Discrete harmony search based expert model for epileptic seizure detection in electroencephalography

• Published in: Expert systems with applications Vol. 39; no. 4; pp. 4055 - 4062
• Main Authors: Gandhi, Tapan Kumar, Chakraborty, Prithwish, Roy, Gourab Ghosh, Panigrahi, Bijay Ketan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2012
• Subjects: Algorithms; Classification; Decomposition; Discrete harmony; Discrete wavelet transform (DWT); Entropy; Mathematical models; Neural networks; Probabilistic neural network (PNN); Searching; Seizing; Seizure; Discrete wavelet transform (DWT); Seizure; Discrete harmony; Probabilistic neural network (PNN)
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2011.09.093

Seizure detection and classification using signal processing methods has been an important issue of research for the last two decades. In the present study, a novel scheme was presented to detect epileptic seizure activity with very fast and highest accuracy from background electro encephalogram (EEG) data recorded from epileptic and normal subjects. The proposed scheme is based on discrete wavelet packet transform (DWT) with energy, entropy, standard deviation, mean, kurtosis, skewness and entropy estimation at each node of the decomposition tree followed by application of probabilistic neural network (PNN). Normal as well as epileptic EEG epochs were decomposed into approximation and details coefficients till sixth-level using DWT packet. Discrete harmony search with modified differential operator was used to select the optimal features out of all above mentioned statistical and non-statistical parameters. In order to demonstrate the efficacy of the proposed algorithm for classification purpose using PNN, we have implemented 10-fold cross validation. Clinical EEG data recorded from normal as well as epileptic subjects are used to test the performance of this new scheme. It is found that the detection rate is 100% accurate with same level of sensitivity and specificity.