Robust Hermite decomposition algorithm for classification of sleep apnea EEG signals
Sleep apnea (SA) event occurs due to restraint in normal respiration. It requires accurate diagnosis, because of neurotic and cardiac disorders. In this work, particle swarm optimisation (PSO)-based Hermite decomposition algorithm is proposed, for identification of SA event using electroencephalogram (EEG) signals with parameterised classifier. The information from randomly varying complex EEG signals is extracted in terms of PSO optimised Hermite functions (HFs), with constraint of minimum error function. The Hermite coefficients computed from HFs-based statistical features are applied as input to PSO parameterised least square support vector machine classifier. The proposed decomposition for EEG signals provides negligible mean value of error function and obtain best results for identification of apnea event compared to existing methods.
