Classification of EEG signals for epileptic seizures using Levenberg-Marquardt algorithm based Multilayer Perceptron Neural Network

Issue title: Special Section: Soft Computing and Intelligent Systems: Techniques and Applications

Guest editors: Sabu M. Thampi, El-Sayed M. El-Alfy, Sushmita Mitra and Ljiljana Trajkovic

Article type: Research Article

Authors: Narang, Ankit^{a; *} | Batra, Bhumika^a | Ahuja, Arpit^a | Yadav, Jyoti^a | Pachauri, Nikhil^b

Affiliations: [a] Department of Instrumentation and Control, Netaji Subhas Institute of Technology, Azad Hind Fauj Marg, Dwarka, Delhi, India | [b] Department of Electrical and Electronics Engineering, Delhi Technical Campus, Greater Noida, Uttar Pradesh, India

Correspondence: [*] Corresponding author. Ankit Narang, Department of Instrumentation and Control, Netaji Subhas Institute of Technology, Azad Hind Fauj Marg, Sector-3, Dwarka, Delhi, India. E-mail: [email protected].

Abstract: EEG is the most effective diagnostic technique to determine epilepsy in a patient. The objective of this research work is to apply classification techniques on EEG signals to determine whether the patient has suffered from epileptic seizure. This is carried out through the extraction of various time and frequency domain features. The two classifiers, i.e. Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used and compared using various evaluation parameters. The simulation results and corresponding quantitative analysis shows that ANN classifier is superior to SVM.

Keywords: Artificial neural network, support vector machine, EEG signal

DOI: 10.3233/JIFS-169460

Journal: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1669-1677, 2018