Original scientific paper
https://doi.org/10.1080/00051144.2023.2297481
Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals
P. Jasphin Jeni Sharmila
; Department of Computational Intelligence, SRM IST, Kattankulathur, India
*
T. S. Shiny Angel
; Department of Computational Intelligence, SRM IST, Kattankulathur, India
* Corresponding author.
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.
Keywords
Alzheimer; epilepsy; machine learning; deep belief network; kernel PCA; tuna swarm optimization
Hrčak ID:
323050
URI
Publication date:
7.2.2024.
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