Assessing the Effects of Alzheimer Disease on EEG Signals Using the Entropy Measure: A Meta-analysis.

Area of Science:

• Neuroscience
• Medical Informatics

Background:

• Alzheimer's Disease (AD) is the leading cause of dementia, affecting approximately 80% of older adults.
• AD is linked to alterations in EEG signals, including slower rhythms, reduced complexity, and impaired brain communication.

Purpose of the Study:

• To investigate the effect of Alzheimer's Disease on EEG signal entropy.
• To determine if EEG entropy can serve as a benchmark for differentiating AD patients from healthy individuals.

Main Methods:

• A meta-analysis was conducted using 18 articles, analyzing 25 different entropy measures of EEG signals.
• Keywords used for the search included "Entropy," "EEG," and "Alzheimer."
• Standardized Mean Difference (SMD) was used to calculate effect size, and funnel plots assessed meta-analysis bias.

Main Results:

• The meta-analysis revealed that Alzheimer's Disease significantly affects EEG signals.
• A reduction in EEG signal entropy was consistently observed in individuals with AD compared to healthy controls.

Conclusions:

• EEG entropy is a valuable biomarker for detecting the impact of Alzheimer's Disease on brain activity.
• The findings support the use of EEG entropy measures for distinguishing between healthy individuals and those with AD.