Volume 13, Issue 2 (March & April 2022)                   BCN 2022, 13(2): 153-164 | Back to browse issues page

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Ahmadieh H, Ghassemi F. Assessing the Effects of Alzheimer Disease on EEG Signals Using the Entropy Measure: A Meta-analysis. BCN 2022; 13 (2) :153-164
URL: http://bcn.iums.ac.ir/article-1-1869-en.html
1- Department of Biomedical Engineering, Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran.
Introduction: Alzheimer disease (AD) is the most prevalent neurodegenerative disorder and a type of dementia. About 80% of dementia in older adults is due to AD. According to multiple research articles, AD is associated with several changes in EEG signals, such as slow rhythms, reduction in complexity and functional associations, and disordered functional communication between different brain areas. This research focuses on the entropy parameter.
Methods: In this study, the keywords “Entropy,” “EEG,” and “Alzheimer” were used. In the initial search, 102 articles were found. In the first stage, after investigating the Abstracts of the articles, the number of them was reduced to 62, and upon further review of the remaining articles, the number of articles was reduced to 18. Some papers have used more than one entropy of EEG signals to compare, and some used more than one database. So, 25 entropy measures were considered in this meta-analysis. We used the Standardized Mean Difference (SMD) to find the effect size and compare the effects of AD on the entropy of the EEG signal in healthy people. Funnel plots were used to investigate the bias of meta-analysis.
Results: According to the articles, entropy seems to be a good benchmark for comparing the EEG signals between healthy people and AD people. 
Conclusion: It can be concluded that AD can significantly affect EEG signals and reduce the entropy of EEG signals.
Type of Study: Original | Subject: Computational Neuroscience
Received: 2020/07/23 | Accepted: 2020/10/3 | Published: 2022/03/1

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