EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques
IntroductionDespite the existence of numerous clinical techniques for identifying neurological brain disorders in their early stages, Electroencephalogram (EEG) data shows great promise as a means of detecting Alzheimer's disease (AD) at an early stage. The main goal of this research is to crea...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2023.1190203/full |
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| author | Khalil AlSharabi Yasser Bin Salamah Majid Aljalal Akram M. Abdurraqeeb Fahd A. Alturki |
| author_facet | Khalil AlSharabi Yasser Bin Salamah Majid Aljalal Akram M. Abdurraqeeb Fahd A. Alturki |
| author_sort | Khalil AlSharabi |
| collection | DOAJ |
| description | IntroductionDespite the existence of numerous clinical techniques for identifying neurological brain disorders in their early stages, Electroencephalogram (EEG) data shows great promise as a means of detecting Alzheimer's disease (AD) at an early stage. The main goal of this research is to create a reliable and accurate clinical decision support system leveraging EEG signal processing to detect AD in its initial phases.MethodsThe research utilized a dataset consisting of 35 neurotypical individuals, 31 patients with mild AD, and 22 patients with moderate AD. Data were collected while participants were at rest. To extract features from the EEG signals, a band-pass filter was applied to the dataset and the Empirical Mode Decomposition (EMD) technique was employed to decompose the filtered signals. The EMD technique was then leveraged to generate feature vectors by combining multiple signal features, thereby enhancing diagnostic performance. Various artificial intelligence approaches were also explored and compared to identify features of the extracted EEG signals distinguishing mild AD, moderate AD, and neurotypical cases. The performance of the classifiers was evaluated using k-fold cross-validation and leave-one-subject-out (LOSO) cross-validation methods.ResultsThe results of this study provided valuable insights into potential avenues for the early diagnosis of AD. The performance of the various offered methodologies has been compared and evaluated by computing the overall diagnosis precision, recall, and accuracy. The proposed methodologies achieved a maximum classification accuracy of 99.9 and 94.8% for k-fold and LOSO cross-validation techniques, respectively.ConclusionThe study aims to assess and compare different proposed methodologies and determine the most effective combination approach for the early detection of AD. Our research findings strongly suggest that the proposed diagnostic support technique is a highly promising supplementary tool for discovering prospective diagnostic biomarkers that can greatly aid in the early clinical diagnosis of AD. |
| first_indexed | 2024-03-12T11:43:05Z |
| format | Article |
| id | doaj.art-e9b1f891790d4508a7b0b0fe0342e604 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-03-12T11:43:05Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-e9b1f891790d4508a7b0b0fe0342e6042023-08-31T12:50:42ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612023-08-011710.3389/fnhum.2023.11902031190203EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniquesKhalil AlSharabiYasser Bin SalamahMajid AljalalAkram M. AbdurraqeebFahd A. AlturkiIntroductionDespite the existence of numerous clinical techniques for identifying neurological brain disorders in their early stages, Electroencephalogram (EEG) data shows great promise as a means of detecting Alzheimer's disease (AD) at an early stage. The main goal of this research is to create a reliable and accurate clinical decision support system leveraging EEG signal processing to detect AD in its initial phases.MethodsThe research utilized a dataset consisting of 35 neurotypical individuals, 31 patients with mild AD, and 22 patients with moderate AD. Data were collected while participants were at rest. To extract features from the EEG signals, a band-pass filter was applied to the dataset and the Empirical Mode Decomposition (EMD) technique was employed to decompose the filtered signals. The EMD technique was then leveraged to generate feature vectors by combining multiple signal features, thereby enhancing diagnostic performance. Various artificial intelligence approaches were also explored and compared to identify features of the extracted EEG signals distinguishing mild AD, moderate AD, and neurotypical cases. The performance of the classifiers was evaluated using k-fold cross-validation and leave-one-subject-out (LOSO) cross-validation methods.ResultsThe results of this study provided valuable insights into potential avenues for the early diagnosis of AD. The performance of the various offered methodologies has been compared and evaluated by computing the overall diagnosis precision, recall, and accuracy. The proposed methodologies achieved a maximum classification accuracy of 99.9 and 94.8% for k-fold and LOSO cross-validation techniques, respectively.ConclusionThe study aims to assess and compare different proposed methodologies and determine the most effective combination approach for the early detection of AD. Our research findings strongly suggest that the proposed diagnostic support technique is a highly promising supplementary tool for discovering prospective diagnostic biomarkers that can greatly aid in the early clinical diagnosis of AD.https://www.frontiersin.org/articles/10.3389/fnhum.2023.1190203/fullAlzheimer's diseaseartificial neural networkconvolutional neural networkdeep learningempirical mode decompositionK-fold |
| spellingShingle | Khalil AlSharabi Yasser Bin Salamah Majid Aljalal Akram M. Abdurraqeeb Fahd A. Alturki EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques Frontiers in Human Neuroscience Alzheimer's disease artificial neural network convolutional neural network deep learning empirical mode decomposition K-fold |
| title | EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques |
| title_full | EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques |
| title_fullStr | EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques |
| title_full_unstemmed | EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques |
| title_short | EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques |
| title_sort | eeg based clinical decision support system for alzheimer s disorders diagnosis using emd and deep learning techniques |
| topic | Alzheimer's disease artificial neural network convolutional neural network deep learning empirical mode decomposition K-fold |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2023.1190203/full |
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