Machine Learning-Driven Diagnostic Models for Alzheimer’s Disease  Prediction using EEG Signal

Balaji Venkateswaran, Md Rashid Equbal, Ashish Jolly, Gendal Lal, Surendra  Singh Chauhan, Anuj Kumar, Ikram Ali

Machine Learning-Driven Diagnostic Models for Alzheimer’s Disease Prediction using EEG Signal

Authors

Balaji Venkateswaran, Md Rashid Equbal, Ashish Jolly, Gendal Lal, Surendra Singh Chauhan, Anuj Kumar, Ikram Ali

Keywords:

Alzheimer’s Disease, EEG Signal Analysis, Machine Learning, Neurodegenerative Disorders

Abstract

This paper presents a machine learning-driven diagnostic model for the early prediction ofAlzheimer’s disease using electroencephalogram (EEG) signals, with a particular focus on the SupportVector Machine (SVM) algorithm. The proposed approach leverages key EEG features extractedthrough advanced signal processing techniques to effectively capture the subtle neurological patternsassociated with Alzheimer’s.

References

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Muscle Artifacts from EEG Data: Multichannel or Single-Channel Techniques? IEEE Sensors Journal, 16(7), 1986–1997. doi: 10.1109/JSEN.2015.2506982.

Alickovic, E., Kevric, J., & Subasi, A. (2018). Performance evaluation of empirical mode decomposition, discrete wavelet transforms, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomedical Signal Processing and Control, 39, 94102. doi: 10.1016/j.bspc.2017.07.022.

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Published

2024-12-25

How to Cite

Balaji Venkateswaran, Md Rashid Equbal, Ashish Jolly, Gendal Lal, Surendra Singh Chauhan, Anuj Kumar, Ikram Ali. (2024). Machine Learning-Driven Diagnostic Models for Alzheimer’s Disease Prediction using EEG Signal. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 4464–4470. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2855