Open AccessRobust Hermite decomposition algorithm for classification of sleep apnea EEG signals
Author(s) -
Taran S.,
Bajaj V.,
Sharma D.
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.1365
Subject(s) - hermite polynomials , hermite interpolation , electroencephalography , particle swarm optimization , algorithm , support vector machine , computer science , mean squared error , pattern recognition (psychology) , sleep apnea , artificial intelligence , speech recognition , mathematics , statistics , medicine , mathematical analysis , psychiatry , cardiology
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.