EEG İşaretlerinin Hilbert Huang Dönüşümü ve Sınıflandırılması

Year 2022, Volume: 22 Issue: 6, 1323 - 1333, 28.12.2022

Gazi Akgün , Ömer Akgün

https://doi.org/10.35414/akufemubid.1145857

Abstract

Bu çalışmada Elektroensefalogram (EEG) sinyallerinin analizi ve bu analiz üzerinden sınıflandırılması amaçlanmıştır. Bu amaçla EEG işaretleri Hilbert Huang metodu ile alt frekans bantlarındaki bileşenlerine ayrılmış, anlık frekans ve marjinal izge vektörleri elde edilmiştir. Bu vektörler ve bileşenler kullanılarak istatistiksel öznitelikleri çıkarılmıştır. Bu öznitelikler göz açık – göz kapalı , sağlıklı-epileptik ve epileptik nöbet alt sınıflarında incelenmiş, destek vektör makinesi (DVM), yapay sinir ağları (YSA) ve doğrusal ayrım analizi (DAA) algoritmaları ile sınıflandırılmış ve sonuçlar karşılaştırmalı olarak tartışılmıştır.

Keywords

EEG, Epilepsi, Hilbert Huang, Ampirik Mod Ayrışımı, Yapay Sinir Ağları, Destek Vektör Makineleri, Sınıflandırma

Hilbert Huang Transformation and Classification of EEG Signals

Abstract

The goal of this study is to classify the Electroencephalogram (EEG) signals through signal analysis. To achieve this, Hilbert Huang's method is used to decompose EEG signals into components in lower frequency bands, yielding instantaneous frequency and marginal spectral vectors. These vectors and components are then used to extract statistical features. These features are classified in the eye-open, eye-closed, healthy-epileptic, and epileptic seizure subclasses with the support vector machine (SVM), artificial neural networks (ANN), and linear discrimination analysis (LDA) algorithms, and the results are discussed in comparison.

Keywords

EEG, Epilepsy, Hilbert Huang, Emprical Mode Decomposition, Artificial Neural Networks, Support Vector Machines, Classification

References

• Acharya, U. R., Sree, S. V., Chattopadhyay, S., Yu, W., & Ang, P. C. A., 2011. Application of recurrence quantification analysis for the automated identification of epileptic EEG signals. International journal of neural systems,, 21(03), 199-211.
• 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.
• Altan, G., YAYIK, A., Kutlu, Y., YILDIRIM, S., & YILDIRIM, E., 2014. Konjestif Kalp Yetmezliğinin Hilbert-Huang Dönüşüm ile Analizi. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 16(48), 94-103.
• 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.
• Beghi, E., & Giussani, G., 2018. Aging and the epidemiology of epilepsy. Neuroepidemiology, 51(3-4), 216-223. Choe, S. H., Chung, Y. G., & Kim, S. P., 2010. Statistical spectral feature extraction for classification of epileptic EEG signals. In 2010 International Conference on Machine Learning and Cybernetics (Vol. 6, pp. 3180-3185). IEEE. Ding, H., Huang, Z., Song, Z., & Yan, Y., 2007. Hilbert–Huang transform based signal analysis for the characterization of gas–liquid two-phase flow. . Flow measurement and instrumentation, , 18(1), 37-46.
• Garg, M., Sinha, B., & Chandra, S., 2015. Identification of relations from IndoWordNet for indian languages using support vector machine. In 2015 International Conference on Computing and Network Communications (CoCoNet) IEEE., (s. pp. 547-552).
• Güler, I., & Übeyli, E. D., 2005. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. . Journal of neuroscience methods, , 148(2), 113-121.
• Huang, M., Wu, P., Liu, Y., Bi, L., & Chen, H., 2008. Application and contrast in brain-computer interface between Hilbert-Huang transform and wavelet transform. . The 9th International Conference for Young Computer Scientists, (s. pp. 1706-1).
• Hung, Y. W., Chiu, Y. H., Jou, Y. C., Chen, W. H., & Cheng, K. S, 2015. Bed posture classification based on artificial neural network using fuzzy c-means and latent semantic analysis. . Journal of the Chinese Institute of Engineers, , 38(4), 415-425.
• Jahankhani, P., Kodogiannis, V., & Revett, K., 2006. EEG signal classification using wavelet feature extraction and neural networks. . IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA'06), (s. pp. 120-124).
• Lenzi, G. G., Evangelista, R. F., Duarte, E. R., Colpini, L. M. S., Fornari, A. C., Menechini Neto, R., ... & Santos, O. A. A., 2016. Photocatalytic degradation of textile reactive dye using artificial neural network modeling approach. Desalination and Water Treatment, 57(30), 14132-14144.
• Li, C. H., Ho, H. H., Kuo, B. C., Taur, J. S., Chu, H. S., & Wang, M. S., 2015. A semi-supervised feature extraction based on supervised and fuzzy-based linear discriminant analysis for hyperspectral image classification. . Appl. Math, 9(1L), 81-87.
• Li, P., Karmakar, C., Yan, C., Palaniswami, M., & Liu, C., 2016. Classification of 5-S epileptic EEG recordings using distribution entropy and sample entropy. Frontiers in physiology, s. 7,136. Nigam, V. P., & Graupe, D., 2004. A neural-network-based detection of epilepsy. . Neurological research, 26(1), 55-60.
• Orhan, U., Hekim, M., & Ozer, M., 2011. EEG signals classification using the K-means clustering and a multilayer perceptron neuralnetwork model. Expert Syst. Appl., 38, 13475–13481.
• Özdemir, N., & Yıldırım, E., 2012. Epileptic seizureprediction based on Hilbert Huang Transform and Artificial Neural Networks. 20th Signal Processing and Communications Applications Conference (SIU) IEEE., (s. pp. 1-4).
• Polat, K., & Güneş, S., 2007. Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. . Applied Mathematics and Computation,, 187(2), 1017-1026. Qi, Z., Tian, Y., & Shi, Y., 2013. Structural twin support vector machine for classification. Knowledge-Based Systems, 43, 74-81.
• Riaz, F. H., Riaz, F., Hassan, A., Rehman, S., Niazi, I. K., & Dremstrup, K. , 2015. EMD-based temporal and spectral features for the classification of EEG signals using supervised learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, s. 24(1), 28.
• Sharma, R., & Pachori, R. B., 2015. Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Systems with Applications, , 42(3), 1106-1117.
• Silva, L., Vaz, J. R., Castro, M. A., Serranho, P., Cabri, J., & Pezarat-Correia, P., 2015. Recurrence quantification analysis and support vector machines for golf handicap and low back pain EMG classification. . Journal of Electromyography and Kinesiology. 25(4), 637-647.
• 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.
• Subasi, A., & Gursoy, M. I., 2010. EEG signal classification using PCA, ICA, LDA and support vector machines. . Expert systems with applications, , 37(12), 8659-8666.
• Tharwat, A., Gaber, T., Ibrahim, A., & Hassanien, A. E., 2017. Linear discriminant analysis: A detailed tutorial. . AI communications, 30(2), 169-190.
• Yan, J., & Lu, L., 2014. Improved Hilbert–Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis. . Signal Processing, 98, 74-87.
• Yan, R., & Gao, R. X., 2006. Hilbert–Huang transform-based vibration signal analysis for machine health monitoring. IEEE Transactions on Instrumentation and measurement, , 55(6), 2320-2329.
• Yan, S., Wang, H., Liu, C., & Zhao, H., 2015. Electrocorticogram classification based on wavelet variance and Fisher linear discriminant analysis. . In The 27th Chinese Control and Decision Conference (2015 CCDC) . IEEE., (s. pp. 5404-5408).