Migren Tanısında Öncü Sinyal Ayrıştırma Yöntemlerinin Performanslarının Karşılaştırılması

Yıl 2022, , 413 - 419, 30.09.2022

Zülfikar Aslan

https://doi.org/10.24012/dumf.1103391

Öz

Bu çalışma, migren hastalığını (MH) Elektroensefalogram (EEG) verisi kullanarak otomatik olarak teşhisini gerçekleştirmek amacıyla bir bilgisayar destekli tanı sistemi sunmaktadır. Ayrıca önerilen yöntemin farklı ayrıştırma yöntemleri ile test edilerek karşılaştırmalı analizi gerçekleştirilmiştir. EEG sinyalleri Çok Ölçekli Temel Bileşen Analizi (MSPCA) uygulanarak var olan gürültüler giderildikten sonra, Ayarlanabilir Q Faktör Dalgacık Dönüşümü (TQWT), Ampirik Mod Ayrıştırma (EMD) ve Ampirik Dalgacık Dönüşümü (EWT) ayrıştırma yöntemleri ile içsel mod fonksiyonları (IMF) bileşenlerine ayrılmıştır. Her bir IMF bileşeninden istatistiksel özellikler hesaplanarak özellik çıkarım işlemi gerçekleştirilmiştir. Sınıflandırma performansları, her bir IMF bileşeninin özellikleri, Rastgele Orman algoritması ile sınıflandırılarak test edilmiştir. En yüksek sınıflandırma doğruluğu IMF1 ve IMF2 bileşenlerinden elde edilmiştir. IMF1 bileşenine ait özelliklerin Rastgele Orman algoritması ile sınıflandırılmasıyla TQWT için 88.9%, EMD için 92.47% ve EWT için 81.41% sınıflandırma başarımı elde edilmiştir. Bu çalışmada gerçekleştirilen deneysel çalışmalar da EMD ayrıştırma yöntemi karşılaştırılan diğer yöntemlere göre MH ve sağlıklı kontrol deneklerin ayırt edilmesinde iyi bir performans sergilediği gözlemlenmiştir.

Anahtar Kelimeler

Migren Teşhisi, Elektroensefalografi, Ayrıştırma Yöntemleri, Ampirik Mod Ayrıştırması, Ayarlanabilir Q Faktörü Dalgacık Dönüşümü, Ampirik Dalgacık Dönüşümü

Kaynakça

• [1] D. Weatherspoon, “Everything You Want to Know About Migraine,” 2017. [Online]. Available: https://www.healthline.com/health/migraine. [Accessed: 17-Sep-2021].
• [2] V. Ulrich, M. Gervil, K. O. Kyvik, J. Olesen, and M. B. Russell, “Evidence of a genetic factor in migraine with aura: a population-based Danish twin study,” Ann. Neurol. Off. J. Am. Neurol. Assoc. Child Neurol. Soc., vol. 45, no. 2, pp. 242–246, 1999. https://doi.org/10.1002/1531-8249(199902)45:2%3C242::AID-ANA15%3E3.0.CO;2-1
• [3] Z.-H. Cao, L.-W. Ko, K.-L. Lai, S.-B. Huang, S.-J. Wang, and C.-T. Lin, “Classification of migraine stages based on resting-state EEG power,” in 2015 International Joint Conference on Neural Networks (IJCNN), 2015, pp. 1–5. https://doi.org/10.1109/IJCNN.2015.7280582
• [4] S. Siuly, S. K. Khare, V. Bajaj, H. Wang, and Y. Zhang, “A Computerized Method for Automatic Detection of Schizophrenia Using EEG Signals,” IEEE Trans. Neural Syst. Rehabil. Eng., 2020. https://doi.org/10.1109/TNSRE.2020.3022715
• [5] Z. ASLAN and M. AKIN, “A COMPARISON OF HEURISTIC SEARCH ALGORITHMS IN AUTOMATIC DETECTION OF SCHIZOPHRENIA,” in 4TH INTERNATIONAL ENERGY & ENGINEERING CONGRESS, 2019, pp. 1248–1258.
• [6] Z. ASLAN and M. AKIN, “Automatic detection of schizophrenia by applying deep learning over spectrogram images of EEG signals,” Trait. du Signal, 2020. https://doi.org/10.18280/ts.370209
• [7] S. B. Akben, D. Tuncel, and A. Alkan, “Classification of multi-channel EEG signals for migraine detection.,” Biomed. Res., vol. 27, no. 3, pp. 743--748, 2016.
• [8] A. R. Hassan, S. Siuly, and Y. Zhang, “Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating,” Comput. Methods Programs Biomed., vol. 137, pp. 247–259, 2016. https://doi.org/10.1016/j.cmpb.2016.09.008
• [9] Z. Yin, Z. Dong, X. Lu, S. Yu, X. Chen, and H. Duan, “A clinical decision support system for the diagnosis of probable migraine and probable tension-type headache based on case-based reasoning,” J. Headache Pain, vol. 16, no. 1, pp. 1–9, 2015. https://doi.org/10.1186/s10194-015-0512-x
• [10] B. Krawczyk, D. Simić, S. Simić, and M. Woźniak, “Automatic diagnosis of primary headaches by machine learning methods,” Cent. Eur. J. Med., vol. 8, no. 2, pp. 157–165, 2013. https://doi.org/10.2478/s11536-012-0098-5
• [11] A. Subasi, A. Ahmed, E. Aličković, and A. R. Hassan, “Effect of photic stimulation for migraine detection using random forest and discrete wavelet transform,” Biomed. Signal Process. Control, vol. 49, pp. 231–239, 2019. https://doi.org/10.1016/j.bspc.2018.12.011
• [12] S. B. Akben, A. Subasi, and D. Tuncel, “Analysis of repetitive flash stimulation frequencies and record periods to detect migraine using artificial neural network,” J. Med. Syst., vol. 36, no. 2, pp. 925–931, 2012. https://doi.org/10.1007/s10916-010-9556-2
• [13] M. Chaman Zar, Alireza; Haigh, Sarah; Grover, Pulkit; Behrmann, “Ultra high-density EEG recording of interictal migraine and controls: sensory and rest. Carnegie Mellon University. Dataset.,” 2020.
• [14] B. R. Bakshi, “Multiscale PCA with application to multivariate statistical process monitoring,” AIChE J., vol. 44, no. 7, pp. 1596–1610, 1998. https://doi.org/10.1002/aic.690440712
• [15] I. W. Selesnick, “Wavelet transform with tunable Q-factor,” IEEE Trans. signal Process., vol. 59, no. 8, pp. 3560–3575, 2011. https://doi.org/10.1109/TSP.2011.2143711
• [16] S. Patidar and R. B. Pachori, “Classification of cardiac sound signals using constrained tunable-Q wavelet transform,” Expert Syst. Appl., vol. 41, no. 16, pp. 7161–7170, 2014. https://doi.org/10.1016/j.eswa.2014.05.052
• [17] V. Bajaj, S. Taran, S. K. Khare, and A. Sengur, “Feature extraction method for classification of alertness and drowsiness states EEG signals,” Appl. Acoust., vol. 163, p. 107224, 2020. https://doi.org/10.1016/j.apacoust.2020.107224
• [18] S. K. Khare and V. Bajaj, “Constrained based tunable Q wavelet transform for efficient decomposition of EEG signals,” Appl. Acoust., vol. 163, p. 107234, 2020. https://doi.org/10.1016/j.apacoust.2020.107234
• [19] C. Amo, L. De Santiago, R. Barea, A. López-Dorado, and L. Boquete, “Analysis of gamma-band activity from human EEG using empirical mode decomposition,” Sensors, vol. 17, no. 5, p. 989, 2017. https://doi.org/10.3390/s17050989
• [20] N. E. Huang et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. London. Ser. A Math. Phys. Eng. Sci., vol. 454, no. 1971, pp. 903–995, 1998. https://doi.org/10.1098/rspa.1998.0193
• [21] F.-F. Tsai, S.-Z. Fan, Y.-S. Lin, N. E. Huang, and J.-R. Yeh, “Investigating power density and the degree of nonlinearity in intrinsic components of anesthesia EEG by the Hilbert-Huang transform: an example using ketamine and alfentanil,” PLoS One, vol. 11, no. 12, p. e0168108, 2016. https://doi.org/10.1371/journal.pone.0168108
• [22] H. Liang, S. L. Bressler, R. Desimone, and P. Fries, “Empirical mode decomposition: a method for analyzing neural data,” Neurocomputing, vol. 65, pp. 801–807, 2005. https://doi.org/10.1016/j.neucom.2004.10.077
• [23] C.-H. Hsu, C.-Y. Lee, and W.-K. Liang, “An improved method for measuring mismatch negativity using ensemble empirical mode decomposition,” J. Neurosci. Methods, vol. 264, pp. 78–85, 2016. https://doi.org/10.1016/j.jneumeth.2016.02.015
• [24] H. Liang, S. L. Bressler, E. A. Buffalo, R. Desimone, and P. Fries, “Empirical mode decomposition of field potentials from macaque V4 in visual spatial attention,” Biol. Cybern., vol. 92, no. 6, pp. 380–392, 2005. https://doi.org/10.1007/s00422-005-0566-y
• [25] J. Gilles, “Empirical wavelet transform,” IEEE Trans. signal Process., vol. 61, no. 16, pp. 3999–4010, 2013. https://doi.org/10.1109/TSP.2013.2265222
• [26] V. S. Geetikaverma, “Empirical Wavelet Transform \& its Comparison with Empirical Mode Decomposition: A review,” Int. J. Appl. Eng, vol. 4, no. 5, 2016.
• [27] K. Jackowski, D. Jankowski, D. Simić, and S. Simić, “Migraine diagnosis support system based on classifier ensemble,” in International Conference on ICT Innovations, 2014, pp. 329–339. http://www.doi.org/10.1007/978-3-319-09879-1_33