DOI: 10.17798/bitlisfen.1772589 ISSN: 2147-3129

Decoding Dementia: Leveraging the Tunable Q Factor Wavelet Transform to Classify EEG Signals in Alzheimer’s and Frontotemporal Dementia

Mehmet Vural, Yaman Akbulut, Abdulkadir Yelman, Salih Taha Alperen Özçelik, Abdülkadir Şengür
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Early diagnosis of neurodegenerative diseases such as Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD) is essential for improving patient care and reducing healthcare burden.This study proposes a machine learning-based framework for the classification of EEG signals using the Tunable Q-Factor Wavelet Transform (TQWT). EEG recordings obtained from 88 participants (36 AD, 23 FTD, and 29 cognitively normal subjects) were analyzed under resting-state conditions using 19 EEG channels. The signals were decomposed using multi-level TQWT to extract statistical and rhythm-based features from EEG frequency bands. A total of 1881 features were obtained from both standard and rhythm-based decompositions. Several machine learning classifiers including Decision Trees, K-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Neural Networks, and Ensemble Learning models were evaluated.Experimental results show that rhythm-based TQWT features provide a compact and discriminative representation of EEG signals. The highest classification accuracy (92.7%) was achieved using the Ensemble Learning (Bagged Trees) classifier. The results demonstrate that TQWT-based EEG feature extraction combined with machine learning algorithms can effectively distinguish AD, FTD, and cognitively normal subjects, suggesting strong potential for supporting non-invasive dementia diagnosis.

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