Predicting Alzheimer's Disease Using Artificial Intelligence Techniques

Haider Ali Muften

Authors

Haider Ali Muften Department of Computer Science / College of Education / Sawa University / Al-Muthanna / Iraq

Keywords:

Alzheimer’s Disease, EEG Signal Classification, Machine Learning, Convolutional Neural Network (CNN), Early Detection

Abstract

Alzheimer disease (AD) is a progressive neurodegenerative disorder beginning with the accumulation of pathological proteins in brains and ultimately leading to neuronal death. Alzheimer's disease is among the most severe of cases that have a significant deterioration in cognitive ability with particular emphasis on detrimental effects to memory, intellect and more general behavioral functions. How: There is no cure at the moment, but researchers are working tirelessly in hopes of finding one. The immediate need for early stage diagnosis and manifestation of biomarkers has streamlined the therapeutic algorithms in terms of potential drug trials & preventive medication regimens, instituted at a very early developmental phase. Electroencephalography (EEG) is simple, faster and cost-effective non-invasive technique which can be used as adjunct for automation of Alzheimer's disease diagnosis. It merits inption that epoch length of segment EEG signals data might impact the performance for classification. To tackle this issue, we presented a device-free diagnostic EEG framework, where the ideal segment length estimation for classification is obtained using machine learning and deep learning-based approaches. It consists of the data collection of EEG, preprocessing by removing noise, and segmentation in time axis. In this work, we run a comparison of using deep learning models (multilayer neural networks and convolutional neural networks) to the more traditional machine learning models. Model: Training (logospheric regression, decision tree, random forest, gradient enhancement, AdaBoost, XGBoost; CNN and MLP); Classification; Evaluation The accuracy we obtained using open data Kaggle set is 0.83%, 96,7%. and 99,3% respectively. We tested the proposed models on an entirely novel application of identifying frontotemporal dementia and achieved substantial advances relative to previous publications. Moreover, we performed several analyses and graphically presented extracted categories contents to justify the developed model. The study will set a standard in the realm of neurological disorder research and one that will support future researchers and technical experts focused on this field.

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References

S. Siuly and Y. Zhang, “Medical big data: Neurological diseases diagnosis through medical data analysis,” Data Science and Engineering, vol. 1, no. 2, pp. 54–64, 2016.

J. Weller and A. Budson, “Current understanding of Alzheimer’s disease diagnosis and treatment,” F1000Research, vol. 7, p. F1000 Faculty Rev-1161, 2018.

P. M. Rodrigues et al., “Lacsogram: A new EEG tool to diagnose Alzheimer's disease,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 9, pp. 3384–3395, 2021.

K. Mollazadeh-Moghaddam et al., “Delirium associated with donepezil in a patient with Alzheimer's disease: A case report,” Iranian Journal of Psychiatry, vol. 8, no. 1, p. 59, 2013.

F. Rahmani, M. Fathi, and E. Bahadori, “Recognition of famous and unfamiliar faces among patients suffering from Amnesia Mild Cognitive Impairment (AMCI) and Alzheimer’s Disease,” Iranian Journal of Psychiatry, vol. 14, no. 3, pp. 227–234, 2019.

Y. Hou et al., “GCNs-net: A graph convolutional neural network approach for decoding time-resolved EEG motor imagery signals,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 6, pp. 7312–7323, 2022.

K. AlSharabi et al., “EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques,” Frontiers in Human Neuroscience, vol. 17, Art. no. 1190203, 2023.

J. Liss et al., “Practical recommendations for timely, accurate diagnosis of symptomatic Alzheimer’s disease (MCI and dementia) in primary care: A review and synthesis,” Journal of Internal Medicine, vol. 290, no. 2, pp. 310–334, 2021.

A. Khaleghi et al., “Computational neuroscience approach to psychiatry: A review on theory-driven approaches,” Clinical Psychopharmacology and Neuroscience, vol. 20, no. 1, p. 26, 2022.

A. Khaleghi et al., “New ways to manage pandemics: Using technologies in the era of COVID-19: A narrative review,” Iranian Journal of Psychiatry, vol. 15, no. 3, p. 236, 2020.

A. Khaleghi et al., “Abnormalities of alpha activity in frontocentral region of the brain as a biomarker to diagnose adolescents with bipolar disorder,” Clinical EEG and Neuroscience, vol. 50, no. 5, pp. 311–318, 2019.

A. Khaleghi et al., “A neuronal population model based on cellular automata to simulate the electrical waves of the brain,” Waves in Random and Complex Media, vol. 34, no. 3, pp. 1445–1464, 2024.

J. Dauwels, F. Vialatte, and A. Cichocki, “Diagnosis of Alzheimer's disease from EEG signals: Where are we standing?,” Current Alzheimer Research, vol. 7, no. 6, pp. 487–505, 2010.

A. Afzali et al., “Automated major depressive disorder diagnosis using a dual-input deep learning model and image generation from EEG signals,” Waves in Random and Complex Media, pp. 1–16, 2023.

S. Dodge and L. Karam, “A study and comparison of human and deep learning recognition performance under visual distortions,” in Proc. 26th Int. Conf. Computer Communication and Networks (ICCCN), Vancouver, BC, Canada, 2017, pp. 1–7.

M. Fan et al., “Topological pattern recognition of severe Alzheimer's disease via regularized supervised learning of EEG complexity,” Frontiers in Neuroscience, vol. 12, Art. no. 685, 2018.

D. Puri et al., “EEG-based diagnosis of Alzheimer's disease using Kolmogorov complexity,” in Applied Information Processing Systems: Proceedings of ICCET 2021. Singapore: Springer, 2021, pp. 157–165.

D. Puri et al., “Alzheimer’s disease detection using empirical mode decomposition and Hjorth parameters of EEG signal,” in Proc. Int. Conf. Decision Aid Sciences and Applications (DASA), Chiang Rai, Thailand, 2022, pp. 1–6.

J. Escudero et al., “Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy,” Physiological Measurement, vol. 27, no. 11, pp. 1091–1106, 2006.

D. Abásolo et al., “Entropy analysis of the EEG background activity in Alzheimer's disease patients,” Physiological Measurement, vol. 27, no. 3, pp. 241–253, 2006.

E. Neto et al., “Regularized linear discriminant analysis of EEG features in dementia patients,” Frontiers in Aging Neuroscience, vol. 8, Art. no. 273, 2016.

Y. Zhao and L. He, “Deep learning in the EEG diagnosis of Alzheimer’s disease,” in Asian Conference on Computer Vision (ACCV), Cham, Switzerland: Springer, 2014, pp. 340–353.

B. Nayana et al., “EEG-based neurodegenerative disease diagnosis: Comparative analysis of conventional methods and deep learning models,” Scientific Reports, vol. 15, no. 1, Art. no. 15950, 2025.

J. Lin and W. Huang, “Automated diagnosis of mild cognitive impairment through connectivity analysis of EEG signals and a DL scheme,” Journal of Engineering and Applied Science, vol. 72, no. 1, Art. no. 109, 2025.

A. M. Alghamdi et al., “A novel approach hybrid of ensemble learning and 3-D CNN mechanism: Early-stage diagnosis of Alzheimer’s disease using EEG signals,” Scientific Reports, vol. 15, no. 1, Art. no. 35893, 2025.

F. C. Morabito et al., “Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings,” in Proc. IEEE 2nd Int. Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow (RTSI), Bologna, Italy, 2016, pp. 1–6.

R. Ferri et al., “Stacked autoencoders as new models for an accurate Alzheimer’s disease classification support using resting-state EEG and MRI measurements,” Clinical Neurophysiology, vol. 132, no. 1, pp. 232–245, 2021.

C. Ieracitano et al., “A convolutional neural network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings,” Neurocomputing, vol. 323, pp. 96–107, 2019.

A. Miltiadous et al., “DICE-net: A novel convolution-transformer architecture for Alzheimer detection in EEG signals,” IEEE Access, vol. 11, pp. 71840–71858, 2023.

Y. Chen et al., “Multi-feature fusion learning for Alzheimer's disease prediction using EEG signals in resting state,” Frontiers in Neuroscience, vol. 17, Art. no. 1272834, 2023.

X. Bi and H. Wang, “Early Alzheimer’s disease diagnosis based on EEG spectral images using deep learning,” Neural Networks, vol. 114, pp. 119–135, 2019.

S. Fouladi et al., “Efficient deep neural networks for classification of Alzheimer’s disease and mild cognitive impairment from scalp EEG recordings,” Cognitive Computation, vol. 14, no. 4, pp. 1247–1268, 2022.

M. Şeker and M. S. Özerdem, “Investigating convolutional and transformer-based models for classifying Mild Cognitive Impairment using 2D spectral images of resting-state EEG,” Biomedical Signal Processing and Control, vol. 105, Art. no. 107667, 2025.

P. Datta et al., “Alzheimer's Disease Detection and Its Classification by Deep Learning,” in Proc. Int. Conf. Power Energy, Environment & Intelligent Control (PEEIC), 2023, pp. 1–6.

Q. Dao, M. A. El-Yacoubi, and A.-S. Rigaud, “Detection of Alzheimer disease on online handwriting using 1D convolutional neural network,” IEEE Access, vol. 11, pp. 2148–2155, 2023.