Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals - paper

Sep 17, 2026

Alzheimer’s and epilepsy are common nervous system diseases in older adults, with seizure likelihood rising for patients with mild cognitive impairment and Alzheimer’s disease. Analyzing electroencephalogram (EEG) signals enables non-invasive discrimination between these conditions. The study proposes a machine learning evaluation model that improves performance by addressing high-dimensional EEG features through particle swarm optimization for initial feature selection and kernel PCA for dimensionality reduction. Experiments on Bon and Dementia benchmark datasets show higher precision, recall, F1-score, and accuracy, reaching 94%, outperforming GMM, RVM, SVM, and ANN methods.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Introduction

Automatika Journal for Control, Measurement, Electronics, Computing and Communications ISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/taut20)[www.tandfonline.com/journals/taut20](homepage: www.tandfonline.com/journals/taut20) Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals P. Jasphin Jeni S harmila & T. S. Shiny Angel To cite this article: P. Jasphin Jeni S harmila & T. S. Shiny Angel (2024) Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals, Automatika, 65:2, 597-608, DOI: 10. 1080/00051144 .2023.2297481 To link to this article: [https://doi.org/10.1080/00051](https://doi.org/10.1080/00051)144.2023.2297481 © 2024 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group. | Published online: 07 Feb 2024. | | | --- | --- | | | Submit your article to this journal | | | Article views: 950 | | | View related articles | | View Crossmark data | | | Citing articles: 2 View citing articles | | Full Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=taut20](https://www.tandfonline.com/action/journalInformation?journalCode=taut20) Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals P. Jasphin Jeni Sharmila and T. S. Shiny Angel Department of Computational Intelligence, SRM IST, Kattankulathur, India ABSTRACT One of the common nervous system diseases in older adults is Alzheimer’s and epilepsy, and the possibility of occurrence increases with age. The chances of seizure are high for patients with mild cognitive impairment and Alzheimer’s disease. So, there is a bidirectional association between Alzheimer’s and epilepsy, as both affect the neurodegenerative processes. Electroencephalogram (EEG) is a possible non-invasive measurement technique widely used to measure the variations in brain signals. EEG signal is analyzed to discriminate the Alzheimer and epilepsy. Numerous research works evaluated the clinical relevance of Alzheimer’s and epilepsy. Specifically, machine learning-based evaluation models developed recently bring the facts by extracting features from the EEG signals. However, machine learning-based models lag in performance due to high dimensional EEG features. For initial feature selection particle swarm optimization is included in the proposed model and to reduce the computation complexity of the classifier, kernel PCA is incorporated for dimensionality reduction. Experimentations using benchmark Bon and Dementia datasets confirms the proposed model better performances in terms of precision, recall, f1-score and accuracy. The attained accuracy of 94% is much better than existing Gaussian Mixture Model (GMM), Relevance Vector Machine (RVM), Support Vector Machine (SVM), and Artificial Neural Network (ANN) methods. ARTICLE HISTORY Received 2 November 2023 Accepted 13 December 2023 KEYWORDS Alzheimer; epilepsy; machine learning; deep belief network; kernel PCA; tuna swarm optimization 1. Introduction The specific reason for neurological disorders varies for each person. However, some common cause that leads to neurological disorders like Alzheimer’s and epilepsy are genetic disorders, congenital abnormalities, lifestyle problems including malnutrition, etc., among the numerous neurological diseases, Alzheimer’s and epilepsy have gained more attention in the research community. The most predominant neurodegenerative brain disease, Alzheimer’s, affects older adults. Alzheimer’s is an irreversible, neurological brain disorder, and it is a progressive disease that slowly reduces brain functions like thinking, memory skills, etc., by destroying the brain cells. Alzheimer’s patients cannot perform simple tasks, and their daily activities become complex due to reduced brain functions. Alzheimer’s is the primary reason for dementia. Alzheimer’s progre

FAQs

Why are Alzheimer’s and epilepsy detection important in older adults?

Alzheimer’s disease and epilepsy are common in older adults, and seizure chances increase with age. Mild cognitive impairment and Alzheimer’s disease are linked to a higher likelihood of seizures.

How does the proposed model process EEG data?

EEG signals are used with machine learning feature evaluation. Particle swarm optimization performs initial feature selection, and kernel PCA reduces dimensionality to lower classifier computation complexity.

What evidence supports the model’s effectiveness?

Experiments on benchmark Bon and Dementia datasets report improved precision, recall, F1-score, and accuracy. The achieved accuracy reaches 94%, outperforming GMM, RVM, SVM, and ANN approaches.

Sep 17, 2026

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