Extraction of Wavelet Features for the Classification of Sleep Stages Using Single Channel EEG

Sleep is just as important as diet and exercise. Humans spend about one third of their lives asleep.
Sleep tests involves processing and analysis of many signals combination called as
Polysomnographic signal (PSG). In the large data sets like Sleep Electroencephalogram (Sleep EEG),
to do analysis it becomes tedious and time taken. Instead of considering the whole data, considering
a few critical features from the signal makes the analysis simpler and the memory requirements are
also less, since the analysis could be carried out on digital platform. A feature is a distinguishable
sectional property obtained from a portion of signal. Feature extraction depicts the number of feature
to be extracted from the signal. Thus the feature extraction plays a pivotal role in the analysis of Sleep
EEG. In this work we discussed the decomposition of Sleep EEG signal into required frequency bands
and adopted feature extraction techniques of wavelet decomposition method to extract features from
Sleep EEG signal by considering single channel EEG.