A new Approach to Epilepsy Diagnosis based on the Social Spider Algorithm and the Support Vector Machine
Subject Areas : Computer Engineering and IT
1 -
Keywords: Epileptic Seizure Detection, Features Selection, Electroencephalogram Signals, Social Spider Optimization algorithm.,
Abstract :
Epilepsy can be defined as recurrent seizures caused by sudden electrical discharges in a group of human brain cells. Electroencephalogram (EEG) signals play a very important role in the diagnosis of this disease. EEG signal recordings, which are recorded by portable recording devices, produce very long data, and the detection of the epileptic zone requires a long time for the analysis of all the data by a specialist. Traditional analysis methods are tedious, so in recent years, a large number of automatic systems for epilepsy diagnosis have emerged. In this paper, a new approach based on the social spider optimization algorithm (SSO) is presented. First, the EEG signal is decomposed based on the discrete wavelet transform. Then, 132 statistical features, entropy and chaos features are extracted, and then the best features are selected by the SSO with the proposed objective function. Finally, the selected features are used to classify seizure and non-seizure EEG signals by support vector machine algorithm. Simulation results show that the proposed method has good accuracy and sensitivity on Bonn university dataset, and this method can effectively help doctors in diagnosing epilepsy, thus reducing their workload.
References:
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[41]ف. قرهداغی س. مشگینی, "تشخیص حملههای صرعی از روی ضرایب موجک با استفاده از شبکههای عصبی مصنوعی و الگوریتم بهینهسازی انبوه ذرات (PSO)," هوش محاسباتی در مهندسی برق, vol. 10, no. 3, pp. 11-24, 2019, doi: 10.22108/isee.2019.116284.1207.
[1] S. Mekruksavanich, W. Phaphan, and A. Jitpattanakul, "Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism," Mathematical Biosciences and Engineering, vol. 22, no. 1, pp. 73-105, 2025, DOI: 10.3934/mbe.2025004
[2] M. Pouryosef, R. Abedini-Nassab, and S. M. R. Akrami, "A novel framework for epileptic seizure detection using electroencephalogram signals based on the bat feature selection algorithm," Neuroscience, vol. 541, pp. 35-49, 2024, DOI: 10.1016/j.neuroscience.2024.01.014
[3] X. Qiu, F. Yan, and H. Liu, "A difference attention ResNet-LSTM network for epileptic seizure detection using EEG signal," Biomedical Signal Processing and Control, vol. 83, p. 104652, 2023, DOI: 10.1016/j.bspc.2023.104652
[4] M. Shen, P. Wen, B. Song, and Y. Li, "An EEG based real-time epilepsy seizure detection approach using discrete wavelet transform and machine learning methods," Biomedical Signal Processing and Control, vol. 77, p. 103820, 2022, DOI: 10.1016/j.bspc.2022.103820
[5] J. Zhang, S. Zheng, W. Chen, G. Du, Q. Fu, and H. Jiang, "A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction," Scientific Reports, vol. 14, no. 1, p. 16916, 2024, DOI: 10.1038/s41598-024-67855-4
[6] H. Ocak, "Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy," Expert Systems with Applications, vol. 36, no. 2, pp. 2027-2036, 2009, DOI: 10.1016/j.eswa.2007.12.065
[7] S. Jain, V. V. Gupta, A. S. Bist, M. Joshi, and A. Garg, "Automated Epileptic Seizure Detection of EEG Signals Using Machine Learning," in International Conference on Cognitive Computing and Cyber Physical Systems, 2025: Springer, pp. 349-357. DOI: 10.1007/978-981-97-7371-8_27
[8] G. Yogarajan et al., "EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network," Scientific Reports, vol. 13, no. 1, p. 17710, 2023. DOI: 10.1038/s41598-023-44318-w
[9] M. Omidvar, A. Zahedi, and H. Bakhshi, "EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN/SVM classifiers," Journal of ambient intelligence and humanized computing, vol. 12, no. 11, pp. 10395-10403, 2021. DOI: 10.1007/s12652-020-02837-8.
[10] M. P. Kantipudi, N. P. Kumar, R. Aluvalu, S. Selvarajan, and K. Kotecha, "An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection," Scientific Reports, vol. 14, no. 1, p. 843, 2024. DOI: 10.1038/s41598-024-51337-8
[11] U. R. Acharya, F. Molinari, S. V. Sree, S. Chattopadhyay, K.-H. Ng, and J. S. Suri, "Automated diagnosis of epileptic EEG using entropies," Biomedical Signal Processing and Control, vol. 7, no. 4, pp. 401-408, 2012. DOI: 10.1016/j.bspc.2011.07.007
[12] D. S. T. Behara, A. Kumar, P. Swami, B. K. Panigrahi, and T. K. Gandhi, "Detection of epileptic seizure patterns in EEG through fragmented feature extraction," in 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016: IEEE, pp. 2539-2542. DOI:10.1109/INDIACom.2016.7563001
[13] Z. Zhang and K. K. Parhi, "Seizure prediction using long-term fragmented intracranial canine and human EEG recordings," in 2016 50th Asilomar Conference on Signals, Systems and Computers, 2016: IEEE, pp. 361-365. DOI: 10.1109/ACSSC.2016.7869060
[14] N. D. Truong, A. D. Nguyen, L. Kuhlmann, M. R. Bonyadi, J. Yang, and O. Kavehei, "A generalised seizure prediction with convolutional neural networks for intracranial and scalp electroencephalogram data analysis," arXiv preprint arXiv:1707.01976, 2017. DOI: 10.48550/arXiv.1707.01976
[15] M. Niknazar, S. Mousavi, B. V. Vahdat, and M. Sayyah,"A new framework based on recurrence quantification analysis for epileptic seizure detection," IEEE journal of biomedical and health informatics, vol. 17, no. 3, pp. 572-578, 2013. DOI:10.1109/JBHI.2013.2255132
[16] N. Sadati, H. R. Mohseni, and A. Maghsoudi, "Epileptic seizure detection using neural fuzzy networks," in 2006 IEEE international conference on fuzzy systems, 2006: IEEE, pp. 596-600, DOI: 10.1109/FUZZY.2006.1681772
[17] U. Orhan, M. Hekim, and M. Ozer, "EEG signals classification using the K-means clustering and a multilayer perceptron neural network model," Expert Systems with Applications, vol. 38, no. 10, pp. 13475-13481, 2011. DOI: 10.1016/j.eswa.2011.04.149
[18] A. R. Naghsh-Nilchi and M. Aghashahi, "Epilepsy seizure detection using eigen-system spectral estimation and Multiple Layer Perceptron neural network," Biomedical Signal Processing and Control, vol. 5, no. 2, pp. 147-157, 2010. DOI: 10.1016/j.bspc.2010.01.004
[19] H. Ocak, "Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm," Signal processing, vol. 88, no. 7, pp. 1858-1867, 2008.
DOI: 10.1016/j.sigpro.2008.01.026
[20] A. Jain and D. Zongker, "Feature selection: Evaluation, application, and small sample performance," IEEE transactions on pattern analysis and machine intelligence, vol. 19, no. 2, pp. 153-158, 1997. DOI: 10.1109/34.574797
[21] S. Mousavi, M. Niknazar, and B. V. Vahdat, "Epileptic seizure detection using AR model on EEG signals," in 2008 Cairo International Biomedical Engineering Conference, 2008: IEEE, pp. 1-4. DOI:10.1109/CIBEC.2008.4786067
[22] L. Wang et al., "Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis," Entropy, vol. 19, no. 6, p. 222, 2017.
DOI: 10.3390/e19060222
[23] A. Hamad, E. H. Houssein, A. E. Hassanien, and A. A. Fahmy, "A hybrid EEG signals classification approach based on grey wolf optimizer enhanced SVMs for epileptic detection," in Proceedings of the international conference on advanced intelligent systems and informatics 2017, 2018: Springer, pp. 108-117. DOI: 10.1007/978-3-319-64861-3_10
[24] Z. Hossein-Nejad and M. Nasri, "Epileptic Seizure Detection based on Statistical and Wavelet Features and Siamese Network," in 2023 14th International Conference on Information and Knowledge Technology (IKT), 2023: IEEE, pp. 201-205. DOI:10.1109/IKT62039.2023.10433025
[25] P. Mallik, A. K. Nayak, S. K. Mohapatra, and K. Swain, "Effectiveness of SOSA, FFA and GOA Algorithms for Epileptic Seizure Detection Using DWT and SVM," in Computing, Communication and Intelligence: CRC Press, 2025, pp. 52-56. DOI:10.1201/9781003581215-11
[26] Z. Hossein, "Social Spider Optimization Algorithm in Multimodal Medical Image Registration," Journal of Optimization of Soft Computing (JOSC), vol. 1, no. 1, pp. 23-30, 2023. https://www.researchgate.net/publication/375279624
[27] A. Luque-Chang, E. Cuevas, F. Fausto, D. Zaldívar, and M. Pérez, "Social spider optimization algorithm: modifications, applications, and perspectives," Mathematical Problems in Engineering, vol. 2018, no. 1, p. 6843923, 2018. DOI: 10.1155/2018/6843923
[28] R. Vijay and V. Priya, "Anti-islanding protection of distributed generation based on social spider optimization technique," International Journal of Advanced Engineering Research and Science, vol. 4, no. 6, p. 237193, 2017. DOI: 10.22161/ijaers.4.6.5
[29] U. R. Acharya et al., "Characterization of focal EEG signals: a review," Future Generation Computer Systems, vol. 91, pp. 290-299, 2019. DOI: 10.1016/j.future.2018.08.044
[30] P. Boonyakitanont, A. Lek-Uthai, K. Chomtho, and J. Songsiri, "A review of feature extraction and performance evaluation in epileptic seizure detection using EEG," Biomedical Signal Processing and Control, vol. 57, p. 101702, 2020. DOI: 10.1016/j.bspc.2019.101702
[31] M. Ahmad, M. Saeed, S. Saleem, and A. M. Kamboh, "Seizure detection using EEG: A survey of different techniques," in 2016 International Conference on Emerging Technologies (ICET), 2016: IEEE, pp. 1-6.
DOI:10.1109/ICET.2016.7813209
[32] E. Cuevas, M. Cienfuegos, D. Zaldívar, and M. Pérez-Cisneros, "A swarm optimization algorithm inspired in the behavior of the social-spider," Expert Systems with Applications, vol. 40, no. 16, pp. 6374-6384, 2013. DOI: 10.1016/j.eswa.2013.05.041
[33] E. Cuevas and M. Cienfuegos, "A new algorithm inspired in the behavior of the social-spider for constrained optimization," Expert Systems with Applications, vol. 41, no. 2, pp. 412-425, 2014. DOI: 10.1016/j.eswa.2013.07.067
[34] C. J. Burges, "A tutorial on support vector machines for pattern recognition," Data mining and knowledge discovery, vol. 2, no. 2, pp. 121-167, 1998. doi: 10.1023/A:1009715923555
[35] M. K. Siddiqui, R. Morales-Menendez, X. Huang, and N. Hussain, "A review of epileptic seizure detection using machine learning classifiers," Brain informatics, vol. 7, no. 1, p. 5, 2020. DOI:10.1186/s40708-020-00105-1
[36] R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, "Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state," Physical Review E, vol. 64, no. 6, p. 061907, 2001. DOI: 10.1103/PhysRevE.64.061907
[37] T. Zhang, W. Chen, and M. Li, "Generalized Stockwell transform and SVD-based epileptic seizure detection in EEG using random forest," Biocybernetics and Biomedical Engineering, vol. 38, no. 3, pp. 519-534, 2018.
DOI:10.1016/j.bbe.2018.03.007
[38] M. Mahmud, M. S. Kaiser, A. Hussain, and S. Vassanelli, "Applications of deep learning and reinforcement learning to biological data," IEEE transactions on neural networks and learning systems, vol. 29, no. 6, pp. 2063-2079, 2018. DOI: 10.1109/TNNLS.2018.2790388
[39] E. Aydemir, T. Tuncer, and S. Dogan, "A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method," Medical hypotheses, vol. 134, p. 109519, 2020. DOI: 10.1016/j.mehy.2019.109519
[40] T. Iloon, R. Barati, and H. Azad, "A New Approach in Epilepsy Diagnosis using Discrete Wavelet Transformation and Analysis of Variance," Signal Processing and Renewable Energy, vol. 7, no. 3, pp. 1-11, 2023. https://oiccpress.com/spre/article/view/7894
[41]ف. قرهداغی س. مشگینی, "تشخیص حملههای صرعی از روی ضرایب موجک با استفاده از شبکههای عصبی مصنوعی و الگوریتم بهینهسازی انبوه ذرات (PSO)," هوش محاسباتی در مهندسی برق, vol. 10, no. 3, pp. 11-24, 2019, doi: 10.22108/isee.2019.116284.1207.
