The uncontrolled firing of neurons in brain leads to epileptic seizures in the patients. A novel scheme to detect epileptic seizures from back ground electroencephalogram (EEG) is proposed in this paper. This scheme is based on discrete wavelet packet transform with energy, entropy, kurtosis, skewness, mean, median and standard deviation as the properties for creating features of signals for classification. Optimal features are selected using genetic algorithm (GA) with support vector machine as a classifier for creating objective function values for the GA. Clinical EEG data from epileptic and normal subjects are used in the experiment. The knowledge of neurologist (medical expert) is utilized to train the system. To evaluate the efficacy of the proposed scheme, a 10 fold cross-validation is implemented, and the detection rate is found 100% accurate with 100% of sensitivity and specificity for the data under consideration. The proposed GA-SVM scheme is a novel technique using a hybrid approach with wavelet packet decomposition, support vector machine and GA. It is novel in terms of selection of features sub set, use of SVM classifier as objective function for GA and improved classification rate. The proposed model can be used in the developing and the third world countries where the medical facilities are in acute shortage and qualified neurologists are not available. This system can be helpful in assisting the neurologists in terms of automated observation and saving valuable human expert time. 
Genetic algorithms tuned expert model for detection of epileptic seizures from EEG signatures