.