Ayrık Dalgacık Dönüşüm Lideri ve Topluluk Öğrenme Yöntemleri Kullanılarak EEG Kayıtlarından Hafif Bilişsel Bozukluğun Otomatik Tespiti

Year 2023, Volume: 14 Issue: 1, 47 - 54, 23.03.2023

Afrah Said , Hanife Göker

https://doi.org/10.24012/dumf.1227520

Abstract

Hafif Bilişsel Bozukluk (MCI), yaygın olarak normal biliş ile demans arasında bir geçiş aşaması olarak adlandırılan bir bilişsel gerileme riskidir. MCI'lı hastalar tipik olarak, düşünme yeteneklerinin bozulması gibi bilişsel eksikliklere neden olan Alzheimer hastalığına (AD) ilerlemektedir. Bu çalışma, elektroensefalografi (EEG) sinyallerini kullanarak MCI hastalarını saptamayı amaçlamaktadır. Bu çalışmada kullanılan EEG veri seti, 18 MCI ve 16 kontrol gruplarından kaydedilen EEG sinyallerinden oluşmaktadır. Çalışmada, öncelikle çok ölçekli temel bileşenler analizi (çok ölçekli PCA) kullanılarak, EEG sinyallerinden gürültüler temizlenmiştir. Daha sonra ayrık dalgacık dönüşüm lideri (DWT lideri) özellik çıkarma yöntemi kullanılarak, EEG sinyallerinden 36 öznitelik çıkarılmıştır. Son olarak bu öznitelik vektörleri ile topluluk öğrenme algoritmaları kullanılarak kontrol ve MCI grupları sınıflandırılmıştır. Sonuç olarak AdaBoostM1 algoritması %93,50 doğruluk, %93,27 duyarlılık, %93,75 özgüllük, %94,38 kesinlik, %93,82 f1 skoru ve %86,97 Matthews korelasyon katsayısı (MCC) ile en yüksek başarıya sahiptir. Bu çalışma, oldukça tatmin edici doğruluk elde ederek, topluluk öğrenme algoritmasının MCI tespiti için kullanılabileceğini kanıtlamaktadır.

Keywords

Ayrık dalgacık dönüşüm lideri, EEG, topluluk öğrenme, MCI, otomatik tanı

Automatic Detection of Mild Cognitive Impairment from EEG Recordings Using Discrete Wavelet Transform Leader and Ensemble Learning Methods

Abstract

Mild Cognitive Impairment (MCI) is a risk of cognitive decline, commonly referred to as a transitional stage between normal cognition and dementia. Patients with MCI typically progress to Alzheimer's disease (AD), which causes cognitive deficits such as deterioration of their thinking abilities. This study aims to detect MCI patients using electroencephalography (EEG) signals. The EEG dataset used in this study consists of EEG signals recorded from 18 MCI and 16 control groups. Firstly, EEG signals were denoised using multiscale principal component analysis (multiscale PCA). Then, 36 features were extracted from the EEG signals using the discrete wavelet transform leader (DWT leader) feature extraction method. Finally, using the extracted feature vectors, control groups, and MCI groups were classified by ensemble learning algorithms. As a result, AdaBoostM1 algorithm has the highest success with 93.50% accuracy, 93.27% sensitivity, 93.75% specificity, 94.38% precision, 93.82% f1-score, and 86.97% Matthews correlation coefficient (MCC). By achieving quite satisfactory accuracy, this study proves that the ensemble learning algorithm can also be used for MCI detection.

Keywords

Discrete wavelet transform leader, EEG, ensemble learning, MCI, automatic detection

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