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Updated: Aug 20, 2025
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
Published on: June 27, 2013
Assessing the Effects of Alzheimer Disease on EEG Signals Using the Entropy Measure: A Meta-analysis
Hajar Ahmadieh1, Farnaz Ghassemi1
1Department 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.
Highlights:
Our primary question addressed in this study is "Can Alzheimer's Disease significantly affect EEG signals or not?" This paper is the first Meta-Analysis study that reveals the effects of Alzheimer's Disease on EEG signals and the caused reduction in the complexity of the EEG signal. 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.
Plain Language Summary:
Alzheimer's Disease is one of the most prevalent neurodegenerative disorder which can affect EEG signals. This study is the first Meta-Analysis in this regard and the results confirm that Alzheimer's Disease reduces the complexity of the EEG signals. We used 25 entropy measures applied in 18 articles. The materials in this Meta-Analysis are 1-SMD for finding the effect size and 2- Funnel plot for investigating the bias of Meta-Analysis.
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