Abstract
Sleep apnea is a sleeping disorder, which adversely affects the health of humans. The diagnosis of sleep apnea is possible by the detection of apnea events using electroencephalogram (EEG) recordings. This paper introduces an adaptive decomposition for the detection of apnea events using EEG signals. In introduced decomposition, the evolutionary techniques (ETs) optimized Hermite functions (HFs) represent the EEG signals. In tested ETs, the artificial bee colony (ABC) algorithm provides the least reconstruction error for the representation of EEG signals. The ABC is considered for Hermite coefficients-based feature extraction. From the extracted features, a highly discriminative feature set is obtained using the Fisher-score ranking test. The apnea detection performance of ranking-based selected features is evaluated using the extreme learning machine and least-squares support vector machine classifiers. The proposed method obtained performance measures, sensitivity 99.47%, specificity 99.58%, and accuracy 99.53%, are better as compared to the state-of-the-art methods.
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