An Analysis of Integration of Hill Climbing in Crossover and Mutation operation for EEG Signal Classification

Abstract

A common problem in the diagnosis of epilepsy is the volatile and unpredictable nature of the epileptic seizures. Hence, it is essential to develop Automatic seizure detection methods. Genetic programming (GP) has a potential for accurately predicting a seizure in an EEG signal. However, the destructive nature of crossover operator in GP decreases the accuracy of predicting the onset of a seizure. Designing constructive crossover and mutation operators (CCM) and integrating local hill climbing search technique with the GP have been put forward as solutions. In this paper, we proposed a hybrid crossover and mutation operator, which uses both the standard GP and CCM-GP, to choose high performing individuals in the least possible time. To demonstrate our approach, we tested it on a benchmark EEG signal dataset. We also compared and analyzed the proposed hybrid crossover and mutation operation with the other state of art GP methods in terms of accuracy and training time. Our method has shown remarkable classification results. These results affirm the potential use of our method for accurately predicting epileptic seizures in an EEG signal and hint on the possibility of building a real time automatic seizure detection system.