A PSO-SVM-Based Approach for Classifying ECG and EEG Bio signals in Seizure Detection
Abstract
Early identification of epileptic activities is essential for clinical analysis and preventing advancement of the disease. Despite the development of neurological diagnostic techniques, the current analysis of epileptic seizures is still relying on a visual interpretation of electroencephalogram (EEG) signal. Neurology specialists manually perform this examination to detect patterns, a process that is both challenging and time-consuming. Biomedical signals, such as EEG and electrocardiogram (ECG), are important tools for studying human brain disorders, particularly epilepsy. This paper aims to develop a system that automatically detects epileptic seizures using discrete wavelet decomposition (DWT), particle swarm optimization (PSO), and support vector machine (SVM), thereby relieving clinicians of their challenging tasks. The proposed system employs the DWT method, PSO, and SVM. This approach has three steps. First, we introduce a method that uses a four-level discrete wavelet transform (DWT) to extract important information from electroencephalogram and electrocardiogram signals by breaking them down into useful features. Second, we optimize the SVM classifier parameters using the PSO algorithm. Finally, we classify the extracted parameters using the optimized SVM. The system achieves an average accuracy of 97.92%, a 100% recall, a 96.15% specificity, and a 0.96 AUC value. Our findings demonstrate the success of this method, showing that the PSO-optimized SVM performs significantly better in classification. In addition, our findings also demonstrate the importance of using ECG signals as supplemental data. One implication of our work is the potential for creating wearable, real-time, customized seizure warning systems. In the future, these systems will be deployed on embedded platforms in real time and validated using larger datasets.
Downloads
References
N. Margolese, A. Badeghiesh, H. Baghlaf, S. Jacobson, and M. H. Dahan, “Maternal epilepsy and pregnancy, delivery and neonatal outcomes: A population-based retrospective cohort study,” Epilepsy & Behavior, 163, p. 110221. https://doi.org/10.1016/j.yebeh.2024.110221.
World Health Organization, “Global status report on alcohol and health 2018,” World Health Organization, Geneva, 2018.
C. De Stefano, F. Fontanella, C. Marrocco, and A. Scotto Di Freca, “A GA-based feature selection approach with an application to handwritten character recognition,” Pattern Recognition Letters, vol. 35, no. 1, pp. 130–141, Jan. 2014, doi: 10.1016/j.patrec.2013.01.026.
Alalayah, K.M.; Senan, E.M.; Atlam, H.F.; Ahmed, I.A.; Shatnawi, H.S.A. Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means. Diagnostics 2023, 13, 1957. https://doi.org/10.3390/diagnostics13111957.
Das, S.; Mumu, S.A.; Akhand, M.A.H.; Salam, A.; Kamal, M.A.S. Epileptic Seizure Detection from Decomposed EEG Signal through 1D and 2D Feature Representation and Convolutional Neural Network. Information 2024, 15, 256. https://doi.org/10.3390/info15050256.
K. M. Hassan, Md. R. Islam, T. Tanaka, and Md. K. I. Molla, “Epileptic Seizure Detection from EEG Signals Using Multiband Features with Feedforward Neural Network,” in 2019 International Conference on Cyberworlds (CW), Oct. 2019, pp. 231–238. doi: 10.1109/CW.2019.00046.
A. Sharma, S. Rani, and M. Driss, “Hybrid evolutionary machine learning model for advanced intrusion detection architecture for cyber threat identification,” PLOS ONE, vol. 19, no. 9, p. e0308206, Sep. 2024, doi: 10.1371/journal.pone.0308206.
T. Shawly et al., “LHAENA: Lightweight Hybrid Attention Ensemble Network Architecture for Epileptic Seizure Detection,” Journal of Disability Research, vol. 4, p. 20250581, Jul. 2025, doi: 10.57197/JDR-2025-0581.
S. Qiu, W. Wang, and H. Jiao, “LightSeizureNet: A Lightweight Deep Learning Model for Real-Time Epileptic Seizure Detection,” IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 4, pp. 1845–1856, Apr. 2023, doi: 10.1109/JBHI.2022.3223970.
A. A. Ein Shoka, M. M. Dessouky, A. El-Sayed, and E. E.-D. Hemdan, “EEG seizure detection: concepts, techniques, challenges, and future trends,” Multimed Tools Appl, vol. 82, no. 27, pp. 42021–42051, Nov. 2023, doi: 10.1007/s11042-023-15052-2.
F. Ok, R. Rajesh, F. Ok, and R. Rajesh, “Empirical Mode Decomposition of EEG Signals for the Effectual Classification of Seizures,” in Advances in Neural Signal Processing, IntechOpen, 2020. doi: 10.5772/intechopen.89017.
S. Pattnaik, N. Rout, and S. Sabut, “Machine learning approach for epileptic seizure detection using the tunable-Q wavelet transform based time–frequency features,” Int. j. inf. tecnol., vol. 14, no. 7, pp. 3495–3505, Dec. 2022, doi: 10.1007/s41870-022-00877-1.
K. M. Hassan, Md. R. Islam, T. T. Nguyen, and Md. K. I. Molla, “Epileptic seizure detection in EEG using mutual information-based best individual feature selection,” Expert Systems with Applications, vol. 193, p. 116414, May 2022, doi: 10.1016/j.eswa.2021.116414.
H. S. Nogay and H. Adeli, “Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning,” European Neurology, vol. 83, no. 6, pp. 602–614, Jan. 2021, doi: 10.1159/000512985.
S. Benbadis, S. Beniczky, E. Bertram, S. MacIver, and S. L. Moshé, “The role of EEG in patients with suspected epilepsy,” Epileptic Disorders, vol. 22, no. 2, pp. 143–155, 2020, doi: 10.1684/epd.2020.1151.
Aayesha, M. Bilal Qureshi, M. Afzaal, M. Shuaib Qureshi, and J. Gwak, “Fuzzy-Based Automatic Epileptic Seizure Detection Framework,” Computers, Materials & Continua, vol. 70, no. 3, pp. 5601–5630, 2022, doi: 10.32604/cmc.2022.020348.
M. Sameer and B. Gupta, “CNN based framework for detection of epileptic seizures,” Multimed Tools Appl, vol. 81, no. 12, pp. 17057–17070, May 2022, doi: 10.1007/s11042-022-12702-9.
J. He, J. Cui, G. Zhang, M. Xue, D. Chu, and Y. Zhao, “Spatial–temporal seizure detection with graph attention network and bi-directional LSTM architecture,” Biomedical Signal Processing and Control, vol. 78, p. 103908, Sep. 2022, doi: 10.1016/j.bspc.2022.103908.
R. Sharma and H. K. Meena, “Emerging Trends in EEG Signal Processing: A Systematic Review,” SN COMPUT. SCI., vol. 5, no. 4, pp. 1–14, Apr. 2024, doi: 10.1007/s42979-024-02773-w.
N. S. Amer and S. B. Belhaouari, “EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review,” IEEE Access, vol. 11, pp. 143116–143142, 2023, doi: 10.1109/ACCESS.2023.3341419.
S. Bouazizi and H. Ltifi, “Enhancing accuracy and interpretability in EEG-based medical decision making using an explainable ensemble learning framework application for stroke prediction,” Decision Support Systems, vol. 178, p. 114126, Mar. 2024, doi: 10.1016/j.dss.2023.114126.
F. Mason et al., “Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review,” Journal of Clinical Medicine, vol. 13, no. 3, p. 747, Jan. 2024, doi: 10.3390/jcm13030747.
I. Mporas, V. Tsirka, E. I. Zacharaki, M. Koutroumanidis, M. Richardson, and V. Megalooikonomou, “Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients,” Expert Systems with Applications, vol. 42, no. 6, pp. 3227–3233, Apr. 2015, doi: 10.1016/j.eswa.2014.12.009.
Ali Hossam Shoeb, “Application of machine learning to epileptic seizure onset detection and treatment,” Massachusetts Institute of Technology, 2009. [Online]. Available: http://dspace.mit.edu/handle/1721.1/7582
A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. E215-220, Jun. 2000, doi: 10.1161/01.cir.101.23.e215.
J. Guttag, CHB-MIT Scalp EEG Database (version 1.0.0). PhysioNet, 2010. doi: 10.13026/C2K01R.
E. Ali, M. Angelova, and C. Karmakar, “Epileptic seizure detection using CHB-MIT dataset: The overlooked perspectives,” R Soc Open Sci, vol. 11, no. 5, p. 230601, doi: 10.1098/rsos.230601.
E. Alickovic, J. Kevric, and A. Subasi, “Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction,” Biomedical Signal Processing and Control, vol. 39, pp. 94–102, Jan. 2018, doi: 10.1016/j.bspc.2017.07.022.
S. C. Ks, A. Mishra, V. Shirhatti, and S. Ray, “Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time-Frequency Power Spectrum of Brain Signals,” J. Neurosci., vol. 36, no. 12, pp. 3399–3408, Mar. 2016, doi: 10.1523/JNEUROSCI.3633-15.2016.
L. Zougagh, H. Bouyghf, M. Nahid, and I. Sabiri, “A New Approach for Epileptic Seizure Detection from EEG and ECG Signals Using Wavelet Decomposition,” in International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023), Springer, Cham, 2024, pp. 370–378. doi: 10.1007/978-3-031-52388-5_33.
P. S. Addison, The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance, Second Edition, 2nd ed. Boca Raton: CRC Press, 2017. doi: 10.1201/9781315372556.
I. Mporas, V. Tsirka, E. I. Zacharaki, M. Koutroumanidis, M. Richardson, and V. Megalooikonomou, “Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients,” Expert Systems with Applications, vol. 42, no. 6, pp. 3227–3233, Apr. 2015, doi: 10.1016/j.eswa.2014.12.009.
L. Zougagh, H. Bouyghf, M. Nahid, and I. Sabiri, “Epilepsy detection using wavelet transform, genetic algorithm, and decision tree classifier,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 13, no. 3, Art. no. 3, Sep. 2024, doi: 10.11591/ijai.v13.i3.pp3447-3455.
D. N. Joanes and C. A. Gill, “Comparing measures of sample skewness and kurtosis,” Journal of the Royal Statistical Society: Series D (The Statistician), vol. 47, no. 1, pp. 183–189, 1998, doi: 10.1111/1467-9884.00122.
X. Yan and M. Jia, “A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing,” Neurocomputing, vol. 313, pp. 47–64, Nov. 2018, doi: 10.1016/j.neucom.2018.05.002.
Y. Zhong, H. Wei, L. Chen, and T. Wu, “Automated EEG Pathology Detection Based on Significant Feature Extraction and Selection,” Mathematics, vol. 11, no. 7, p. 1619, Jan. 2023, doi: 10.3390/math11071619.
J. Kennedy and R. C. Eberhart, “A discrete binary version of the particle swarm algorithm,” in Computational Cybernetics and Simulation 1997 IEEE International Conference on Systems, Man, and Cybernetics, Oct. 1997, pp. 4104–4108 vol.5. doi: 10.1109/ICSMC.1997.637339.
Z.-S. Chen, B. Zhu, Y.-L. He, and L.-A. Yu, “A PSO based virtual sample generation method for small sample sets: Applications to regression datasets,” Engineering Applications of Artificial Intelligence, vol. 59, pp. 236–243, Mar. 2017, doi: 10.1016/j.engappai.2016.12.024.
L. Zougagh, H. Bouyghf, M. Nahid, and B. Ouacha, “Feature extraction and classification of epileptic seizures from combined EEG and ECG signals,” J. Phys.: Conf. Ser., vol. 2550, no. 1, p. 012028, Aug. 2023, doi: 10.1088/1742-6596/2550/1/012028.
Y. Zhong, H. Wei, L. Chen, and T. Wu, “Automated EEG Pathology Detection Based on Significant Feature Extraction and Selection,” Mathematics, vol. 11, no. 7, p. 1619, Jan. 2023, doi: 10.3390/math11071619.
E. Juárez-Guerra, V. Alarcon-Aquino, and P. Gómez-Gil, “Epilepsy Seizure Detection in EEG Signals Using Wavelet Transforms and Neural Networks,” in New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering, Springer, Cham, 2015, pp. 261–269. doi: 10.1007/978-3-319-06764-3_33.
J. Xu, Y. Zhang, and D. Miao, “Three-way confusion matrix for classification: A measure driven view,” Information Sciences, vol. 507, pp. 772–794, Jan. 2020, doi: 10.1016/j.ins.2019.06.064.
Copyright (c) 2025 Lahcen Zougagh, Hamid Bouyghf, Mohammed Nahid
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
.png)
.png)
.png)
.png)
.png)
