On the classification of sleep states by means of statistical and spectral features from single channel Electroencephalogram

Traditional sleep scoring based on visual inspection of Electroencephalogram (EEG) signals is onerous for sleep scorers because of the gargantuan volume of data that have to be analyzed per examination. Computer-aided sleep staging can alleviate the onus of the sleep scorers. Again, most of the existing works on automatic sleep staging are multichannel based. Multichannel based sleep scoring is not pragmatic for the implementation of a wearable and portable sleep quality evaluation device. Due to all these factors, automatic sleep scoring based on single channel EEG is garnering increasing attention of sleep researchers. In this work, we propound a single channel based solution to sleep scoring. First, we decompose the EEG signals into segments. We then compute various statistical and spectral features from the signal segments. After performing statistical analyses, we perform classification using artificial neural network. Results of various experiments perspicuously manifest that the proposed scheme is superior to state-of-the-art ones in accuracy.