Optimisasi Model Backpropagation untuk Meningkatkan Deteksi Kejang Epilepsi pada Sinyal Electroencephalogram
Abstract
Epilepsy is a chronic neurological disorder characterized by recurrent seizures caused by abnormal electrical activity in the brain. Fast and accurate seizure detection is crucial to support medical intervention and improve patients' quality of life. Currently, Electroencephalogram (EEG) signals are widely used to diagnose epilepsy as they record brain electrical activity in real-time. However, manual analysis of EEG signals requires time and precision, necessitating a more effective automated solution. This study aims to optimize the Backpropagation model for detecting epileptic seizures using EEG data. The research involved collaboration between Telkom University, Sumber Waras Hospital, and the University of Bonn. The EEG data collected was processed through Discrete Cosine Transform (DCT) to extract important features before being used to train the artificial neural network (ANN) model. The model was trained and tested using varying numbers of epochs to measure its accuracy. The results show that the Backpropagation model achieved optimal accuracy of 91.15% at 100 epochs and increased to 93.05% at 200 epochs. Although accuracy improved with more epochs, the longer computational time posed a risk of overfitting. This research demonstrates that the Backpropagation algorithm can be optimized to detect epileptic seizures accurately and efficiently. The implication for Sumber Waras Hospital is that this model can be implemented in EEG monitoring systems to detect seizures in real-time, supporting faster medical intervention and reducing reliance on manual analysis. Thus, this study contributes to providing a more efficient diagnostic solution and enhancing healthcare services for epilepsy patients.References
[1] T. Nisar and R. Priyadarshini, “A comprehensive study of machine learning-based methods to predict epileptic seizures,” World Journal of Advanced Engineering Technology and Sciences, 2024, doi: 10.30574/wjaets.2024.12.1.0187.
[2] A. I. Jaya, G. Soemarno, and J. W. Puspita, “Classification of Epileptiform Waves Based on Frequency by Using Backpropagation Neural Network,” 2018. doi: 10.1088/1742-6596/1028/1/012048.
[3] A. Damayanti, A. B. Pratiwi, and Miswanto, “Epilepsy detection on EEG data using backpropagation, firefly algorithm and simulated annealing,” 2016. doi: 10.1109/ICSTC.2016.7877368.
[4] H. F. Choi, “Classification of Epileptic Seizures Based on CNN and Guided Back-Propagation for Interpretation Analysis,” 2023. doi: 10.1007/978-3-031-32213-6_16.
[5] K. Han, C. Liu, and D. Friedman, “Artificial intelligence/machine learning for epilepsy and seizure diagnosis,” Epilepsy & Behavior, 2024, doi: 10.1016/j.yebeh.2024.109736.
[6] V. S. Kumar, C. Karpagavalli, J. Chhablani, S. Divya, S. N. Taqui, and N. Vinayagam, “Deep Learning-Based EEG Signal Classification of Epileptic Patients,” 2024. doi: 10.1109/icoeca62351.2024.00114.
[7] V. S. Veena and R. P. Devi, “Analysis of Epileptic Seizure Detection Using Deep Learning Algorithms,” 2024. doi: 10.1109/icdcece60827.2024.10548098.
[8] A. Penumalli, K. A. V. Kumar, A. Pamidimukkala, and B. Begum, “Epileptic Seizures Detection using Fusion of Artificial Neural Network with Hybrid Deep Learning,” 2024. doi: 10.1109/accai61061.2024.10602167.
[9] D. Khurshid, F. Wahid, S. Ali, A. H. Gumaei, S. M. Alzanin, and M. A. A. Mosleh, “A deep neural network-based approach for seizure activity recognition of epilepsy sufferers,” Front Med (Lausanne), 2024, doi: 10.3389/fmed.2024.1405848.
[10] X. Zhang and J. Wang, “Bibliometric Analysis of EEG Signal Processing Techniques for Neurological Applications,” Front Hum Neurosci, vol. 18, p. 129, 2024.
[11] N. K. Al-Qazzaz, M. Alrahhal, S. Hamad, S. Jaafer, M. H. Ali, and S. A. Ahmad, “Automatic Diagnosis of Epileptic Seizures using Entropy-based Features and Multimodel Deep Learning Approaches,” Med Eng Phys, 2024, doi: 10.1016/j.medengphy.2024.104206.
[12] B. Sidaoui, “Predicting states of epilepsy patients using deep learning models,” Applied Computer Science, 2024, doi: 10.35784/acs-2024-19.
[13] C. Saisree and U. Kumaran, “Pothole Detection Using Deep Learning Classification Method,” in Procedia Computer Science, Elsevier B.V., 2022, pp. 2143–2152. doi: 10.1016/j.procs.2023.01.190.
[14] “Deep Learning Approaches for Seizure Detection and Prediction Using EEG Signals: A Comprehensive Review and Proposed CNN Framework,” Journal of Angiotherapy, 2024, doi: 10.25163/angiotherapy.869616.
[15] 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,” Dental science reports, 2024, doi: 10.1038/s41598-024-67855-4.
[16] L. Zhang et al., “Automatic interictal epileptiform discharge (IED) detection based on convolutional neural network (CNN),” Front Mol Biosci, vol. 10, 2023, doi: 10.3389/fmolb.2023.1146606.
[17] D. Depannemaecker, L. Lopez, C. Gauld, and L. Pio-Lopez, “Does Deep Learning Have Epileptic Seizures? On the Modeling of the Brain,” HALScience, 2022, doi: 10.20944/preprints202207.0229.v1.
[18] X. Tian et al., “Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection,” Ieee Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 10, pp. 1962–1972, 2019, doi: 10.1109/tnsre.2019.2940485.
[19] Y. M. Massoud, M. Abdelzaher, L. Kuhlmann, and M. A. Abd El Ghany, “General and patient-specific seizure classification using deep neural networks,” Analog Integr Circuits Signal Process, vol. 116, no. 3, pp. 205–220, Sep. 2023, doi: 10.1007/s10470-023-02153-z.
[20] K. Sakashita et al., “Deep learning for the diagnosis of mesial temporal lobe epilepsy,” PLoS One, vol. 18, no. 2 February, Feb. 2023, doi: 10.1371/journal.pone.0282082.
[21] B. Abbasi and D. M. Goldenholz, “Machine Learning Applications in Epilepsy,” Epilepsia, vol. 60, no. 10, pp. 2037–2047, 2019, doi: 10.1111/epi.16333.
[22] G. Hwang et al., “Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy With Machine Learning,” Brain Connect, vol. 9, no. 2, pp. 184–193, 2019, doi: 10.1089/brain.2018.0601.
[2] A. I. Jaya, G. Soemarno, and J. W. Puspita, “Classification of Epileptiform Waves Based on Frequency by Using Backpropagation Neural Network,” 2018. doi: 10.1088/1742-6596/1028/1/012048.
[3] A. Damayanti, A. B. Pratiwi, and Miswanto, “Epilepsy detection on EEG data using backpropagation, firefly algorithm and simulated annealing,” 2016. doi: 10.1109/ICSTC.2016.7877368.
[4] H. F. Choi, “Classification of Epileptic Seizures Based on CNN and Guided Back-Propagation for Interpretation Analysis,” 2023. doi: 10.1007/978-3-031-32213-6_16.
[5] K. Han, C. Liu, and D. Friedman, “Artificial intelligence/machine learning for epilepsy and seizure diagnosis,” Epilepsy & Behavior, 2024, doi: 10.1016/j.yebeh.2024.109736.
[6] V. S. Kumar, C. Karpagavalli, J. Chhablani, S. Divya, S. N. Taqui, and N. Vinayagam, “Deep Learning-Based EEG Signal Classification of Epileptic Patients,” 2024. doi: 10.1109/icoeca62351.2024.00114.
[7] V. S. Veena and R. P. Devi, “Analysis of Epileptic Seizure Detection Using Deep Learning Algorithms,” 2024. doi: 10.1109/icdcece60827.2024.10548098.
[8] A. Penumalli, K. A. V. Kumar, A. Pamidimukkala, and B. Begum, “Epileptic Seizures Detection using Fusion of Artificial Neural Network with Hybrid Deep Learning,” 2024. doi: 10.1109/accai61061.2024.10602167.
[9] D. Khurshid, F. Wahid, S. Ali, A. H. Gumaei, S. M. Alzanin, and M. A. A. Mosleh, “A deep neural network-based approach for seizure activity recognition of epilepsy sufferers,” Front Med (Lausanne), 2024, doi: 10.3389/fmed.2024.1405848.
[10] X. Zhang and J. Wang, “Bibliometric Analysis of EEG Signal Processing Techniques for Neurological Applications,” Front Hum Neurosci, vol. 18, p. 129, 2024.
[11] N. K. Al-Qazzaz, M. Alrahhal, S. Hamad, S. Jaafer, M. H. Ali, and S. A. Ahmad, “Automatic Diagnosis of Epileptic Seizures using Entropy-based Features and Multimodel Deep Learning Approaches,” Med Eng Phys, 2024, doi: 10.1016/j.medengphy.2024.104206.
[12] B. Sidaoui, “Predicting states of epilepsy patients using deep learning models,” Applied Computer Science, 2024, doi: 10.35784/acs-2024-19.
[13] C. Saisree and U. Kumaran, “Pothole Detection Using Deep Learning Classification Method,” in Procedia Computer Science, Elsevier B.V., 2022, pp. 2143–2152. doi: 10.1016/j.procs.2023.01.190.
[14] “Deep Learning Approaches for Seizure Detection and Prediction Using EEG Signals: A Comprehensive Review and Proposed CNN Framework,” Journal of Angiotherapy, 2024, doi: 10.25163/angiotherapy.869616.
[15] 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,” Dental science reports, 2024, doi: 10.1038/s41598-024-67855-4.
[16] L. Zhang et al., “Automatic interictal epileptiform discharge (IED) detection based on convolutional neural network (CNN),” Front Mol Biosci, vol. 10, 2023, doi: 10.3389/fmolb.2023.1146606.
[17] D. Depannemaecker, L. Lopez, C. Gauld, and L. Pio-Lopez, “Does Deep Learning Have Epileptic Seizures? On the Modeling of the Brain,” HALScience, 2022, doi: 10.20944/preprints202207.0229.v1.
[18] X. Tian et al., “Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection,” Ieee Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 10, pp. 1962–1972, 2019, doi: 10.1109/tnsre.2019.2940485.
[19] Y. M. Massoud, M. Abdelzaher, L. Kuhlmann, and M. A. Abd El Ghany, “General and patient-specific seizure classification using deep neural networks,” Analog Integr Circuits Signal Process, vol. 116, no. 3, pp. 205–220, Sep. 2023, doi: 10.1007/s10470-023-02153-z.
[20] K. Sakashita et al., “Deep learning for the diagnosis of mesial temporal lobe epilepsy,” PLoS One, vol. 18, no. 2 February, Feb. 2023, doi: 10.1371/journal.pone.0282082.
[21] B. Abbasi and D. M. Goldenholz, “Machine Learning Applications in Epilepsy,” Epilepsia, vol. 60, no. 10, pp. 2037–2047, 2019, doi: 10.1111/epi.16333.
[22] G. Hwang et al., “Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy With Machine Learning,” Brain Connect, vol. 9, no. 2, pp. 184–193, 2019, doi: 10.1089/brain.2018.0601.
Published
2024-12-17
How to Cite
NURDIAWAN, Odi; FATHURROHMAN, Fathurrohman; FAQIH, Ahmad.
Optimisasi Model Backpropagation untuk Meningkatkan Deteksi Kejang Epilepsi pada Sinyal Electroencephalogram.
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System, [S.l.], v. 9, n. 2, p. 151-160, dec. 2024.
ISSN 2548-3587.
Available at: <https://87383.shichuantrade-tw.tech/index.php/ISBI/article/view/3187>. Date accessed: 13 mar. 2025.
doi: https://doi.org/10.51211/isbi.v9i2.3187.
Section
Articles
