Sleep states detection using halfwave and Franklin transformation

Sleep is a physiological phenomenon and a sufficient amount of sleep is mandatory for a human for his/her health. The aim of the paper is to develop an efficient algorithm to detect the various sleep states by combining different biomedical signals. The novelty of the algorithm is that we used two piecewise linear data reduction techniques namely a new Halfwave method in time domain and Franklin transformation in frequency domain. The obtained two piecewise linear forms of signals have low complexity that still preserve the characteristics of the stages of the sleep in the signals. The components of the feature vector are generated from the parameters of the two reduced piece wise linear functions. Algorithm is tested on MIT-BIH Polysomnographic Database having more than 70 hours long term EEG, blood and nasal signals with six different sleep classes. Proposed method shows better performance in terms of average Sensitivity, Specificity, Accuracy and False Alarm Rate/hour than state of the art methods.