Automated analysis of the EEG signals for prediction of possible effectiveness of rTMS treatment in alzheimer's patient

Basit Öğe Kaydı
dc.authorid0000-0002-9282-6568
dc.contributor.authorDuzman, Hacer
dc.contributor.authorTorlak, Meryem
dc.contributor.authorHindi, Osama Ali
dc.contributor.authorKayasandık, Cihan Bilge
dc.date.accessioned2022-10-12T06:21:07Z
dc.date.available2022-10-12T06:21:07Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractAlzheimer'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.
dc.identifier.citationDuzman, H., Torlak, M., Hindi, O. A. ve Kayasandık, C. B. (2022). Automated analysis of the EEG signals for prediction of possible effectiveness of rTMS treatment in alzheimer's patient. 30th Signal Processing and Communications Applications Conference, SIU 2022. Safranbolu, 15-18 May 2022. https://dx.doi.org/10.1109/SIU55565.2022.9864857
dc.identifier.doi10.1109/SIU55565.2022.9864857
dc.identifier.isbn9781665450928
dc.identifier.scopus2-s2.0-85138714401
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://dx.doi.org/10.1109/SIU55565.2022.9864857
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9827
dc.indekslendigikaynakScopus
dc.institutionauthorKayasandık, Cihan Bilge
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof30th Signal Processing and Communications Applications Conference, SIU 2022en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectAlzheimer's Disease
dc.subjectEEG
dc.subjectMachine Learning
dc.subjectMultichannel Analysis
dc.subjectrTMS
dc.titleAutomated analysis of the EEG signals for prediction of possible effectiveness of rTMS treatment in alzheimer's patient
dc.typeConference Object

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
Kayasandik-Cihan Bilge-2022.pdf
Boyut:
1004.92 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Bildiri Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu