Analysis of sleep EEG activity during hypopnoea episodes by least squares support vector machine employing AR coefficients

This paper presents the application of least squares support vector machines (LS-SVMs) for automatic detection of alterations in the human electroencephalogram (EEG) activities during hypopnoea episodes. The obstructive sleep apnoea hypopnoea syndrome (OSAH) means “cessation of breath” during the sleep hours and the sufferers often experience related changes in the electrical activity of the brain and heart. Decision making was performed in two stages: feature extraction by computation of autoregressive (AR) coefficients and classification by the LS-SVMs. The EEG signals (pre and during hypopnoea) from three electrodes (C3, C4 and O2) were used as input patterns of the LS-SVMs. The performance of the LS-SVMs was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed LS-SVM has potential in detecting changes in the human EEG activity due to hypopnoea episodes.