Deep Learning Model for Prediction of Dementia Severity based on EEG Signals
This study aimed to determine variations in the electroencephalograms (EEGs) of 15 individuals who were diagnosed with mild cognitive impairment (MCI) following stroke, 5 individuals suffering from vascular dementia (VD) and 15 healthy normal control (NC) individuals who performed a working memory...
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Al-Khwarizmi College of Engineering – University of Baghdad
2024-12-01
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| Series: | Al-Khawarizmi Engineering Journal |
| Online Access: | https://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/938 |
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| author | Noor Kamal Al-Qazzaz Sawal Hamid Bin Mohd Ali Siti Anom Ahmad |
| author_facet | Noor Kamal Al-Qazzaz Sawal Hamid Bin Mohd Ali Siti Anom Ahmad |
| author_sort | Noor Kamal Al-Qazzaz |
| collection | DOAJ |
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This study aimed to determine variations in the electroencephalograms (EEGs) of 15 individuals who were diagnosed with mild cognitive impairment (MCI) following stroke, 5 individuals suffering from vascular dementia (VD) and 15 healthy normal control (NC) individuals who performed a working memory task. Conventional filters including notch and bandpass filters were utilised to remove noise from the EEG data. The proposed method comprises computing the estimates of the attention entropy (AttEn), bubble entropy (BubbEn) and symbolic dynamic entropy (SyDyEn) of univariate data sequence features. The long short-term memory (LSTM) deep learning neural network was used to automatically classify dementia severity through noninvasive EEG-based recordings. The LSTM classification result with AttEn exceeds an average of 88.9% than BubbEn and SyDyEn, with classification results of 69.2% and 77.7%, respectively. The analysis of the brain EEG-based dementia severity dataset suggests that AttEn could potentially serve as a biomarker for detecting dementia severity. AttEn can capture relevant patterns and features in the EEG data and may be indicative of the severity of dementia with LSTM RNN to differentiate patients with VD, patients with MCI and NC individuals.
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| format | Article |
| id | doaj-art-1005f631de8b45a3bb43f16b5bb0fa3f |
| institution | DOAJ |
| issn | 1818-1171 2312-0789 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Al-Khwarizmi College of Engineering – University of Baghdad |
| record_format | Article |
| series | Al-Khawarizmi Engineering Journal |
| spelling | doaj-art-1005f631de8b45a3bb43f16b5bb0fa3f2025-08-20T02:51:00ZengAl-Khwarizmi College of Engineering – University of BaghdadAl-Khawarizmi Engineering Journal1818-11712312-07892024-12-0120410.22153/kej.2024.08.002Deep Learning Model for Prediction of Dementia Severity based on EEG SignalsNoor Kamal Al-Qazzaz 0Sawal Hamid Bin Mohd Ali 1Siti Anom Ahmad 2Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, IraqDepartment of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor 43600, Malaysia4Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia This study aimed to determine variations in the electroencephalograms (EEGs) of 15 individuals who were diagnosed with mild cognitive impairment (MCI) following stroke, 5 individuals suffering from vascular dementia (VD) and 15 healthy normal control (NC) individuals who performed a working memory task. Conventional filters including notch and bandpass filters were utilised to remove noise from the EEG data. The proposed method comprises computing the estimates of the attention entropy (AttEn), bubble entropy (BubbEn) and symbolic dynamic entropy (SyDyEn) of univariate data sequence features. The long short-term memory (LSTM) deep learning neural network was used to automatically classify dementia severity through noninvasive EEG-based recordings. The LSTM classification result with AttEn exceeds an average of 88.9% than BubbEn and SyDyEn, with classification results of 69.2% and 77.7%, respectively. The analysis of the brain EEG-based dementia severity dataset suggests that AttEn could potentially serve as a biomarker for detecting dementia severity. AttEn can capture relevant patterns and features in the EEG data and may be indicative of the severity of dementia with LSTM RNN to differentiate patients with VD, patients with MCI and NC individuals. https://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/938 |
| spellingShingle | Noor Kamal Al-Qazzaz Sawal Hamid Bin Mohd Ali Siti Anom Ahmad Deep Learning Model for Prediction of Dementia Severity based on EEG Signals Al-Khawarizmi Engineering Journal |
| title | Deep Learning Model for Prediction of Dementia Severity based on EEG Signals |
| title_full | Deep Learning Model for Prediction of Dementia Severity based on EEG Signals |
| title_fullStr | Deep Learning Model for Prediction of Dementia Severity based on EEG Signals |
| title_full_unstemmed | Deep Learning Model for Prediction of Dementia Severity based on EEG Signals |
| title_short | Deep Learning Model for Prediction of Dementia Severity based on EEG Signals |
| title_sort | deep learning model for prediction of dementia severity based on eeg signals |
| url | https://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/938 |
| work_keys_str_mv | AT noorkamalalqazzaz deeplearningmodelforpredictionofdementiaseveritybasedoneegsignals AT sawalhamidbinmohdali deeplearningmodelforpredictionofdementiaseveritybasedoneegsignals AT sitianomahmad deeplearningmodelforpredictionofdementiaseveritybasedoneegsignals |