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

doi:10.17798/bitlisfen.1772589

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

Keywords

Ethical Statement

The authors declare that there is no conflict of interest.

Thanks

This study was conducted using the facilities of Fırat University. No external funding was received.

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other), Biomedical Sciences and Technology, Biomedical Diagnosis

Journal Section

Research Article

Authors

Mehmet Vural ^*
0000-0002-5973-4856
Türkiye

Yaman Akbulut
0000-0002-4760-4843
Türkiye

Abdulkadir Yelman
0000-0002-0887-7945
Türkiye

Salih Taha Alperen Özçelik
0000-0002-7929-7542
Türkiye

Abdülkadir Şengür
0000-0003-1614-2639
Türkiye

Publication Date

June 30, 2026

Submission Date

August 26, 2025

Acceptance Date

May 21, 2026

Published in Issue

Year 2026 Volume: 15 Number: 2

DOI

https://doi.org/10.17798/bitlisfen.1772589

IZ

https://izlik.org/JA85MG47FG

Cite

RIS / Bibtex

APA

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, 15(2), 545-556. https://doi.org/10.17798/bitlisfen.1772589

AMA

1.Vural M, Akbulut Y, Yelman A, Özçelik STA, Şengür A. 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. 2026;15(2):545-556. doi:10.17798/bitlisfen.1772589

Chicago

Vural, Mehmet, Yaman Akbulut, Abdulkadir Yelman, Salih Taha Alperen Özçelik, and Abdülkadir Şengür. 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 15 (2): 545-56. https://doi.org/10.17798/bitlisfen.1772589.

EndNote

Vural M, Akbulut Y, Yelman A, Özçelik STA, Şengür A (June 1, 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 15 2 545–556.

IEEE

[1]M. Vural, Y. Akbulut, A. Yelman, S. T. A. Özçelik, and A. Şengür, “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, vol. 15, no. 2, pp. 545–556, June 2026, doi: 10.17798/bitlisfen.1772589.

ISNAD

Vural, Mehmet - Akbulut, Yaman - Yelman, Abdulkadir - Özçelik, Salih Taha Alperen - Şengür, Abdülkadir. “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 15/2 (June 1, 2026): 545-556. https://doi.org/10.17798/bitlisfen.1772589.

JAMA

1.Vural M, Akbulut Y, Yelman A, Özçelik STA, Şengür A. 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. 2026;15:545–556.

MLA

Vural, Mehmet, et al. “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, vol. 15, no. 2, June 2026, pp. 545-56, doi:10.17798/bitlisfen.1772589.

Vancouver

1.Mehmet Vural, Yaman Akbulut, Abdulkadir Yelman, Salih Taha Alperen Özçelik, Abdülkadir Şengür. 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. 2026 Jun. 1;15(2):545-56. doi:10.17798/bitlisfen.1772589