Basic and Clinical Neuroscience Journal
مجله علوم اعصاب پایه و بالینی
BCN
Medical Sciences
http://bcn.iums.ac.ir
137
journal137
2008-126X
2228-7442
10.32598/bcn
en
jalali
1400
12
1
gregorian
2022
3
1
13
2
online
1
fulltext
en
Assessing the Effects of Alzheimer Disease on EEG Signals Using the Entropy Measure: A Meta-analysis
Computational Neuroscience
Computational Neuroscience
Original
Original
<div style="text-align: justify;"><strong>Introduction</strong>: 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.<br>
<strong>Methods</strong>: 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.<br>
<strong>Results</strong>: According to the articles, entropy seems to be a good benchmark for comparing the EEG signals between healthy people and AD people. <br>
<strong>Conclusion</strong>: It can be concluded that AD can significantly affect EEG signals and reduce the entropy of EEG signals.</div>
EEG Signal, Entropy, Alzheimer disease, Meta-analysis
153
164
http://bcn.iums.ac.ir/browse.php?a_code=A-10-1144-3&slc_lang=en&sid=1
Hajar
Ahmadieh
h.ahmadieh68@gmail.com
13700319475328460041161
13700319475328460041161
No
Department of Biomedical Engineering, Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran.
Farnaz
Ghassemi
ghassemi@aut.ac.ir
13700319475328460041162
13700319475328460041162
Yes
Department of Biomedical Engineering, Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran.