Alkolik ve Normal EEG Sinyallerinin Zaman-Alan Tanımlayıcı Analizi Tabanlı Otomatik Sınıflandırılması

Yıl 2023, Cilt: 35 Sayı: 1, 291 - 300, 28.03.2023

Berna Arı

https://doi.org/10.35234/fumbd.1222526

Öz

Alkolizm, beyin problemlerine ve buna bağlı bilişsel, duygusal ve davranışsal bozukluklara yol açan ciddi bir hastalıktır. Alkolizmi tespit etmek için öne çıkan kaynaklardan biri, Elektroensefalogram (EEG) sinyallerini analiz etmektir. Fakat alkolik EEG sinyallerinin sınıflandırılması, alkolik kişilerin beyin hastalıklarının tanı ve tedavisine yönelik biyomedikal araştırmalarda zorlu bir süreçtir. Bu çalışmada, alkolik EEG sinyallerinden zaman-alan tanımlayıcılarına ve topluluk öğrenmesine dayalı otomatik olarak tanımlayan yeni bir yöntem sunulmaktadır. Önerilen yöntem, tek kanallı EEG sinyallerinin dalgacık paket ayrıştırma ile farklı frekans alt bantlarına ayrılması, zaman-alan tanımlayıcıları ile öznitelik çıkarımı ve topluluk torbalama ağaçları ile sınıflandırma aşamalarından oluşmaktadır. Tek kanallı EEG veri seti ile yapılan deneysel çalışmalarda %97,50 başarım sağlanmıştır. Deneysel sonuçlar önerilen yöntemin, son teknoloji yöntemlere kıyasla daha iyi bir performansa sahip olduğunu göstermektedir. Bu yöntem alkolik bireylerin otomatik tespitinde uzmanlara yardımcı olabilecektir.

Anahtar Kelimeler

EEG, dalgacık paket ayrıştırma, zaman-alan tanımlayıcıları, sınıflandırma.

Kaynakça

• Oscar-Berman, M., & Marinković, K. (2007). Alcohol: effects on neurobehavioral functions and the brain. Neuropsychology review, 17(3), 239-257.
• Shen, M., Wen, P., Song, B., & Li, Y. (2023). Detection of alcoholic EEG signals based on whole brain connectivity and convolution neural networks. Biomedical Signal Processing and Control, 79, 104242.
• Das, D., Zhou, S., & Lee, J. D. (2012). Differentiating alcohol-induced driving behavior using steering wheel signals. IEEE Transactions on Intelligent Transportation Systems, 13(3), 1355-1368.
• World Health Organization. (2018). What Quantitative and Qualitative Methods Have Been Developed to Measure Community Empowerment at a National Level? (Vol. 59). World Health Organization.
• Sadiq, M. T., Akbari, H., Siuly, S., Li, Y., & Wen, P. (2022). Alcoholic EEG signals recognition based on phase space dynamic and geometrical features. Chaos, Solitons & Fractals, 158, 112036.
• Khan, D. M., Yahya, N., Kamel, N., & Faye, I. (2021). Effective connectivity in default mode network for alcoholism diagnosis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 796-808.
• Demir, F., Sengur, A., Ari, A., Siddique, K., & Alswaitti, M. (2021). Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson’s Disease Diagnosis. IEEE Access, 9, 149456-149464.
• Gökşen, N., & Arıca, S. (2017). A simple approach to detect alcoholics using electroencephalographic signals. In EMBEC & NBC 2017 (pp. 1101-1104). Springer, Singapore.
• Mumtaz, W., Vuong, P. L., Xia, L., Malik, A. S., & Rashid, R. B. A. (2017). An EEG-based machine learning method to screen alcohol use disorder. Cognitive neurodynamics, 11(2), 161-171.
• Bajaj, V., Guo, Y., Sengur, A., Siuly, S., & Alcin, O. F. (2017). A hybrid method based on time–frequency images for classification of alcohol and control EEG signals. Neural Computing and Applications, 28(12), 3717-3723.
• Fayyaz, A., Maqbool, M., & Saeed, M. (2019, August). Classifying alcoholics and control patients using deep learning and peak visualization method. In Proceedings of the 3rd International Conference on Vision, Image and Signal Processing (pp. 1-6).
• Agarwal, S., & Zubair, M. (2021). Classification of Alcoholic and Non-Alcoholic EEG Signals Based on Sliding-SSA and Independent Component Analysis. IEEE Sensors Journal, 21(23), 26198-26206.
• Dong, H., Li, T., Ding, R., & Sun, J. (2018). A novel hybrid genetic algorithm with granular information for feature selection and optimization. Applied Soft Computing, 65, 33-46.
• Farsi, L., Siuly, S., Kabir, E., & Wang, H. (2020). Classification of alcoholic EEG signals using a deep learning method. IEEE Sensors Journal, 21(3), 3552-3560.
• American Electroencephalographic Association 1990 (2007) Standard electrode position nomenclature, http://kdd.ics.uci.edu/databases/eeg/eeg.data.html/
• Snodgrass, J. G., & Vanderwart, M. (1980). A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity. Journal of experimental psychology: Human learning and memory, 6(2), 174.
• Mallat, S. (1989). A Theory for Multiresolution Approximations and Wavelet Orthonormal Bases of ℓ2 (r). IEEE Trans. Pattern Recognition and Machine Intelligent, 11, 674-693.
• Alçin, Ö. F., Budak, Ü., Aslan, M., Akbulut, Y., Cömert, Z., Akpınar, M. H., & Şengür, A. (2020). Classification of physical actions from surface EMG signals using the wavelet packet transform and local binary patterns. In Modelling and Analysis of Active Biopotential Signals in Healthcare, Volume 1. IOP Publishing.
• Hu, Y., Wong, Y., Wei, W., Du, Y., Kankanhalli, M., & Geng, W. (2018). A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition. PloS one, 13(10), e0206049.
• Wei, W., Wong, Y., Du, Y., Hu, Y., Kankanhalli, M., & Geng, W. (2019). A multi-stream convolutional neural network for sEMG-based gesture recognition in muscle-computer interface. Pattern Recognition Letters, 119, 131-138.
• Arı, A. (2020). Analysis of EEG signal for seizure detection based on WPT. Electronics Letters, 56(25), 1381-1383.
• Khushaba, R. N., Al-Ani, A., Al-Timemy, A., & Al-Jumaily, A. (2016, December). A fusion of time-domain descriptors for improved myoelectric hand control. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-6). IEEE.
• Arı, A., Ayaz, F. & Hanbay, D. (2019). EMG sinyallerinin kısa zamanlı fourier dönüşüm özellikleri kullanılarak yapay sinir ağları ile sınıflandırılması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 31(2), 443-451.
• Al-Timemy, A. H. (2017, October). An investigation of feature combinations of time-domain power spectral descriptors feature extraction for myoelectric control of hand prostheses. In 2017 Fourth International Conference on Advances in Biomedical Engineering (ICABME) (pp. 1-4). IEEE.
• Hjorth, B. (1970). EEG analysis based on time domain properties. Electroencephalography and clinical neurophysiology, 29(3), 306-310.
• Aslan, M., & Zurel, E. N. (2022). An efficient hybrid model for appliances classification based on time series features. Energy and Buildings, 266, 112087.
• Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140.
• Freund, Y. R., Schapire,(1995), A decision theoretic generalization of online learning and application to boosting. In European Conference on Computational Learning Theory (pp. 23-37).
• Pazoki, M. A Novel Fault Classification Scheme for Series Capacitor Compensated Transmission Line Based on Bagged Tree Ensemble Classifier.
• Chairatanasongporn, N., & Jaiyen, S. (2015, October). A hybrid ensemble of machine and statistical learning using confidence-based boosting. In 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 41-45). IEEE.
• Saeed, M. S., Mustafa, M. W., Sheikh, U. U., Jumani, T. A., & Mirjat, N. H. (2019). Ensemble bagged tree based classification for reducing non-technical losses in multan electric power company of Pakistan. Electronics, 8(8), 860.
• Aslan, M. Derin Öğrenme Tabanlı Otomatik Beyin Tümör Tespiti. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(1), 399-407.
• Uzen, H., Turkoglu, M., & Hanbay, D. (2021). Texture defect classification with multiple pooling and filter ensemble based on deep neural network. Expert Systems with Applications, 175, 114838.
• Ekaputri, C., Widadi, R., & Rizal, A. (2020, June). EEG signal classification for alcoholic and non-alcoholic person using multilevel wavelet packet entropy and support vector machine. In 2020 8th International Conference on Information and Communication Technology (ICoICT) (pp. 1-4). IEEE.
• Malar, E., & Gauthaam, M. (2020). Wavelet analysis of EEG for the identification of alcoholics using probabilistic classifiers and neural networks. International Journal of Intelligence and Sustainable Computing, 1(1), 3-18.
• Kannathal, N., Acharya, U. R., Lim, C. M., & Sadasivan, P. K. (2005). Characterization of EEG—a comparative study. Computer methods and Programs in Biomedicine, 80(1), 17-23.
• Kumari, N., Anwar, S., & Bhattacharjee, V. (2022). A Deep Learning-Based Approach for Accurate Diagnosis of Alcohol Usage Severity Using EEG Signals. IETE Journal of Research, 1-15.