http://www.iaeme.com/IJARET/index.asp 1938 editor@iaeme.com International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 12, December 2020, pp. 1938-1947, Article ID: IJARET_11_12_184 Available online at http://www.iaeme.com/ijaret/issues.asp?JType=IJARET&VType=11&IType=12 ISSN Print: 0976-6480 and ISSN Online: 0976-6499 DOI: 10.34218/IJARET.11.12.2020.184 © IAEME Publication Scopus Indexed SEIZURE EEG SIGNALS DETECTION AND CLASSIFICATION USING WAVELET TRANSFORM AND WOA BASED LLRBFNN MODEL Sreelekha Panda Raajdhani Engineering College, Bhubaneswar, India, Satyasis Mishra* Department of Electronics and Communication Engineering, Centurion University of Technology and Management, Odisha, India Mihir Narayana Mohanty Department of Electronics and Communication Engineering, ITER, Siksha ‗O‘ Anusandhan University, Bhubaneswar, Odisha, India *Corresponding Author ABSTRACT This paper presents a novel classification WOA-LLRBFNN (Whale optimization algorithm- Local Linear Radial Basis Function neural network) Model for classification of seizure EEG signal. The Bonn dataset has been applied for classification and detection of the seizure. The wavelet transform has been used for detection of seizure from the dataset. There are three wavelets such as Daubechies, coiflet and symlet are considered for detection and comparison results are presented. It is found that coiflet transform has shown good detection results in comparison to the other wavelets. Further, the proposed WOA based LLRBFNN accuracy results are compared with LLRBFNN, LLWNN (Local linear wavelet transform), RBFNN model to show the robustness of the model. It is found that the WOA-LLRBFNN model achieved an accuracy of 99.12% which is higher than the other models. Keywords: Radial basis function neural network, wavelet transform, whale optimization algorithm, Local linear linear radial basis function neural network. Cite this Article: Sreelekha Panda, Satyasis Mishra and Mihir Narayana Mohanty, Seizure EEG Signals Detection and Classification using Wavelet Transform and WOA Based LLRBFNN Model. International Journal of Advanced Research in Engineering and Technology, 11(12), 2020, pp. 1938-1947. http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=12