A hybrid classification model using artificial bee colony with particle swarm optimization and minimum relative entropy as post classifier for epilepsy classification

Abstract

One of the most striking features of the human brain is that it has an amazing spatio temporal dynamics. Due to the excessive and irregular electrical action flowing in the human brain, the seizure activity happens. Because of epileptic seizures, it gives rise to unusual and strange sensations thereby affecting the behaviour and quality of life of the person. The electrical activities of the human brain can be easily measured with the help of Electroencephalogram (EEG) signals. In this paper, the hybrid model of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms is implemented to get a first level optimization in the epilepsy risk level of the EEG signals. Further Minimum Relative Entropy (MRE) is used as a second level post classifier to optimize it further for the perfect classification of epilepsy risk levels from EEG signals. The results show that an average post classification accuracy with MRE classifier is 97.90% along with an average time delay of 2.06 s.