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Journals / Bitlis Eren Üniversitesi Fen Bilimleri Dergisi / 2026 / Volume: 15 Issue: 2

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

Mehmet Vural(FIRAT ÜNİVERSİTESİ, REKTÖRLÜK)0000-0002-5973-4856
Yaman Akbulut(FIRAT ÜNİVERSİTESİ)0000-0002-4760-4843
Abdulkadir Yelman(BİNGÖL ÜNİVERSİTESİ)0000-0002-0887-7945
Salih Taha Alperen Özçelik(BİNGÖL ÜNİVERSİTESİ)0000-0002-7929-7542
Abdülkadir Şengür(FIRAT ÜNİVERSİTESİ)0000-0003-1614-2639
Pages
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Publication Date
2026-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental results show that rhythm-based TQWT features provide a compact and discriminative representation of EEG signals.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact:Experimental results show that rhythm-based TQWT features provide a compact and discriminative representation of EEG signals.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:EEG Signals · Alzheimer's Disease · Frontotemporal Dementia

Abstract

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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Cite This Article

Vural, M., Akbulut, Y., Yelman, A., Özçelik, S. T. A., Şengür, A. (2026). Decoding Dementia: Leveraging the Tunable Q Factor Wavelet Transform to Classify EEG Signals in Alzheimer’s and Frontotemporal Dementia. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Volume: 15 Issue: 2. https://doi.org/10.17798/bitlisfen.1772589

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