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DOĞRUSAL ÖNGÖRÜMLEME KODLAMA YÖNTEMİ İLE EEG SİNYALLERİNDEN OTOMATİK EPİLEPTİK NÖBET TESPİTİ

Yıl 2025, Cilt: 28 Sayı: 3, 1345 - 1361, 03.09.2025

Nuri İkizler

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

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

Epileptik Nöbet Tespiti EEG Doğrusal Öngörümleme Kodlama Makine Öğrenmesi

Kaynakça

  • 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

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

Yıl 2025, Cilt: 28 Sayı: 3, 1345 - 1361, 03.09.2025

Nuri İkizler

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

Öz

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.

Anahtar Kelimeler

Epileptic Seizure Detection EEG Linear Predictive Coding Machine Learning

Kaynakça

  • 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
Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İnsan Merkezli Bilgi İşleme (Diğer), Makine Öğrenme (Diğer)
Bölüm Elektrik Elektronik Mühendisliği
Yazarlar

Nuri İkizler 0000-0002-7632-1973

Yayımlanma Tarihi 3 Eylül 2025
Gönderilme Tarihi 2 Nisan 2025
Kabul Tarihi 16 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 28 Sayı: 3

Kaynak Göster

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
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