ArticleNeuropsychiatric disease and treatment2026
Wavelet Entropy Analysis of EEG Signals During Wake and Sleep in Patients with Alzheimer's Disease: A Pilot Study.
Article in Neuropsychiatric disease and treatment, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: Our primary objective is to delve into the wavelet entropy of EEG during wake and sleep in patients with Alzheimer's disease(AD).This is a pilot study aimed at exploring the potential of wavelet entropy as an indicator of EEG complexity in AD patients. Patients and Methods: This study enrolled 30 participants (15 AD patients vs. 15 age-/sex-matched healthy controls). Wavelet entropy analysis was conducted on the electroencephalogram (EEG) signals recorded from all participants across the two groups. A comparative analysis was undertaken between the integral wavelet entropy (En) and individual-scale wavelet entropy (En(a)) during wakefulness and distinct sleep stages in the two patient groups. Results: Compared with the healthy control group, the entropy of AD group was significantly lower in wakefulness and significantly higher in N3 stage (all P < 0.001). AD patients demonstrated lower En(a) in the β and α frequency bands during wakefulness, compared to the healthy controls (all P <0.001). Conversely, during N3 stage, these patients displayed higher En(a) values across β, α, and θ frequency bands compared to the control cohort (all P <0.001). Conclusion: Wavelet entropy can be used as a reliable indicator of the complexity of EEG signals during waking and different sleep stages in patients with AD. This provides a new insight into the pathophysiological mechanisms of dementia.Due to the limited sample size, larger-scale studies are needed in the future to validate these findings.
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