Neuropsychiatric disease and treatment · 2026
Wavelet Entropy Analysis of EEG Signals During Wake and Sleep in Patients with Alzheimer's Disease: A Pilot Study.
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Tong Yujiao, Hu Guanqun, Liu Lingfeng, Zhang Ying, Jiang Nan, Zhang Meiyun
Аннотация
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.
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.