Analysis of dimensionality reduction techniques with ABC-PSO classifier for classification of epilepsy from EEG signals

Abstract

Epilepsy is a commonly occurring neurological disorder which is characterized by recurrent seizures and it is of different types. The seizures can occur at various times irrespective of any symptoms. The central nervous systems are severely disturbed by the seizures activity. The abnormal electrical behaviour of a collection of cells in the brain leads to seizures and it can be detected by clinical symptoms. For the detailed study and diagnosis of epilepsy, Electroencephalography (EEG) signals are most commonly used. The electrical activity representation which results due to the generation by the cerebral cortex neurons are shown by EEG recordings. Because of this reason, it forms an integral component in the brain activity evaluation, epilepsy diagnosis and perception of epileptic attack. As the EEG recordings are quite long in nature, processing it is quite difficult. Therefore in this paper, the dimensions of the original EEG recordings are reduced with the help of dimensionality reduction techniques such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Fast ICA and Linear Discriminant Analysis (LDA). The dimensionally reduced values are then fed inside the hybrid classifier called as Artificial Bee Colony-Particle Swarm Optimization (ABC-PSO) Classifier and the epilepsy risk level classification from EEG signals are analyzed. The results show that the highest classification accuracy of 97.42% along with a highest quality value of 22.76 is obtained when Fast ICA is used as a dimensionality reduction technique and classified with ABC-PSO Classifier.