DOĞRUSAL ÖNGÖRÜMLEME KODLAMA YÖNTEMİ İLE EEG SİNYALLERİNDEN OTOMATİK EPİLEPTİK NÖBET TESPİTİ

Nuri İkizler

doi:10.17780/ksujes.1669078

TR EN

DOĞRUSAL ÖNGÖRÜMLEME KODLAMA YÖNTEMİ İLE EEG SİNYALLERİNDEN OTOMATİK EPİLEPTİK NÖBET TESPİTİ

Öz

EEG kayıtlarından otomatik epileptik nöbet tespiti, epilepsi tanı ve tedavisinde kritik bir rol oynamaktadır. Geleneksel olarak gerçekleştirilen nöbet tespitinde, uzmanların uzun süreli EEG kayıtlarını incelemesini gerekir, bu zaman alıcı ve insan hatasına açık bir süreçtir. Bu nedenle, EEG verilerinden nöbetleri otomatik olarak algılayan sistemler, daha hızlı, daha doğru ve kesintisiz izleme imkânı sunar. Ayrıca, uzun süreli EEG kayıtlarının depolanması da ayrı bir zorluk teşkil etmektedir. Bu çalışmada, her iki soruna da çözüm sunan tek bir yaklaşım ile EEG verileri doğrusal öngörümleme kodlama yöntemiyle kodlanarak ve doğrusal öngörümleme katsayıları ile bunların istatistiksel özelliklerini öznitelik vektörleri olarak kullanılarak, çok katmanlı algılayıcı, k-en yakın komşu, rastgele orman ve lojistik model ağacı sınıflandırıcılarının sınıflandırma doğrulukları incelenmiştir. Elde edilen sonuçlar ile orijinal EEG sinyali yerine yalnızca doğrusal öngörümleme katsayılarını kullanarak, çeşitli sınıflandırma görevlerinde çok yüksek doğrulukla otomatik nöbet tespiti yapılabileceği gösterilmiştir.

Anahtar Kelimeler

AUTOMATIC EPILEPTIC SEIZURE DETECTION FROM EEG SIGNALS USING THE LINEAR PREDICTIVE CODING METHOD

Abstract

Automatic epileptic seizure detection from EEG recordings plays a critical role in the diagnosis and treatment of epilepsy. Traditionally, seizure detection requires experts to review long-term EEG recordings, a time-consuming process prone to human error. Therefore, systems that automatically detect seizures from EEG data provide faster, more accurate, and continuous monitoring. Additionally, the storage of long-term EEG recordings presents a separate challenge. In this study, a single approach addressing both issues is proposed, where EEG data is encoded using the Linear Predictive Coding method, and the Linear Predictive Coding coefficients and their statistical features are used as feature vectors. The classification accuracy of Multi-Layer Perceptron, k-Nearest Neighbors, Random Forest, and Logistic Model Tree classifiers are examined. The results show that by using only Linear Predictive Coding coefficients instead of the original EEG signal, automatic seizure detection can be performed with very high accuracy in various classification tasks.

Keywords

References

Abu Alfeilat, H. A., Hassanat, A. B., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., Salman, H. S. E., & Prasath, V. S. (2019). Effects of distance measure choice on k-nearest neighbor classifier performance: A review. Big Data, 7(4), 221-248.
Akut, R. (2019). Wavelet based deep learning approach for epilepsy detection. Health Information Science and Systems, 7(1), 1–9. https://doi.org/10.1007/s13755-019-0069-1
Al-Hadeethi, H., Abdulla, S., Diykh, M., Deo, R. C., & Green, J. H. (2020). Adaptive boost LS-DVM classification approach for time-series signal classification in epileptic seizure diagnosis applications. Expert Systems with Applications, 161, 113676. https://doi.org/10.1016/j.eswa.2020.113676
Alotaiby, T. N., Alshebeili, S. A., & Abd El-Samie, F. E. (2016). Channel selection and seizure detection using a statistical approach. IEEE Xplore.
Altunay, S., Telatar, Z., & Erogul, O. (2010). Epileptic EEG detection using the linear prediction error energy. Expert Systems with Applications, 37(8), 5661-5665. https://doi.org/10.1016/j.eswa.2010.02.008
Âmin, H. U., Yusoff, M. Z., & Ahmad, R. F. (2020). A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques. Biomedical Signal Processing and Control, 56, 101707. https://doi.org/10.1016/j.bspc.2019.101707
Amorim, P., Moraes, T., Fazanaro, D., Silva, J., & Pedrini, H. (2017). Electroencephalogram signal classification based on shearlet and contourlet transforms. Expert Systems with Applications, 67, 140–147. https://doi.org/10.1016/j.eswa.2016.09.037
Anand, S., Jaiswal, S., & Ghosh, P. (2017). Automatic focal epileptic seizure detection in EEG signals. In Proceedings of the 2017 IEEE International WIE Conference on Electrical and Computer Engineering (pp. 103-107). IEEE. https://doi.org/10.1109/WIECON-ECE.2017.8468906

Andrzejak, R., G., Lehnertz, K., Mormann F., Rieke, C., David, P., & Elger, C. (2001). Indications of nonlinear deterministic and finite dimensional structures in the time series of brain electrical activity: Dependence on recording region and brain state,’ Phys. Rev. E, Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top., vol. 64, Nov. 2001, Art. no. 061907, https://doi.org/10.1103/PhysRevE.64.061907.
Anjum, M. F. (2020). Linear predictive coding distinguishes spectral EEG features of Parkinson's disease. Parkinsonism & Related Disorders, 79, 79–85. https://doi.org/10.1016/j.parkreldis.2020.08.001
Ashokkumar, S. R., Anupallavi, S., Premkumar, M., & Jeevanantham, V. (2021). Implementation of deep neural networks for classifying electroencephalogram signal using fractional S-transform for epileptic seizure detection. International Journal of Imaging Systems and Technology, 31(2), 895–908. https://doi.org/10.1002/ima.22565
Bajaj, V., & Pachori, R. B. (2011). Classification of seizure and non-seizure EEG signals using empirical mode decomposition. IEEE Transactions on Information Technology in Biomedicine, 16(6), 1135-1142. https://doi.org/10.1109/TITB.2011.2160336
Birjandtalab, J., Pouyan, M. B., Cogan, D., Nourani, M., & Harvey, J. (2017). Automated seizure detection using limited-channel EEG and non-linear dimension reduction. Computers in Biology and Medicine, 82, 49-58. https://doi.org/10.1016/j.compbiomed.2017.01.011
Chakraborty, M., & Mitra, D. (2021). Epilepsy seizure detection using kurtosis-based VMD’s parameters selection and bandwidth features. Biomedical Signal Processing and Control, 64, 102255. https://doi.org/10.1016/j.bspc.2020.102255
Chen, G. (2014). Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features. Expert Systems with Applications, 41(5), 2391-2394. https://doi.org/10.1016/j.eswa.2013.09.025
Choubey, H., & Pandey, A. (2019). A new feature extraction and classification mechanisms for EEG signal processing. Multidimensional Systems and Signal Processing, 30(4), 1793–1809. https://doi.org/10.1007/s11045-018-0628-7
Eltrass, A. S., Tayel, M. B., & El-Qady, A. F. (2021). Automatic epileptic seizure detection approach based on multi-stage quantized kernel least mean square filters. Biomedical Signal Processing and Control, 70, 103031. https://doi.org/10.1016/j.bspc.2021.103031
Harpale, V., & Bairagi, V. (2021). An adaptive method for feature selection and extraction for classification of epileptic EEG signal in significant states. Journal of King Saud University - Computer and Information Sciences, 33(6), 668-676. https://doi.org/10.1016/j.jksuci.2018.04.014
Hu, J., & Szymczak, S. (2023). A review on longitudinal data analysis with random forest. Briefings in Bioinformatics, 24(2), bbad002. https://doi.org/10.1093/bib/bbad002
Ikizler, N., & Ekim, G. (2025). Investigating the effects of Gaussian noise on epileptic seizure detection: The role of spectral flatness, bandwidth, and entropy. Engineering Science and Technology, an International Journal, 64, 102005. https://doi.org/10.1016/j.jestch.2025.102005
İkizler, N., & Ekim, G. (2025). Epileptik Nöbet Tespiti İçin Yüksek Çözünürlüklü Güç Spektral Yoğunluğu Yaklaşımları. Politeknik Dergisi 1-1. https://doi.org/10.2339/politeknik.1605362
Joshi, V., Pachori, R. B., & Vijesh, A. (2014). Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomedical Signal Processing and Control, 9, 1-5. https://doi.org/10.1016/j.bspc.2013.09.003
Kruse, R., Mostaghim, S., Borgelt, C., Braune, C., & Steinbrecher, M. (2022). Multi-layer perceptron’s. In Computational intelligence: A methodological introduction (pp. 53–124). Springer International Publishing. https://doi.org/10.1007/978-3-030-42227-1_5
Lee, M., Ryu, J., & Kim, D. (2020). Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals. ETRI Journal, 42(2), 217–229. https://doi.org/10.4218/etrij.2018-0118
Li, M., Chen, W., & Zhang, T. (2017). Automatic epileptic EEG detection using DT-CWT-based non-linear features. Biomedical Signal Processing and Control, 34, 114-125. https://doi.org/10.1016/j.bspc.2017.01.010
Liu, Q., Zhao, X., Hou, Z., & Liu, H. (2017). Epileptic seizure detection based on the kernel extreme learning machine. Technology and Health Care, 25, 399–409. https://doi.org/10.3233/THC-171343
Mardini, W., Yassein, M. M. B., Al-Rawashdeh, R., Aljawarneh, S., Khamayseh, Y., & Meqdadi, O. (2020). Enhanced detection of epileptic seizure using EEG signals in combination with machine learning classifiers. IEEE Access, 8, 24046–24055. https://doi.org/10.1109/ACCESS.2020.2970012
Milligan, T. A. (2021). Epilepsy: A clinical overview. The American Journal of Medicine, 134(7), 840-847. https://doi.org/10.1016/j.amjmed.2021.03.021
Miltiadous, A. (2023). Machine learning algorithms for epilepsy detection based on published EEG databases: A systematic review. IEEE Access, 11, 564-594. https://doi.org/10.1109/ACCESS.2022.3232563
Mouleeshuwarapprabu, R., & Kasthuri, N. (2023). Feature extraction and classification of EEG signal using multilayer perceptron. Journal of Electrical Engineering & Technology, 18(4), 3171–3178. https://doi.org/10.1007/s42835-023-01508-w.
Naidu, G., Zuva, T., & Sibanda, E. M. (2023). A review of evaluation metrics in machine learning algorithms. In Computer Science On-Line Conference (pp. 15–25). Springer International Publishing. https://doi.org/10.1007/978-3-031-35314-7_2.
National Institute of Neurological Disorders and Stroke. (2024). Retrieved August 23, 2024, from http://www.ninds.nih.gov/
Parmar, A., Katariya, R., & Patel, V. (2019). A review on random forest: An ensemble classifier. In International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018 (pp. 758-763). Springer International Publishing.
Pazhanirajan, S., & Dhanalakshmi, P. (2013). EEG signal classification using linear predictive cepstral coefficient features. International Journal of Computer Applications, 73(1), 28-31.
Polat, K., & Nour, M. (2020). Epileptic seizure detection based on new hybrid models with electroencephalogram signals. IRBM, 41(6), 331–353. https://doi.org/10.1016/j.irbm.2020.06.008
Samiee, K., Kovacs, P., & Gabbouj, M. (2015). Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform. IEEE Transactions on Biomedical Engineering, 62(2), 541-552. https://doi.org/10.1109/TBME.2014.2361356
San-Segundo, R., Gil-Martín, M., D’Haro-Enríquez, L. F., & Pardo, J. M. (2019). Classification of epileptic EEG recordings using signal transforms and convolutional neural networks. Computers in Biology and Medicine, 109, 148-158. https://doi.org/10.1016/j.compbiomed.2019.04.010
Sharma, M., & Pachori, R. B. (2017). A novel approach to detect epileptic seizures using a combination of tunable-Q wavelet transform and fractal dimension. Journal of Mechanics in Medicine and Biology, 17(7), 1740003. https://doi.org/10.1142/S0219519417400036
Sharmila, A., Raj, S. A., Shashank, P., & Mahalakshmi, P. (2018). Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine: A case study. Journal of Medical Engineering & Technology, 42(1), 1-8. https://doi.org/10.1080/03091902.2017.1394389
Sharmila, A., & Mahalakshmi, P. (2017). Wavelet-based feature extraction for classification of epileptic seizure EEG signal. Journal of Medical Engineering & Technology, 41(8), 670-680. https://doi.org/10.1080/03091902.2017.1394388
Spratling, M. W. (2017). A review of predictive coding algorithms. Brain and Cognition, 112, 92–97. https://doi.org/10.1016/j.bandc.2015.11.003
Tajadini, B., Seydnejad, S. R., & Rezakhani, S. (2024). Short-term epileptic seizures prediction based on cepstrum analysis and signal morphology. BMC Biomedical Engineering, 6(1), 6. https://doi.org/10.1186/s42490-024-00006-8
Trigka, M., Dritsas, E., & Mylonas, P. (2024, September). Driver sleepiness detection using machine learning models on EEG data. In Proceedings of the 13th Hellenic Conference on Artificial Intelligence (pp. 1–4). https://doi.org/10.1145/3688671.3688780
Wang, Z., Na, J., & Zheng, B. (2020). An improved k-NN classifier for epilepsy diagnosis. IEEE Access, 8, 100022–100030. https://doi.org/10.1109/ACCESS.2020.2996946
Wijayanto, I., Rizal, S., & Hadiyoso, S. (2023). Epileptic electroencephalogram signal classification using wavelet energy and random forest. AIP Conference Proceedings, 2654(1). AIP Publishing. https://doi.org/10.1063/5.0116298
World Health Organization. (2024). Retrieved August 23, 2024, from https://www.who.int/
Yavuz, E., Kasapbaşı, M. C., Eyüpoğlu, C., & Yazıcı, R. (2018). An epileptic seizure detection system based on cepstral analysis and generalized regression neural network. Biocybernetics and Biomedical Engineering, 38(2), 201-216. https://doi.org/10.1016/j.bbe.2018.01.002

Details

Primary Language

Turkish

Subjects

Human Centered Computing (Other) , Machine Learning (Other)

Journal Section

Research Article

Authors

Nuri İkizler ^*
0000-0002-7632-1973
Türkiye

Publication Date

September 3, 2025

Submission Date

April 2, 2025

Acceptance Date

June 16, 2025

Published in Issue

Year 2025 Volume: 28 Number: 3

DOI

https://doi.org/10.17780/ksujes.1669078

IZ

https://izlik.org/JA27UR93EH

Cite

RIS / Bibtex

APA

İkizler, N. (2025). DOĞRUSAL ÖNGÖRÜMLEME KODLAMA YÖNTEMİ İLE EEG SİNYALLERİNDEN OTOMATİK EPİLEPTİK NÖBET TESPİTİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(3), 1345-1361. https://doi.org/10.17780/ksujes.1669078