EEG biomarker of Sleep Apnoea using discrete wavelet transform and approximate entropy

Abstract

Sleep Apnoea Syndromes (SAS) is a sleep disorder which caused breathing pauses during sleep at night. There is various method of analyzing sleep EEG signals can be found in the literature. In this paper both linear; Discrete Wavelet Transform (DWT) and non-linear; Approximate Entropy (ApEn) extraction methods were performed to differentiate features of each sleep stages between apnoea and healthy person. The efficiency of both extraction methods was compared by using ANOVA. In our study, we observed the non-linear feature of ApEn improves the ability to discriminate healthy and sleep apnoea at different sleep stages.