EEG SIGNAL ANALYSIS FOR CLASSIFYING ALZHEIMER’S AND FRONTOTEMPORAL DEMENTIA DISORDERS USING ENSEMBLE METHODS
Accurate identification of neurodegenerative disorders, including Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD), is crucial for prompt medical intervention. This research introduces an EEG-based ensemble classification framework aimed at differentiating between AD, FTD, and Cognitively...
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| Format: | Article |
| Language: | English |
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XLESCIENCE
2025-06-01
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| Series: | International Journal of Advances in Signal and Image Sciences |
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| Online Access: | https://xlescience.org/index.php/IJASIS/article/view/279 |
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| author | G Sudha N Mohankumar Gousia Thahniyath Raja Thimmarayan |
| author_facet | G Sudha N Mohankumar Gousia Thahniyath Raja Thimmarayan |
| author_sort | G Sudha |
| collection | DOAJ |
| description | Accurate identification of neurodegenerative disorders, including Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD), is crucial for prompt medical intervention. This research introduces an EEG-based ensemble classification framework aimed at differentiating between AD, FTD, and Cognitively Normal (CN) people. The system uses ElectroEncephaloGram (EEG) signals derived from the publicly accessible dataset from Kaggle. Independent Component Analysis (ICA) is used for preprocessing to remove artefacts and noise. The important key characteristics are identified and chosen by Recursive Feature Elimination (RFE) to provide optimum input for classification from the features of Discrete Wavelet Transform (DWT) sub-bands. Several classifiers, including Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN), are trained, and their predictions are aggregated using Logistic Regression (LR) as the meta-classifier. The resulting model has exceptional diagnostic performance, with the LR ensemble achieving an accuracy of 98.98% in differentiating the three subject groups. Results proved the capability of combining EEG signal analysis with ensemble learning techniques to enhance clinical decision making for the early identification of dementia-related disorders. |
| format | Article |
| id | doaj-art-0fc48a9373f5482981f39aa8ae11d0f7 |
| institution | Matheson Library |
| issn | 2457-0370 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | XLESCIENCE |
| record_format | Article |
| series | International Journal of Advances in Signal and Image Sciences |
| spelling | doaj-art-0fc48a9373f5482981f39aa8ae11d0f72025-07-03T07:00:42ZengXLESCIENCEInternational Journal of Advances in Signal and Image Sciences2457-03702025-06-01111445410.29284/ijasis.11.1.2025.44-54307EEG SIGNAL ANALYSIS FOR CLASSIFYING ALZHEIMER’S AND FRONTOTEMPORAL DEMENTIA DISORDERS USING ENSEMBLE METHODSG SudhaN MohankumarGousia ThahniyathRaja ThimmarayanAccurate identification of neurodegenerative disorders, including Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD), is crucial for prompt medical intervention. This research introduces an EEG-based ensemble classification framework aimed at differentiating between AD, FTD, and Cognitively Normal (CN) people. The system uses ElectroEncephaloGram (EEG) signals derived from the publicly accessible dataset from Kaggle. Independent Component Analysis (ICA) is used for preprocessing to remove artefacts and noise. The important key characteristics are identified and chosen by Recursive Feature Elimination (RFE) to provide optimum input for classification from the features of Discrete Wavelet Transform (DWT) sub-bands. Several classifiers, including Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN), are trained, and their predictions are aggregated using Logistic Regression (LR) as the meta-classifier. The resulting model has exceptional diagnostic performance, with the LR ensemble achieving an accuracy of 98.98% in differentiating the three subject groups. Results proved the capability of combining EEG signal analysis with ensemble learning techniques to enhance clinical decision making for the early identification of dementia-related disorders.https://xlescience.org/index.php/IJASIS/article/view/279alzheimer's disease, frontotemporal dementia, eeg signal, ensemble learning, neurodegenerative disorders. |
| spellingShingle | G Sudha N Mohankumar Gousia Thahniyath Raja Thimmarayan EEG SIGNAL ANALYSIS FOR CLASSIFYING ALZHEIMER’S AND FRONTOTEMPORAL DEMENTIA DISORDERS USING ENSEMBLE METHODS International Journal of Advances in Signal and Image Sciences alzheimer's disease, frontotemporal dementia, eeg signal, ensemble learning, neurodegenerative disorders. |
| title | EEG SIGNAL ANALYSIS FOR CLASSIFYING ALZHEIMER’S AND FRONTOTEMPORAL DEMENTIA DISORDERS USING ENSEMBLE METHODS |
| title_full | EEG SIGNAL ANALYSIS FOR CLASSIFYING ALZHEIMER’S AND FRONTOTEMPORAL DEMENTIA DISORDERS USING ENSEMBLE METHODS |
| title_fullStr | EEG SIGNAL ANALYSIS FOR CLASSIFYING ALZHEIMER’S AND FRONTOTEMPORAL DEMENTIA DISORDERS USING ENSEMBLE METHODS |
| title_full_unstemmed | EEG SIGNAL ANALYSIS FOR CLASSIFYING ALZHEIMER’S AND FRONTOTEMPORAL DEMENTIA DISORDERS USING ENSEMBLE METHODS |
| title_short | EEG SIGNAL ANALYSIS FOR CLASSIFYING ALZHEIMER’S AND FRONTOTEMPORAL DEMENTIA DISORDERS USING ENSEMBLE METHODS |
| title_sort | eeg signal analysis for classifying alzheimer s and frontotemporal dementia disorders using ensemble methods |
| topic | alzheimer's disease, frontotemporal dementia, eeg signal, ensemble learning, neurodegenerative disorders. |
| url | https://xlescience.org/index.php/IJASIS/article/view/279 |
| work_keys_str_mv | AT gsudha eegsignalanalysisforclassifyingalzheimersandfrontotemporaldementiadisordersusingensemblemethods AT nmohankumar eegsignalanalysisforclassifyingalzheimersandfrontotemporaldementiadisordersusingensemblemethods AT gousiathahniyath eegsignalanalysisforclassifyingalzheimersandfrontotemporaldementiadisordersusingensemblemethods AT rajathimmarayan eegsignalanalysisforclassifyingalzheimersandfrontotemporaldementiadisordersusingensemblemethods |