Electrode Area Analysis of EEG Signals Received from Schizophrenic Individuals

Abstract

It is very difficult to distinguish between healthy and schizophrenic individuals based on raw data. However, with the analyzes made, the separation of healthy and sick individuals from each other has become quite evident. In the study, EEG signals were obtained by means of electrodes from the anterior region, middle and posterior regions of the brain, and analyzed according to their positions. Apart from the time-amplitude graph, PSD and STFT analyzes have also performed the analyzes and the results were compared. As a result of this study, the results of PSD analysis are quite successful in distinguishing between healthy and schizophrenic individuals. In this sense, this method includes features that can be used by physicians for diagnostic purposes. In addition, the analysis results are compatible with each other and the results are meaningful. In particular, the results of PSD analyses give very distinctive results that can be used for diagnosis. In addition, the results of the analyzes made with the STFT method are also compatible with the PSD analyses, where healthy individuals have a trend of around 10 Hz, and individuals diagnosed with schizophrenia have a trend of up to 20 Hz.

Keywords

• EEG
• Schizophrenia
• STFT
• PSD Welch method

References

1. Murashko, A. A., & Shmukler, A. (2019). EEG correlates of face recognition in patients with schizophrenia spectrum disorders: A systematic review. Clinical Neurophysiology, 130(6), 986-996.
2. Fond, G., Korchia, T., de Verville, P. S., Godin, O., Schürhoff, F., Berna, F., ... & Boyer, L. (2020). Major depression, sleep, hostility and body mass index are associated with impaired quality of life in schizophrenia. Results from the FACE-SZ cohort. Journal of Affective Disorders, 274, 617-623.
3. Rajji, T. K., Miranda, D., & Mulsant, B. H. (2014). Cognition, function, and disability in patients with schizophrenia: a review of longitudinal studies. The Canadian Journal of Psychiatry, 59(1), 13-17.
4. Mayorov, O. Y., & Fenchenko, V. N. (1996). Method of detection of schizophrenic row disorders at early stages in patients from groups with «functional psychoses» basing on EEG scaling indicators. КЛІНІЧНА ІНФОРМАТИКА І ТЕЛЕМЕДИЦИНА, 52(3), 45.
5. Rasool, S., ZeeshanZafar, M., Ali, Z., & Erum, A. (2018). Schizophrenia: An overview. Clin Pract (Ther), 15, 847-51.
6. Rivollier, F., Lotersztajn, L., Chaumette, B., Krebs, M. O., & Kebir, O. (2014). Hypothèse épigénétique de la schizophrénie: revue de la littérature. L'encephale, 40(5), 380-386.
7. Stip, E., Chouinard, S., & Boulay, L. J. (2005). On the trail of a cognitive enhancer for the treatment of schizophrenia. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 29(2), 219-232.
8. Pearlson, G. D., & Folley, B. S. (2008). Endophenotypes, dimensions, risks: is psychosis analogous to common inherited medical illnesses?. Clinical EEG and neuroscience, 39(2), 73-77.

9. Aydın, E. (2016). Vaka yönetiminin şizofreni hastalarının klinik belirtileri, sosyal işlevselliği ve yaşam kalitesi üzerine etkisi (Uzmanlık tezi). İstanbul, İstanbul Üniversitesi.
10. Çetin, M., & Ceylan, M. E. (2005). AraĢtırma ve Klinik Uygulamada Biyolojik Psikiyatri. Üçüncü baskı, Ġstanbul: Yerküre Tanıtım ve Yayıncılık Hizmetleri, 83-124.
11. Greenwood, T. A., Shutes-David, A., & Tsuang, D. W. (2019). Endophenotypes in schizophrenia: digging deeper to identify genetic mechanisms. Journal of psychiatry and brain science, 4(2).
12. Shim, M., Hwang, H. J., Kim, D. W., Lee, S. H., & Im, C. H. (2016). Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features. Schizophrenia research, 176(2-3), 314-319.
13. Soni, S., Muthukrishnan, S. P., Sood, M., Kaur, S., & Sharma, R. (2020). Altered parahippocampal gyrus activation and its connectivity with resting-state network areas in schizophrenia: An EEG study. Schizophrenia Research, 222, 411-422.
14. http://brain.bio.msu.ru/eeg_schizophrenia.htm
15. Ömer, T. Ü. R. K., Özerdem, M. S., & Akpolat, N. (2015). Gözler açık/kapalı durumunda EEG bantlarındaki frekans değişiminin Güç Spektral Yoğunluğu ile belirlenmesi. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 6(2), 131-138.
16. Seker, S., Akinci, T. C., & Taskin, S. (2012). Spectral and statistical analysis for ferroresonance phenomenon in electric power systems. Electrical Engineering, 94(2), 117-124.
17. Kalin, F., Akinci, T. C., Türkpence, D., Seker, S., & Korkmaz, U. (2020). Detection of Epileptic Seizure Using STFT and Statistical Analysis. In Advances in Neural Signal Processing. IntechOpen.
18. Akinci, T. C. (2011). The defect detection in ceramic materials based on time-frequency analysis by using the method of impulse noise. Archives of Acoustics, 36, 77-85.
19. Kulakli, G., & Akinci, T. C. Psd and Wavelet Analysis of Signals from a Healthy and Epileptic Patient. The Journal of Cognitive Systems, 3(1), 12-14.
20. Yilmaz, M. (2021). Wavelet Based and Statistical EEG Analysis in Patients with Schizophrenia. Traitement du Signal, 38(5).
21. Akgün, Ö. (2011). Mitral Kapak Hastalıklarında Kalp Ses işaretlerinin Analizi (Doctoral dissertation, Marmara Universitesi (Turkey)).