Characterizing Functional Connectivity Network Based on Multi-Domain Analysis for Epilepsy Classification
S. Anupallavi1, G. Mohan Babu2, S. R. Ashokkumar3

1S. Anupallavi*, Research Scholar, Department of ECE , SSM Institute of Engineering and Technology, Dindigul.
2G. Mohan Babu, Associate Professor, Department of ECE ,SSM Institute of Engineering and Technology, Dindigul.
3S. R. Ashok Kumar, Associate Professor, Department of ECE ,SSM Institute of Engineering and Technology, Dindigul.
Manuscript received on December 13, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 2379-2386 | Volume-9 Issue-3, January 2020. | Retrieval Number: C9025019320/2020©BEIESP | DOI: 10.35940/ijitee.C9025.019320
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Abstract: Epileptic is a neural disease exemplified through untypical concurrent signal discharge from the neurons present in the brain region. This abnormal brain functionality could be captured through electroencephalography (EEG) system. Generally the observed EEG signals are examined by the experienced neurologist, which may be time consuming when observing hours of EEG signal. Therefore, this proposed work provides a fully automatic epileptic seizure detection system by means of the multi-domain features along with various machine learning algorithms. Initially, the obtained EEG signals are processed to clear noise and artefacts. Subsequently, the pre-processed signals are segregated as 5 seconds epochs and for each epoch various features are extracted from frequency domain, time domain. Additionally entropy, correlation and graph theory approaches has been used for analysis the connectivity of the brain network. Subsequently, distinguishable features are chosen carefully in this regard from the immense feature set by virtue of multi-objective evolutionary method and convincingly, classification has been performed using support vector machine(SVM).A Bayesian optimization (BaO) algorithm was utilized to optimize the SVM’s hyper-plane parameters. In addition, Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA),Random Forest Ensemble (RFE) and k-Nearest Neighbor Ensemble (k- NNE) was also used for comparing the proposed results. These obtained results validates by considering the performance of this work is competing along with state-of the-arts approaches. The proposed work is implemented on a CHB-MIT database .The obtained performance measure of the classifiers are 99.09%, 81.49%,80.90%,76.85% and 84.14 % in SVM , LDA, QDA, k- NNE and RFE respectively. Finally SVM with Bayesian Optimization (BaO) algorithm outperforms than other classifiers with accuracy, AUC, sensitivity and specificity, as 99.09%, 99.67%, 98.06% and 98.12%, respectively. 
Keywords: About four Key Words or Phrases in Alphabetical Order, Separated by Commas.
Scope of the Article:  Classification