SVM Optimization for Epileptic Seizure Prediction

Abstract

Some of the epileptic patients cannot be treated by drugs or surgery, fact that a ects the patient's daily life. The quality of life of these patients would be extremely improved by the existence of e ective seizure prediction algorithms. Epileptic seizures prediction can be achieved considering it as a classi cation problem. In order to predict the occurrence of an epileptic episode, an ap- proach using computational intelligence methods is currently under develop- ment, on behalf of the EPILEPSIAE project. Twenty-two univariate features were extracted from EEG (electroencephalogram). For a real-time prediction of the epileptic seizures, the number of inputs must be reduced in order to achieve a fast detection of the seizures, while maintaining the predictive power. In this thesis, Support Vector Machines (SVM) were optimized by three evolutionary approaches: The Elitist Non-dominated Sorting Genetic Algo- rithm (NSGA-II), the Particle Swarm Optimization (PSO) and S Metric Selection - Evolutionary Multi-Objective Algorithm (SMS-EMOA). The pa- rameters under optimization were the inputs, and Cost and Gamma of the SVM classi ers. Several tests were made, with di erent formulations, in order to reduce the complexity of the problem. The results show that using these algorithms it is possible to achieve low- complex predictors with appropriate prediction performance.