Variational Bayesian Matrix Factorization and Certain Post Classifiers for Classification of Epilepsy from EEG Signals

Abstract

The main aim of this paper is to employ Variational Bayesian Matrix Factorization (VBMF) as a dimensionality reduction technique followed by the Gaussian Mixture Model (GMM), Genetic Algorithm (GA) and Naive Bayes Classifier (NBC) as post classifiers for the classification of epilepsy risk levels from Electroencephalography (EEG) Signals. Since epilepsy is one of the serious disorders of the brain which is characterized by frequent and recurrent seizures, the detection and classification of it seems to be very important. Using the EEG signals, the epileptic seizures can be analyzed because it aids in the recording, diagnosing and for treating other neurological disorders. In this paper, the results are analyzed and compared in terms of sensitivity, specificity, time delay, quality values, performance index and accuracy.