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1- Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran.
Introduction and Aims: Alzheimer’s disease is the most prevalent neurodegenerative disorder and a type of dementia. 80% of dementia in older adults is because of Alzheimer’s disease. According to multiple research articles, Alzheimer's has several changes in EEG signals such as slowing of rhythms, reduction in complexity and reduction in functional associations, and disordered functional communication between different areas of the brain. This research focuses on the entropy parameter.
Materials and Methods: In this study, the keywords Entropy, EEG, and Alzheimer's were used. In the initial search, 102 research articles were found. In the first stage, after investigating the abstract of 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 for comparing and some of them have used more than one database. So 25 entropy measures were considered in this Meta-Analysis. We used the standardized mean difference (SMD) for finding the effect size to compare the effects of Alzheimer’s disease on the entropy of the EEG signal with healthy people. Funnel plots were used to investigate the bias of Meta-Analysis.
Conclusion: According to the articles, results and funnel plots of this Meta-Analysis, entropy seems to be a good benchmark for comparing the EEG signals in healthy people and people who have Alzheimer’s disease. It can be concluded that Alzheimer’s disease 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

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