An EEG Dataset for Alzheimer’s Disease Patients in Iraq: Electrophysiological Recordings Across Cognitive Stages
DOI:
https://doi.org/10.24237/djes.2024.18203Keywords:
Alzheimer’s Disease , EEG, Machine Learning, Deep Learning, AD DatasetAbstract
Alzheimer's disease (AD) is one of the common neurodegenerative disorders presenting with progressive cognitive decline and synaptic impairment. The need for accurate and accessible diagnostic methods is especially pressing in resource-limited regions. This study presents the first open electroencephalography (EEG) dataset for Alzheimer's Disease (AD) in Iraq, recording electrophysiological activity in four stages of cognition, namely, Healthy, Mild, Moderate, and Severe. The dataset comprised recordings from 53 participants and was recorded using a 40-channel EEG device based on the 10-20 electrode placement standard. Considering this EEG dataset, advanced preprocessing, such as the removal of artifacts, Independent Component Analysis (ICA), and Automatic Subspace Reconstruction (ASR), was performed to ensure high signal quality suitable for all state-of-the-art Machine Learning (ML) and Deep Learning (DL) analyses. Benchmark classification was conducted with a wide variety of ML and DL models, including Random Forest (RF), Gradient Boosting methods, Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and Long Short-Term Memory networks (LSTMs), showing promising results-a maximum of 96.85% by Random Forest within ML techniques and a maximum of 96.05% by DNNs in DL techniques. This dataset fills a critical gap in regional AD research and moves toward developing low-cost, noninvasive diagnostic tools. Future work may be performed on more extended and more diverse datasets with more sophisticated multimodal approaches for improving AD diagnosis and early intervention. The dataset can be downloaded from https://shorturl.at/Z0b8D.
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Copyright (c) 2025 Nigar M. Shafiq Surameery, Abdulbasit Alazzawi, Aras T. Asaad, Abas Nariman Sidiq Tilako, Sarwer Jamal Al-Bajalan
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