Authors:
Kaliprasanna Swain,Tan Kuan Tak,Kamal Upreti,Pravin R. Kshirsagar,Sivaneasan Bala Krishnan,Ramesh Chandra Poonia,Sumant Kumar Mohapatra,Sumya Ranjan Nayak8,DOI NO:
https://doi.org/10.26782/jmcms.2025.06.00005Keywords:
Idiopathic Partial Epilepsy (IPE),Idiopathic Epilepsy (IE),Modified Particle Swarm Optimization (MPSO),ANFIS,Abstract
Epilepsy, a complex neurological disorder, is particularly challenging to diagnose and manage when driven by genetic factors. This study focuses on the analysis of Idiopathic Partial Epilepsy (IPE) and Idiopathic Epilepsy (IE) in both children and women, using a novel approach combining Modified Particle Swarm Optimization (MPSO) with a 9-rule Adaptive Neuro-Fuzzy Inference System (ANFIS). Four feature extraction techniques—Discrete Wavelet Transform (DWT), Shearlet Transform (SLT), Contourlet Transform (CLT), and Stockwell Transform (SWT)—are employed to process electroencephalogram (EEG) signals. The performance of the proposed MPSO-ANFIS model is evaluated and compared with existing methods. Results indicate that the SWT-ANFIS-MPSO method achieves superior classification accuracy for both IE and IPE patients, highlighting its potential to improve epilepsy diagnosis and treatment strategies.Refference:
I. A. E. Hramov, A. A. Koronovskii, V. A. Makarov, A. N. Pavlov, E. Sitnikova. : ‘Mathematical methods of signal processing in neuroscience’. In: Wavelets in Neuroscience. Springer Series in Synergetics. pp. 1–13, 2021. 10.1007/978-3-662-43850-3_1
II. A. K. Jaiswal, H. Banka. : ‘Local pattern transformation-based feature extraction techniques for classification of epileptic EEG signals’. Biomedical Signal Processing and Control. Vol. 34, pp. 81–92, 2017. 10.1016/j.bspc.2017.01.005
III. A. Subasi. : ‘EEG signal classification using wavelet feature extraction and a mixture of expert model’. Expert Systems with Applications. Vol. 32, pp. 1048–1093, 2007. 10.1016/j.eswa.2006.02.005
IV. D. Rafik, B. Hocine, M. C. A. Cherif. : ‘Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals’. Biocybernetics and Biomedical Engineering. Vol. 36, pp. 285–291, 2016. 10.1016/j.bbe.2015.10.006
V. E. D. Ubeyli. : ‘Eigenvector methods for automated detection of electrocardiographic changes in partial epileptic patients’. IEEE Transactions on Information Technology in Biomedicine. Vol. 13(4), 2009. 10.1109/TITB.2008.920614
VI. G. Chen. : ‘Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features’. Expert Systems with Applications. Vol. 41, pp. 2391–2394, 2014. 10.1016/j.eswa.2013.09.037
VII. G. Ling, R. Daniel, A. S. Jose, P. Alejandro. : ‘Classification of EEG signals using relative wavelet energy and artificial neural networks’. GEC Conference Proceedings. June, pp. 177–183, 2009. 10.1145/1543834.1543860.
VIII. H. Adeli, N. Dadmehr. : ‘Analysis of EEG records in an epileptic patient using wavelet transform’. Journal of Neuroscience Methods. Vol. 123, pp. 69–87, 2003. 10.1016/s0165-0270(02)00340-0
IX. H. Liang, Z. Lin, F. Yin. : ‘Removal of ECG contamination from diaphragmatic EMG by nonlinear filtering’. Nonlinear Analysis: Theory, Methods & Applications. Vol. 63, pp. 745–753, 2005. 10.1016/j.na.2004.09.018
X. M. N. Do, M. Vetterli. : ‘The contourlet transform: An efficient directional multiresolution image representation’. IEEE Transactions on Image Representation. Vol. 14, pp. 2091–2106, 2006. 10.1109/TIP.2005.859376
XI. P. Swami, A. K. Godiyal, J. Santhosh, B. K. Panigrahi, M. Bhatia, S. Anand. : ‘Robust expert system design for automated detection of epileptic seizures using SVM classifier’. Proceedings of the IEEE International Conference on Parallel, Distributed and Grid Computing. pp. 219–222, Dec 2014. 10.1109/PDGC.2014.7030745
XII. P. Swami, T. K. Gandhi, B. K. Panigrahi, M. Tripathi, S. Anand. : ‘A novel robust diagnostic model to detect seizures in electroencephalography’. Expert Systems with Applications. Vol. 56, pp. 116–130, 2016. 10.1016/j.eswa.2016.02.040
XIII. R. B. Pachori, S. Patidar. : ‘Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions’. Computer Methods and Programs in Biomedicine. Vol. 113, pp. 494–502, 2014. 10.1016/j.cmpb.2013.11.014
XIV. R. Fisher, W. van Emde Boas, W. Blume, C. Elger, P. Genton, P. Lee, J. Engel. : ‘Epileptic seizures and epilepsy: definitions proposed by the ILAE and the IBE’. Epilepsia. Vol. 46, pp. 470–472, 2005. 10.1111/j.0013-9580.2005.66104.x
XV. R. G. Stockwell. : ‘A basis for efficient representation of the S-transform’. Digital Signal Processing. Vol. 17(1), pp. 371–393, 2007. 10.1016/j.dsp.2006.04.006
XVI. S. Chandaka, A. Chatterjee, S. Munshi. : ‘Cross-correlation aided support vector machine classifier for classification of EEG signals’. Expert Systems with Applications. Vol. 36, pp. 1329–1336, 2009. 10.1016/j.eswa.2007.11.017
XVII. S. Patidar, T. Panigrahi. : ‘Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals’. Biomedical Signal Processing and Control. Vol. 34, pp. 74–80, 2017. 10.1016/j.bspc.2017.01.001
XVIII. T. Gandhi, B. K. Panigrahi, S. Anand. : ‘A comparative study of wavelet families for EEG signal classification’. Neurocomputing. Vol. 74, pp. 3051–3057, 2011. 10.1016/j.neucom.2011.04.029
XIX. V. Joshi, R. B. Pachori, A. Vijesh. : ‘Classification of ictal and seizure-free EEG signals using fractional linear prediction’. Biomedical Signal Processing and Control. Vol. 9, pp. 1–5, 2014. 10.1016/j.bspc.2013.08.006
XX. W. Q. Lim. : ‘The discrete shearlet transform: A new directional transform and compactly supported shearlet frames’. IEEE Transactions on Image Processing. Vol. 19, pp. 1166–1180, 2010. 10.1109/TIP.2010.2041410