Impact of EEG Rhythm on the Prognosis of Epilepsy

PDF (808KB), PP.1-13

Views: 0 Downloads: 0

Author(s)

Neeta H. Chapatwala 1 Chirag N. Paunwala 2 Shankar K. Parmar 3

1. Gujarat Technological University, Ahmedabad, Gujarat, India

2. E & C Engineering, SCET, Surat, Gujarat, India

3. Government Engineering College, Godhra, Gujarat, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2025.05.01

Received: 29 Nov. 2024 / Revised: 20 May 2025 / Accepted: 26 Jun. 2025 / Published: 8 Oct. 2025

Index Terms

EEG Rhythm, Epilepsy, prediction, SVM, Machine Learning

Abstract

A chronic neurological disorder called epilepsy is characterized by frequent, unplanned seizures. A seizure is an unexpected and uncontrolled electrical disturbance in the brain that can cause a variety of physical and behavioral symptoms. Prognosis of epilepsy can be done based on pre-ictal (prior to seizure) signal variations in Electroencephalogram (EEG) rhythm. EEG rhythm like alpha, beta, theta and delta are substantial for epilepsy analysis. This study aimed to investigate the impact of various features from EEG rhythm and the feature reduction in classification of pre-ictal and inter-ictal (between two seizures) signal. Dataset of CHB-MIT comprises of 23 patients with 23 channels are used to extract Time, Frequency and Time-frequency features from EEG rhythms. Analysis shows that, compared to other bands, beta band features show major variation in pre-ictal and inter-ictal phases, which makes training of a Support Vector Machine (SVM) classifier easy for prediction of seizures. Further reduction in feature size using statistical analysis helped to achieve 75% reduction in computation. Results show average sensitivity of 93% and false positive rate of 0.14 per hour. The proposed method classified pre-ictal signal with maximum accuracy of 95%, sensitivity of 100%, specificity of 93% and false positive rate of 0.07per hour with reduced complexity compare to other state of art methods.

Cite This Paper

Neeta H. Chapatwala, Chirag N. Paunwala, Shankar K. Parmar, "Impact of EEG Rhythm on the Prognosis of Epilepsy", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.17, No.5, pp. 1-13, 2025. DOI:10.5815/ijigsp.2025.05.01

Reference

[1]Biset, G., Abebaw, N., Gebeyehu, N. A., Estifanos, N., Birrie, E., & Tegegne, K. D. (2024). Prevalence, incidence, and trends of epilepsy among children and adolescents in Africa: a systematic review and meta-analysis. BMC Public Health, 24(1), 771
[2]Tao, Shan., Yahui, Zhu., Haozhi, Fan., Zeye, Liu., Jing, Xie., Mao, Li., Shenqi, Jing. (2024). 1. Global, regional, and national time trends in the burden of epilepsy, 1990–2019: an age-period-cohort analysis for the global burden of disease 2019 study. Frontiers in Neurology, doi: 10.3389/fneur.2024.1418926
[3]Gordon, Wright. (2023). The Pathogenesis of Epilepsy. 3(1):368-376. doi: 10.54254/2753-8818/3/20220274
[4]Poovarasu, S., Gopinath, R., Mohanraj, A., & Sakthivarshan, S. (2024, April). Epileptic Seizure Diagnosis using Bayesian Belief Network and T-CNN. In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS) (Vol. 1, pp. 1-6). IEEE
[5]Han, K., Liu, C., & Friedman, D. (2024). Artificial intelligence/machine learning for epilepsy and seizure diagnosis. Epilepsy & Behavior, 155, 109736
[6]Biju, K. S. (2023, May). Comparative Study on Epilepsy Prediction Using Modern Techniques. In 2023 International Conference on Control, Communication and Computing (ICCC) (pp. 1-6). IEEE.
[7]M, Anaz, Mohammed, Vishar., D, Hepsiba., R, Prasanna., L., D., Vijay, Anand. (2024). 6. Machine Learning-Based Early Seizure Detection: A Random Forest Classifier Approach for Pre-Ictal Stage Prediction in Epilepsy.   doi: 10.1109/icbsii61384.2024.10564015
[8]P. L. B. and B. K. S., "Comparative Study on Epilepsy Prediction Using Modern Techniques," 2023 International Conference on Control, Communication and Computing (ICCC), Thiruvananthapuram, India, 2023, pp. 1-6, doi: 10.1109/ICCC57789.2023.10165539
[9]Nazari, J., Motie Nasrabadi, A., Menhaj, M. B., & Raiesdana, S. (2022). Epilepsy seizure prediction with few-shot learning method. Brain Informatics, 9(1), 21
[10]Costa, G., Teixeira, C., & Pinto, M. F. (2024). Comparison between epileptic seizure prediction and forecasting based on machine learning. Scientific Reports, 14(1), 5653
[11]Jennifer, L., Hellar., Negar, Erfanian., Behnaam, Aazhang. (2022). 7. Epileptic electroencephalography classification using embedded dynamic mode decomposition. Journal of Neural Engineering, doi: 10.1088/1741-2552/ac7256
[12]A. Sharma, "Epileptic seizure prediction using power analysis in beta band of EEG signals," 2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI), Faridabad, 2015, pp. 117- 121. doi: 10.1109/ICSCTI.2015.7489552
[13]M. Z. Parvez and M. Paul, "EEG signal classification using frequency band analysis towards epileptic seizure prediction," Computer and Information Technology (ICCIT), 2013 16th International Conference on, Khulna, 2014, pp. 126-130. doi: 10.1109/ICCITechn.2014.6997315 
[14]Sadaye, R., Parekh, S., Samuel, J., & Deulkar, K. (2016). Epileptic seizure prediction using power spectrum and amplitude analysis of beta band of EEG signals. Int J Comp Appl, 155, 13-7.
[15]Stoller A (1949) Slowing of the alpha-rhythm of the electroencephalogram and its association with mental deterioration and epilepsy. J Mental Sci 95(401):972–984 
[16]Larsson PG, Kostov H (2005) Lower frequency variability in the alpha activity in eeg among patients with epilepsy. Clin Neurophysiol 116(11):2701–2706 
[17]Pyrzowski J, Siemiński M, Sarnowska A, Jedrzejczak J, Nyka WM (2015) Interval analysis of interictal eeg: pathology of the alpha rhythm in focal epilepsy. Sci Rep 5(1):1–10 
[18]Douw L, van Dellen E, de Groot M, Heimans JJ, Klein M, Stam CJ, Reijneveld JC (2010) Epilepsy is related to theta band brain connectivity and network topology in brain tumor patients. BMC Neurosci 11(1):1–10 
[19]Chauviere L, Rafraf N, Thinus-Blanc C, Bartolomei F, Esclapez M, Bernard C (2009) Early deficits in spatial memory and theta rhythm in experimental temporal lobe epilepsy. J Neurosci 29(17):5402–5410 
[20]Panet-Raymond D, Gotman J (1990) Asymmetry in delta activity in patients with focal epilepsy. Electroencephalogr Clin Neurophysiol 75(6):474–481
[21]Hejazi, M., & Motie Nasrabadi, A. (2019). Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods. Cognitive neurodynamics, 13, 461-473.
[22]Li, F., Liang, Y., Zhang, L., Yi, C., Liao, Y., Jiang, Y., Si, Y., Zhang, Y., Yao, D., Yu, L., & Xu, P. (2019). Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis. Cognitive neurodynamics, 13(2), 175–181. https://doi.org/10.1007/s11571-018-09517-6
[23]Sorokin, J. M., Paz, J. T., & Huguenard, J. R. (2016). Absence seizure susceptibility correlates with pre-ictal β oscillations. Journal of physiology, Paris, 110(4 Pt A), 372–381. https://doi.org/10.1016/j.jphysparis.2017.05.004
[24]Chapatwala, N., Paunwala, C. N., & Doctor, A. (2023, October). SVM-Based Pre-Ictal Phase Detection for Epileptic Seizure Analysis. In 2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 284-289). IEEE.
[25]Tian, Fengshi, Jie Yang, Shiqi Zhao, and Mohamad Sawan. 2021. “A New Neuromorphic Computing Approach for Epileptic Seizure Prediction,” 1–5. https://doi.org/10.1109/iscas51556.2021.9401560.
[26]Usman, Syed Muhammad, Shehzad Khalid, and Zafar Bashir. 2021. “Epileptic Seizure Prediction Using Scalp Electroencephalogram Signals.” Biocybernetics and Biomedical Engineering 41 (1): 211–20. https://doi.org/10.1016/j.bbe.2021.01.001.
[27]Yun, S., Park., Lan, Luo., Keshab, K., Parhi., Theoden, I., Netoff. (2011). 6. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia,  doi: 10.1111/J.1528-1167.2011.03138.X
[28]Wang, Ziyu, Jie Yang, and Mohamad Sawan. 2021. “A Novel Multi-Scale Dilated 3D CNN for Epileptic Seizure Prediction,” 3–6. https://doi.org/10.1109/aicas51828.2021.9458571.
[29]Dissanayake, Theekshana, Tharindu Fernando, Simon Denman, Sridha Sridharan, and Clinton Fookes. 2020. “Patient-Independent Epileptic Seizure Prediction Using Deep Learning Models” XX (Xx): 1–10.
[30]Kalita, D., Dash, S., & Mirza, K. B. (2024). EPINET: AN OPTIMIZED, RESOURCE EFFICIENT DEEP GRU-LSTM NETWORK FOR EPILEPTIC SEIZURE PREDICTION. Biomedical Engineering: Applications, Basis and Communications, 2450021.
[31]Zhang, Yuan, Yao Guo, Po Yang, Wei Chen, and Benny Lo. 2020. “Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network.” IEEE Journal of Biomedical and Health Informatics 24 (2): 465–74. https://doi.org/10.1109/JBHI.2019.2933046.
[32]Muhammad Mateen Qureshi1 • Muhammad Kaleem1“EEG-based seizure prediction with machine learning” Signal, Image and Video Processing, Springer 2022
[33]Inouye, T., Matsumoto, Y., Shinosaki, K., Iyama, A., & Toi, S. (1994). Increases in the power spectral slope of background electroencephalogram just prior to asymmetric spike and wave complexes in epileptic patients. Neuroscience Letters, 181(1–2), 175–178. https://doi.org/10.1016/0304-3940(94)90182-1
[34]Litt, B., Esteller, R., D'Alessandro, M., Echauz, J., Shor, R., Bowen, C., & Vachtsevanos, G. (1999). Evolution of accumulated energy predicts seizures in mesial temporal lobe epilepsy. In Proceedings of the First Joint BMES/EMBS Conference: 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Vol. 1, p. 440). IEEE.
[35]Saurabh Pal, R. A. (2021). Elimination and Backward Selection of Features (P-Value Technique) In Prediction of Heart Disease by Using Machine Learning Algorithms. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(6), 2650–2665.IEEE. https://doi.org/10.17762/turcomat.v12i6.5765
[36]Islam and R. Islam, "Exploring the Impact of Univariate Feature Selection Method on Machine Learning Algorithms for Heart Disease Prediction," 2023 International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM), Gazipur, Bangladesh, 2023, pp. 1-5.https:// doi: 10.1109/NCIM59001.2023.10212832.
[37]Dhanya, R., Paul, I. R., Akula, S. S., Sivakumar, M., & Nair, J. J. (2020). F-test feature selection in stacking ensemble model for breast cancer prediction. Procedia Computer Science, 171, 1561–1570. https://doi.org/10.1016/j.procs.2020.04.167
[38]Usman, S. M., Khalid, S., & Aslam, M. H. (2020). Epileptic seizures prediction using deep learning techniques. Ieee Access, 8, 39998-40007.
[39]Assali, I., Ghazi Blaiech, A., Ben Abdallah, A., Ben Khalifa, K., Carrère, M., & Hédi Bedoui, M. (2023). CNN-based classification of epileptic states for seizure prediction using combined temporal and spectral features. Biomedical Signal Processing and Control, 82, 104519. https://doi.org/10.1016/j.bspc.2022.104519
[40]Jia, M., Liu, W., Duan, J., Chen, L., Chen, C. P., Wang, Q., & Zhou, Z. (2022). Efficient graph convolutional networks for seizure prediction using scalp EEG. Frontiers in Neuroscience, 16, 967116
[41]Esmaeilpour, A., Tabarestani, S. S., & Niazi, A. (2024). Deep learning‐based seizure prediction using EEG signals: A comparative analysis of classification methods on the CHB‐MIT dataset. Engineering Reports, e12918.
[42]Shafiezadeh, S., Duma, G. M., Mento, G., Danieli, A., Antoniazzi, L., Del Popolo Cristaldi, F., ... & Testolin, A. (2024). Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting. Sensors, 24(9), 2863.
[43]Sadaye, R., Parekh, S., Samuel, J., & Deulkar, K. (2016). Epileptic seizure prediction using power spectrum and amplitude analysis of beta band of EEG signals. Int J Comp Appl, 155, 13-7.
[44]Altaf, Z., Unar, M. A., Narejo, S., & Zaki, M. A. (2023). Generalized epileptic seizure prediction using machine learning method. International Journal of Advanced Computer Science and Applications, 14(1). 
[45]Fıçıcı, C., Telatar, Z., & Eroğul, O. (2022). Automated temporal lobe epilepsy and psychogenic nonepileptic seizure patient discrimination from multichannel EEG recordings using DWT based analysis. Biomedical Signal Processing and Control, 77, 103755.
[46]Nazari, J., Motie Nasrabadi, A., Menhaj, M. B., & Raiesdana, S. (2022). Epilepsy seizure prediction with few-shot learning method. Brain Informatics, 9(1), 21.
[47]Tsiouris, K. M., Konitsiotis, S., Koutsouris, D. D., & Fotiadis, D. I. (2019, May). Unsupervised seizure detection based on rhythmical activity and spike detection in EEG signals. In 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (pp. 1-4). IEEE.
[48]Wang Z., & Mengoni, P. (2022). Seizure classification with selected frequency bands and EEG   montages: a Natural Language Processing approach. Brain Informatics, 9(1), 11.
[49]Guttag, J. (2010). CHB-MIT Scalp EEG Database (version 1.0.0). PhysioNet. RRID:SCR_007345. https://doi.org/10.13026/C2K01R
[50]Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220. RRID:SCR_007345.