A hybrid mRMR-genetic based selection method for the prediction of epileptic seizures
Elie Bou Assi, Mohamad Sawan, Dang Khoa Nguyen, Sandy A. Rihana · 2015
Seizure forecasting would significantly improve the quality of life of epileptic patients. Predictive algorithms use high dimensionality data to evaluate the likelihood of an impending seizure. Dimensionality reduction is a key step towards the development of portable prediction systems. In this work, a comparative study of feature selection and classification methods was performed. Based on a Support Vector Machine and an Adaptive Neuro Fuzzy inference system, data reduction was performed by combining a minimum redundancy maximum relevance approach for electrodes selection and a genetic algorithm for features selection. The results show that the selected subset of features operates equally and sometimes even better than the whole features set.