Efficient obstructive sleep apnea classification based on EEG signals

Nowadays, analyzing EEG signals has made it easy to diagnose many sleep-related breathing disorders such as Obstructive Sleep Apnea (OSA), which is a potentially serious sleep disorder that affects the quality of human life. This paper introduces an efficient methodology that could be implemented in hardware to differentiate OSA patients from normal controls, based on the Electroencephalogram (EEG) signals. For this purpose, first, the EEG recorded datasets that were obtained from the Phsyionet website are filtered and decomposed into delta, theta alpha, beta and gamma sub-bands using Infinite Impulse Response (IIR) Butterworth band-pass filters. Second, descriptive features such as energy and variance are extracted from each frequency band that are used as input parameters for classification. Finally, several machine learning algorithms including Support Vector Machines (SVM), Artificial Neural Networks (ANN), Linear Discriminant Analysis (LDA) and Naive Bayes (NB) are employed in order to identify if the OSA exists or not, according to the objective of this study. The results that are obtained from these classifiers are then compared in terms of accuracy, sensitivity and specificity. The experimental results show that the SVM attained the best classification accuracy of 97.14% as compared to the others.