Analysis of Features Extracted from EEG Epochs by Discrete Wavelet Decomposition and Hilbert Transform for Sleep Apnea Detection.

Sleep apnea (SA) is one of the most common disorders manifesting during sleep and the electroencephalo-gram (EEG) belongs to these biomedical signals that change during apnea and hypopnea episodes. In recent years, a few publications reported approaches to the automatic classification of sleep apnea episodes based only on the EEG. The purpose of this work was to analyze statistical features extracted from the EEG epochs by combined discrete wavelet transform (DWT) and Hilbert transform (HT). Additionally, the selected most discriminative 30 features were then used in the automatic classification of normal breathing and obstructive (OSA) and central (CSA) apnea by a feedforward neural network with 17+7 neurons in two hidden layers. This classifier returned the accuracy of 73.9% for the training and 77.3% for the testing set. The analysis of features extracted from EEG epochs revealed the importance of theta, beta and gamma brain waves.