Automated Analysis of the EEG signals for Prediction of Possible Effectiveness of rTMS Treatment in Alzheimer's Patient


Duzman H., Torlak M., Hindi O. A., Kayasandik C. B.

30th Signal Processing and Communications Applications Conference, SIU 2022, Safranbolu, Turkey, 15 - 18 May 2022 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu55565.2022.9864857
  • City: Safranbolu
  • Country: Turkey
  • Keywords: Alzheimer's disease, EEG, Machine Learning, Multichannel Analysis, rTMS
  • Istanbul Technical University Affiliated: Yes

Abstract

© 2022 IEEE.Alzheimer's disease (AD) is a progressive, chronic neurodegenerative brain disease that generally infects the elderly. The analysis of electroencephalography (EEG) signals has been commonly used for diagnosis. Repetitive transcranial magnetic stimulation (rTMS) is one of the most significant non-pharmacological methods that offer a potential treatment for neurological and psychiatric diseases. Nevertheless, recent studies have shown that patients do not benefit from this treatment at the same level. In that case, there would be a loss of time, money, and effort in the application of treatment. In this project, the EEG data is collected and then analyzed through multichannel analysis using various machine-learning methods. There are 14 patient datasets available for analysis. Thus, the main aim is to find the most significant features of the patients' EEG signals who were treated by rTMS. The novelty of this project lies in performing multichannel analysis and finding a personalized treatment for AD by combining both EEG Analysis and rTMS treatment; where such a combination has not been done yet in any project before. Even in the studies that obtained good accuracy results, there was a lot of useful information missed due to the absence of multichannel analysis. As with multichannel analysis, the data of each channel is analyzed separately. The results showed that the benefit of rTMS treatment can be distinguished for each patient based mainly on the following features of their EEG data signals: Alpha, Beta, and Delta band power features, besides the complexity feature.