A Framework for Schizophrenia EEG Signal Classification with Nature Inspired Optimization Algorithms

One of the severe and prolonged disorder of the human brain which disturbs the behavioral characteristics of an individual completely such as interruption in the thinking process and speech is schizophrenia. It is a manifestation of many symptoms such as hallucinations, functional deterioration, disorganized speech and hearing sounds and speeches that are non-existent. In this paper, a computerized approach based on optimization and classification is done to analyze the classification of schizophrenia from Electroencephalography (EEG) signals. As EEG can analyze a lot of brain disorders and is used to study the diseases of the brain in an in-depth manner, it can be used to analyze the schizophrenia EEG signals. In this paper, three feature extraction techniques are employed such as Partial Least Squares (PLS) Non linear Regression technique, Expectation Maximization based Principal Component Analysis (EM-PCA) technique and Isometric Mapping (Isomap) technique. The extracted features are further optimized with four optimization algorithms such as Flower Pollination algorithm, Eagle strategy using different evolution algorithm, Backtracking search optimization algorithm and Group search optimization algorithm. The optimized values are then classified with varied versions of both Adaboost classifier and Naïve Bayesian Classifier. The individual results show that for normal cases, Isomap features when optimized with Backtracking search optimization algorithm and classified with Modest Adaboost classifier, a classification accuracy of 98.77% is obtained. The individual results show that for schizophrenia case, when Isomap features are optimized with Flower Pollination optimization algorithm and classified with Real Adaboost classifier, a classification accuracy of 98.77% is obtained.