Epilepsi Tespitinde Gürbüz Yerel Ortalama Ayrışım ve Ampirik Kip Ayrışım Yöntemlerinin Performans Analizi
Abstract
Beynin elektriksel aktivitesi ile ilgili bilgi sağlayan elektroansefalografi (EEG) verileri nörolojik hastalıkların tanısında yaygın olarak kullanılmaktadır. Yaklaşık olarak dünya nüfusunun %1'ini etkileyen hastalıklardan biri olan epilepsi tespitinde de EEG sinyalleri önemli bilgiler sağlamaktadır. Bu çalışmada EEG sinyalleri kullanılarak epilepsi nöbetinin nöbet öncesi tespiti amaçlanmıştır. Bu amaç doğrultusunda epilepsi ve sağlıklı bireylerden alınan farklı durumlardaki EEG sinyalleri kullanılarak ön işleme adımları gerçekleştirildikten sonra EEG sinyallerinden, Gürbüz Yerel Ortalama Ayrışım (Robust Local Mean Decomposition, RLMD) ve Ampirik Kip Ayrışım (AKA) yöntemi kullanılarak elde edilen alt bant sinyallerinden öznitelikler çıkarılmıştır. Elde edilen öznitelikler ve Yapay Sinir ağları (YSA) ile sınıflandırma çalışmaları yapılmıştır. Yapılan çalışmalar doğrultusunda EEG sinyallerinin farklı durumlarına ait sınıflandırma sonuçları doğruluk, duyarlılık, özgüllük, kesinlik ve f1 skoru performans parametreleri kullanılarak ortaya konmuştur.
Keywords
Epilepsi EEG Gürbüz Yerel Ortalama Ayrışım Yöntemi Ampirik Kip Ayrışım Yöntemi Yapay Sinir Ağları
Supporting Institution
TÜBİTAK
Project Number
1919B012111299
Thanks
Bu çalışma Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından 2209-A Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı kapsamında 1919B012111299 numaralı proje ile desteklenmiştir. Bu sebeple TÜBİTAK 'a teşekkürlerimizi sunarız.
References
- Ghassemi, N., Shoeibi, A., Rouhani, M., & Hosseini-Nejad, H. (2019, October). Epileptic seizures detection in EEG signals using TQWT and ensemble learning. In 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) (pp. 403-408). IEEE.
- Pachori, R. B., & Patidar, S. (2014). Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Computer methods and programs in biomedicine, 113(2), 494-502.
- Mader Jr, E. C., & Olejniczak, P. W. (2010). Epilepsy syndromes. Epilepsy and intensive care monitoring: principles and practice. New York, 119-150.
- Vidyaratne, L. S., & Iftekharuddin, K. M. (2017). Real-time epileptic seizure detection using EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(11), 2146-2156.
- Li, M., Chen, W., & Zhang, T. (2016). Automatic epilepsy detection using wavelet-based nonlinear analysis and optimized SVM. Biocybernetics and biomedical engineering, 36(4), 708-718.
- Hamad, A., Houssein, E. H., Hassanien, A. E., & Fahmy, A. A. (2016, December). Feature extraction of epilepsy EEG using discrete wavelet transform. In 2016 12th international computer engineering conference (ICENCO) (pp. 190-195). IEEE.
- Yan, A., Zhou, W., Yuan, Q., Yuan, S., Wu, Q., Zhao, X., & Wang, J. (2015). Automatic seizure detection using Stockwell transform and boosting algorithm for long-term EEG. Epilepsy & Behavior, 45, 8-14.
- Samiee, K., Kovacs, P., & Gabbouj, M. (2014). Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform. IEEE transactions on Biomedical Engineering, 62(2), 541-552.
- Atoufi, B., Zakerolhosseini, A., & Lucas, C. (2009, October). Improving EEG signal prediction via SSA and channel selection. In 2009 14th International CSI Computer Conference (pp. 349-354). IEEE.
- Hassan, A. R., & Haque, M. A. (2015, November). Epilepsy and seizure detection using statistical features in the complete ensemble empirical mode decomposition domain. In TENCON 2015-2015 IEEE region 10 conference (pp. 1-6). IEEE.
- Valenza, G., Romigi, A., Citi, L., Placidi, F., Izzi, F., Albanese, M., ... & Barbieri, R. (2016, August). Predicting seizures in untreated temporal lobe epilepsy using point-process nonlinear models of heartbeat dynamics. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 985-988). IEEE.
- Sharma, M., Pachori, R. B., & Acharya, U. R. (2017). A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognition Letters, 94, 172-179.
- Srinivasan, V., Eswaran, C., & Sriraam, N. (2007). Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Transactions on information Technology in Biomedicine, 11(3), 288-295.
- Adeli, H., Ghosh-Dastidar, S., & Dadmehr, N. (2007). A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Transactions on Biomedical Engineering, 54(2), 205-211.
- Mutlu, A. Y. (2018). Detection of epileptic dysfunctions in EEG signals using Hilbert vibration decomposition. Biomedical Signal Processing and Control, 40, 33-40.
- Polychronaki, G. E., Ktonas, P. Y., Gatzonis, S., Siatouni, A., Asvestas, P. A., Tsekou, H., ... & Nikita, K. S. (2010). Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection. Journal of neural engineering, 7(4), 046007.
- Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6), 061907. Available from: https://repositori.upf.edu/handle/10230/42894
- Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., ... & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454(1971), 903-995.
- Slimane, Z. E. H., & Naït-Ali, A. (2010). QRS complex detection using empirical mode decomposition. Digital signal processing, 20(4), 1221-1228.Chen, C. F., Lai, M. C., & Yeh, C. C. (2012). Forecasting tourism demand based on empirical mode decomposition and neural network. Knowledge-Based Systems, 26, 281-287.
- Pachori, R. B., & Bajaj, V. (2011). Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Computer methods and programs in biomedicine, 104(3), 373-381.
- Zhang, D. X., Wu, X. P., & Guo, X. J. (2008, May). The EEG signal preprocessing based on empirical mode decomposition. In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering (pp. 2131-2134). IEEE.
- Smith, J. S. (2005). The local mean decomposition and its application to EEG perception data. Journal of the Royal Society Interface, 2(5), 443-454.
- Xie, L., Lang, X., Chen, J., Horch, A., & Su, H. (2016). Time-varying oscillation detector based on improved LMD and robust Lempel–Ziv complexity. Control Engineering Practice, 51, 48-57.
- Yongbo, L. I., Shubin, S. I., Zhiliang, L. I. U., & Xihui, L. (2019). Review of local mean decomposition and its application in fault diagnosis of rotating machinery. Journal of Systems Engineering and Electronics, 30(4), 799-814.
- Kutlu, F. (2014). Melez sınıflandırma yaklaşımı ile EEG işaretlerinden epileptik dönemlerin algılanması (Doctoral dissertation, Karadeniz Teknik Üniversitesi).
- Ekhlasi, A., Nasrabadi, A. M., & Mohammadi, M. R. (2021). Direction of information flow between brain regions in ADHD and healthy children based on EEG by using directed phase transfer entropy. Cognitive Neurodynamics, 15(6), 975-986.
- Erkaymaz, H., Ozer, M., & Orak, İ. M. (2015). Detection of directional eye movements based on the electrooculogram signals through an artificial neural network. Chaos, Solitons & Fractals, 77, 225-229.
- Öztemel, E., (2012). Yapay Sinir Ağları. İstanbul: Papatya Yayıncılık Eğitim
Performance Analysis of Robust Local Mean Decomposition and Empirical Mode Decomposition Methods in the Detection of Epilepsy
Abstract
Electroencephalography (EEG) data, which provides information about the electrical activity of the brain, are widely used in the diagnosis of neurological diseases, EEG signals also provide important information in the detection of epilepsy, which is one of the diseases affecting approximately 1% of the world’s population. In this study, it was aimed to detect the epileptic seizure before the seizure by using EEG signals. For this purpose, after preprocessing steps were performed by using EEG signals in different situations from epilepsy and healthy individuals, features were extracted from EEG signals from subband signals obtained by using Robust Local Mean Decomposition (RLMD) and Empirical Mode Decomposition (AKA) methods. Classification studies were carried out with the obtained features and Artificial Neural Networks (ANN). In line with the studies, the classification results of the different states of the EEG signals were revealed using the performance parameters of accuracy, sensitivity, specificity, precision and f1 score.
Keywords
Epilepsy EEG Robust Local Mean Decomposition Empirical Mode Decomposition Method Artificial Neural Networks
Project Number
1919B012111299
References
- Ghassemi, N., Shoeibi, A., Rouhani, M., & Hosseini-Nejad, H. (2019, October). Epileptic seizures detection in EEG signals using TQWT and ensemble learning. In 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) (pp. 403-408). IEEE.
- Pachori, R. B., & Patidar, S. (2014). Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Computer methods and programs in biomedicine, 113(2), 494-502.
- Mader Jr, E. C., & Olejniczak, P. W. (2010). Epilepsy syndromes. Epilepsy and intensive care monitoring: principles and practice. New York, 119-150.
- Vidyaratne, L. S., & Iftekharuddin, K. M. (2017). Real-time epileptic seizure detection using EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(11), 2146-2156.
- Li, M., Chen, W., & Zhang, T. (2016). Automatic epilepsy detection using wavelet-based nonlinear analysis and optimized SVM. Biocybernetics and biomedical engineering, 36(4), 708-718.
- Hamad, A., Houssein, E. H., Hassanien, A. E., & Fahmy, A. A. (2016, December). Feature extraction of epilepsy EEG using discrete wavelet transform. In 2016 12th international computer engineering conference (ICENCO) (pp. 190-195). IEEE.
- Yan, A., Zhou, W., Yuan, Q., Yuan, S., Wu, Q., Zhao, X., & Wang, J. (2015). Automatic seizure detection using Stockwell transform and boosting algorithm for long-term EEG. Epilepsy & Behavior, 45, 8-14.
- Samiee, K., Kovacs, P., & Gabbouj, M. (2014). Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform. IEEE transactions on Biomedical Engineering, 62(2), 541-552.
- Atoufi, B., Zakerolhosseini, A., & Lucas, C. (2009, October). Improving EEG signal prediction via SSA and channel selection. In 2009 14th International CSI Computer Conference (pp. 349-354). IEEE.
- Hassan, A. R., & Haque, M. A. (2015, November). Epilepsy and seizure detection using statistical features in the complete ensemble empirical mode decomposition domain. In TENCON 2015-2015 IEEE region 10 conference (pp. 1-6). IEEE.
- Valenza, G., Romigi, A., Citi, L., Placidi, F., Izzi, F., Albanese, M., ... & Barbieri, R. (2016, August). Predicting seizures in untreated temporal lobe epilepsy using point-process nonlinear models of heartbeat dynamics. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 985-988). IEEE.
- Sharma, M., Pachori, R. B., & Acharya, U. R. (2017). A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognition Letters, 94, 172-179.
- Srinivasan, V., Eswaran, C., & Sriraam, N. (2007). Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Transactions on information Technology in Biomedicine, 11(3), 288-295.
- Adeli, H., Ghosh-Dastidar, S., & Dadmehr, N. (2007). A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Transactions on Biomedical Engineering, 54(2), 205-211.
- Mutlu, A. Y. (2018). Detection of epileptic dysfunctions in EEG signals using Hilbert vibration decomposition. Biomedical Signal Processing and Control, 40, 33-40.
- Polychronaki, G. E., Ktonas, P. Y., Gatzonis, S., Siatouni, A., Asvestas, P. A., Tsekou, H., ... & Nikita, K. S. (2010). Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection. Journal of neural engineering, 7(4), 046007.
- Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6), 061907. Available from: https://repositori.upf.edu/handle/10230/42894
- Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., ... & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454(1971), 903-995.
- Slimane, Z. E. H., & Naït-Ali, A. (2010). QRS complex detection using empirical mode decomposition. Digital signal processing, 20(4), 1221-1228.Chen, C. F., Lai, M. C., & Yeh, C. C. (2012). Forecasting tourism demand based on empirical mode decomposition and neural network. Knowledge-Based Systems, 26, 281-287.
- Pachori, R. B., & Bajaj, V. (2011). Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Computer methods and programs in biomedicine, 104(3), 373-381.
- Zhang, D. X., Wu, X. P., & Guo, X. J. (2008, May). The EEG signal preprocessing based on empirical mode decomposition. In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering (pp. 2131-2134). IEEE.
- Smith, J. S. (2005). The local mean decomposition and its application to EEG perception data. Journal of the Royal Society Interface, 2(5), 443-454.
- Xie, L., Lang, X., Chen, J., Horch, A., & Su, H. (2016). Time-varying oscillation detector based on improved LMD and robust Lempel–Ziv complexity. Control Engineering Practice, 51, 48-57.
- Yongbo, L. I., Shubin, S. I., Zhiliang, L. I. U., & Xihui, L. (2019). Review of local mean decomposition and its application in fault diagnosis of rotating machinery. Journal of Systems Engineering and Electronics, 30(4), 799-814.
- Kutlu, F. (2014). Melez sınıflandırma yaklaşımı ile EEG işaretlerinden epileptik dönemlerin algılanması (Doctoral dissertation, Karadeniz Teknik Üniversitesi).
- Ekhlasi, A., Nasrabadi, A. M., & Mohammadi, M. R. (2021). Direction of information flow between brain regions in ADHD and healthy children based on EEG by using directed phase transfer entropy. Cognitive Neurodynamics, 15(6), 975-986.
- Erkaymaz, H., Ozer, M., & Orak, İ. M. (2015). Detection of directional eye movements based on the electrooculogram signals through an artificial neural network. Chaos, Solitons & Fractals, 77, 225-229.
- Öztemel, E., (2012). Yapay Sinir Ağları. İstanbul: Papatya Yayıncılık Eğitim
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Project Number | 1919B012111299 |
| Early Pub Date | July 26, 2022 |
| Publication Date | July 31, 2022 |
| Published in Issue | Year 2022 Issue: 39 |
