Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals
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...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-04-01
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| Series: | Automatika |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2023.2297481 |
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| author | P. Jasphin Jeni Sharmila T. S. Shiny Angel |
| author_facet | P. Jasphin Jeni Sharmila T. S. Shiny Angel |
| author_sort | P. Jasphin Jeni Sharmila |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b331dcbdeba040bcb8e8cc5727ceaf41 |
| institution | Kabale University |
| issn | 0005-1144 1848-3380 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj-art-b331dcbdeba040bcb8e8cc5727ceaf412025-08-20T03:48:14ZengTaylor & Francis GroupAutomatika0005-11441848-33802024-04-0165259760810.1080/00051144.2023.2297481Optimized machine learning model for Alzheimer and epilepsy detection from EEG signalsP. Jasphin Jeni Sharmila0T. S. Shiny Angel1Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, IndiaDepartment of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, IndiaOne 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.https://www.tandfonline.com/doi/10.1080/00051144.2023.2297481Alzheimerepilepsymachine learningdeep belief networkkernel PCAtuna swarm optimization |
| spellingShingle | P. Jasphin Jeni Sharmila T. S. Shiny Angel Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals Automatika Alzheimer epilepsy machine learning deep belief network kernel PCA tuna swarm optimization |
| title | Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals |
| title_full | Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals |
| title_fullStr | Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals |
| title_full_unstemmed | Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals |
| title_short | Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals |
| title_sort | optimized machine learning model for alzheimer and epilepsy detection from eeg signals |
| topic | Alzheimer epilepsy machine learning deep belief network kernel PCA tuna swarm optimization |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2023.2297481 |
| work_keys_str_mv | AT pjasphinjenisharmila optimizedmachinelearningmodelforalzheimerandepilepsydetectionfromeegsignals AT tsshinyangel optimizedmachinelearningmodelforalzheimerandepilepsydetectionfromeegsignals |