Genetic Algorithm Optimization of Fuzzy Outputs for Classification of Epilepsy Risk Levels from EEG Signals

摘要

This paper aims to optimize the output of epilepsy activity namely electro encephalogram (EEG) signal by Fuzzy Logic techniques using Genetic Algorithms (GA).The fuzzy techniques are applied to classify the levels of epilepsy based on extracted parameters like energy,variance,peaks,sharp and spike waves,duration,events and covariance obtained from the EEG of the patient.A Binary GA and Continuous GA are then applied on the classified data to obtain the optimized risk level that characterizes the patient's epilepsy risk level.The performance index (PI) and quality value (QV) are calculated for both method.A group of eight patients with known epilepsy findings are used for this study.High PI,such as,92% (BGA)and 96% (CGA) were obtained at QVs of 80% and 90%,respectively.It is found that the Continuous Genetic Algorithm provides a good tool for optimizing the epilepsy risk levels.