EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques.

Autor:	AlSharabi K; Electrical Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi Arabia., Salamah YB; Electrical Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi Arabia., Aljalal M; Electrical Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi Arabia., Abdurraqeeb AM; Electrical Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi Arabia., Alturki FA; Electrical Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi Arabia.
Jazyk:	angličtina
Zdroj:	Frontiers in human neuroscience [Front Hum Neurosci] 2023 Aug 31; Vol. 17, pp. 1190203. Date of Electronic Publication: 2023 Aug 31 (Print Publication: 2023).
DOI:	10.3389/fnhum.2023.1190203
Abstrakt:	Introduction: Despite 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. Methods: The 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. Results: The 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. Conclusion: The 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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 AlSharabi, Salamah, Aljalal, Abdurraqeeb and Alturki.)
Databáze:	MEDLINE
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