RESEARCH ARTICLE
Performance Analysis of Fuzzy Multilayer Support Vector Machine for Epileptic Seizure Disorder Classification using Auto Regression Features
T. Rajendran1, *
Article Information
Identifiers and Pagination:
Year: 2019Volume: 13
Issue: Suppl-1, M2
First Page: 103
Last Page: 113
Publisher ID: TOBEJ-13-103
DOI: 10.2174/1874120701913010103
Article History:
Received Date: 02/03/2019Revision Received Date: 02/04/2019
Acceptance Date: 29/04/2019
Electronic publication date: 17/12/2019
Collection year: 2019
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
Around 1% of the total population in the world suffers from epilepsy, a central nervous system disorder. Epilepsy is the neurological disorder of the human brain which can affect people of all ages. Classification techniques and Signal processing are basic methods in the advancement of an algorithm for seizure detection. The primary procedures of a typical biomedical evaluation and processing framework are data acquisition, feature extraction, preprocessing, and classification. Based on this, seizure detection is performed by using the following two methods.
Methods:
This paper proposes a technique for the classification of EEG signals to detect the epileptic seizures by using Cascade Forward Backpropagation Neural Network (CFBNN) and Fuzzy Multilayer Support Vector Machine (FMSVM) methods.
Results:
Finally, the results of developed classifiers are identified with seizure disorder activities. This research concentrated on Parametric Features such as AR (Autoregressive) Burg, AR YuleWalker, AR Covariance, AR Modified Covariance, and Levinson Durbin Recursion. Linear Prediction Coefficient was analyzed with the EEG dataset gathered from Karunya University. The sensitivity, specificity, and accuracy were calculated for the proposed classifiers.
Conclusion:
The results of the proposed classifiers were computed with minimum and maximum accuracy and these results were compared with the previous results of the classifiers like FFNN, and PNN as shown in the tables. Based on the obtained outputs and calculated parametric functions, the results validated that the FMSVM classifier performed better in the detection of epileptic seizure disorder in terms of accuracy, sensitivity and specificity.
