Analysis and Classification of Schizophrenia Using Event Related Potential Signals

Öz

Schizophrenia (SZ) is a neuropsychiatric disease that affects many people around the world and causes death if not diagnosed and treated early. One of the commonly used methods for early diagnosis is electroencephalography (EEG). The application of signal processing and machine learning methods to EEG signals can support experts and researchers who want to determine SZ disease. In this study, event-related potential (ERP) signals were obtained from the recorded EEG signals as a result of sending auditory stimuli to the SZ patient and healthy control (HC) group. P300 amplitude-latency, hjorth parameters and entropy values were calculated as features from these signals. The features obtained were evaluated with Support Vector Machines (SVM), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANN) classifiers to distinguish SZ patients from the HC group. In this study, the most successful result was obtained in the ANN classifier with an accuracy rate of 93.9%.

Anahtar Kelimeler

• Schizophrenia
• electroencephalography
• event-related potential
• artificial neural networks

Olayla İlgili Potansiyel Sinyalleri Kullanarak Şizofreninin Analizi ve Sınıflandırılması

Öz

Şizofreni (SZ), dünya çapında birçok insanı etkileyen ve erken teşhis ve tedavi edilmediği takdirde ölüme neden olan nöropsikiyatrik bir hastalıktır. Erken tanı için yaygın olarak kullanılan yöntemlerden biri elektroensefalografidir (EEG). Sinyal işleme ve makine öğrenme yöntemlerinin EEG sinyallerine uygulanması, SZ hastalığını belirlemek isteyen uzmanlara ve araştırmacılara destek olabilir. Bu çalışmada, SZ hastası ve sağlıklı kontrol grubuna işitsel uyaranların gönderilmesi sonucunda kaydedilen EEG sinyallerinden olaya bağlı potansiyel (OİP) sinyalleri elde edilmiştir. Bu sinyallerden öznitelikler olarak P300 genlik-gecikme, hjorth parametreleri ve entropi değerleri hesaplanmıştır. Elde edilen özellikler, SZ hastalarını sağlıklı kontrol grubundan ayırt etmek için Destek Vektör Makineleri (DVM), K-En Yakın Komşu (KEYK) ve Yapay Sinir Ağları (YSA) sınıflandırıcıları ile değerlendirildi. Bu çalışmada en başarılı sonuç %93,9 doğruluk oranı ile YSA sınıflandırıcısında elde edilmiştir.

Anahtar Kelimeler

• Şizofreni
• elektroensefalografi
• olaya bağlı potansiyel
• yapay sinir ağları

Kaynakça

1. Buettner, R., Hirschmiller, M., Schlosser, K., Rössle, M., Fernandes, M., Timm, I. J. (2019, October). High-performance exclusion of schizophrenia using a novel machine learning method on EEG data. In 2019 IEEE International Conference on E-Health Networking, Application & Services (HealthCom) (pp. 1-6). IEEE.
2. WHO. Accessed: Jul 14, 2022. [Online]. Available: https://www.who.int/ mental_health/management/schizophrenia/en/
3. Siuly, S., Khare, S. K., Bajaj, V., Wang, H., & Zhang, Y. (2020). A computerized method for automatic detection of schizophrenia using EEG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(11), 2390-2400.
4. Lapsekili, N., Uzun, Ö., Sütçigil, L., Ak, M., Yücel, M. (2011). Şizofreni Hastalarında İlk Atakta P300 Bulguları ile Nörolojik Silik İşaretler Arasındaki İlişki. Dusunen Adam: Journal of Psychiatry & Neurological Sciences, 24(3).
5. Luck, S. J. (2014). An introduction to the event-related potential technique. MIT press.
6. Orhanbulucu, F., Latifoğlu, F., Baş, A. (2020). K-Ortalamalar Kümeleme Yöntemi Kullanılarak ALS Hastalarında Dikkatin Olaya İlişkin Potansiyel Sinyalleri İle İncelenmesi. Avrupa Bilim ve Teknoloji Dergisi, 239-244.
7. Devia, C., Mayol-Troncoso, R., Parrini, J., Orellana, G., Ruiz, A., Maldonado, P. E., Egaña, J. I. (2019). EEG classification during scene free-viewing for schizophrenia detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(6), 1193-1199.
8. Zhang, L. (2019, July). EEG signals classification using machine learning for the identification and diagnosis of schizophrenia. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4521-4524). IEEE.

9. Cavanagh, J. F., Kumar, P., Mueller, A. A., Richardson, S. P., Mueen, A. (2018). Diminished EEG habituation to novel events effectively classifies Parkinson’s patients. Clinical Neurophysiology, 129(2), 409-418.
10. Boostani, R., Sadatnezhad, K., Sabeti, M. (2009). An efficient classifier to diagnose of schizophrenia based on the EEG signals. Expert Systems with Applications, 36(3), 6492-6499.
11. B. Roach, (2017). EEG data from basic sensory task in schizophrenia -button press and auditory tone event related potentials from 81 human subjects. [Online]. Available: https://www.kaggle.com/datasets/broach/button-tone-sz
12. Ford, J. M., Palzes, V. A., Roach, B. J., Mathalon, D. H. (2014). Did I do that? Abnormal predictive processes in schizophrenia when button pressing to deliver a tone. Schizophrenia bulletin, 40(4), 804-812.
13. Orhanbulucu, F., Latifoğlu, F. (2022). Detection of amyotrophic lateral sclerosis disease from event-related potentials using variational mode decomposition method. Computer Methods in Biomechanics and Biomedical Engineering, 25(8), 840-851.
14. Chawla, N. V., Bowyer, K. W., Hall, L. O., Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.
15. Dong, S., Reder, L. M., Yao, Y., Liu, Y., Chen, F. (2015). Individual differences in working memory capacity are reflected in different ERP and EEG patterns to task difficulty. Brain research, 1616, 146-156.
16. Cecchin, T., Ranta, R., Koessler, L., Caspary, O., Vespignani, H., & Maillard, L. (2010). Seizure lateralization in scalp EEG using Hjorth parameters. Clinical neurophysiology, 121(3), 290-300.
17. Amin, H. U., Malik, A. S., Ahmad, R. F., Badruddin, N., Kamel, N., Hussain, M., Chooi, W. T. (2015). Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australasian physical & engineering sciences in medicine, 38(1), 139-149.
18. Kotsiantis, S. B., Zaharakis, I. D., Pintelas, P. E. (2006). Machine learning: a review of classification and combining techniques. Artificial Intelligence Review, 26(3), 159-190.
19. Schall, U., Catts, S. V., Karayanidis, F., Ward, P. B. (1999). Auditory event-related potential indices of fronto-temporal information processing in schizophrenia syndromes: valid outcome prediction of clozapine therapy in a three-year follow-up. International Journal of Neuropsychopharmacology, 2(2), 83-93.