Wavelet Entropy Analysis of EEG Signals During Wake and Sleep in Patients with Alzheimer’s Disease: A Pilot Study

Yujiao Tong,1,2,* Guanqun Hu,1,* Lingfeng Liu,3 Ying Zhang,1 Nan Jiang,4 Meiyun Zhang1,2 1Department of Neurology,Tianjin Union Medical Center, The First Affiliated Hospital of Nankai University, Tianjin, 300121, People’s Republic of China; 2Tianjin Medical University, Tianjin, 300070, People’s Republic of China; 3Department of Outpatient,Air Force Hospital of the Central Theater Command of the People’s Liberation Army of China, Shanxi, 037006, People’s Republic of China; 4School of Mechanical Engineering, Tianjin University, Tianjin, People’s Republic of China, 300354*These authors contributed equally to this workCorrespondence: Meiyun Zhang, Department of Neurology, Tianjin Union Medical Center, The First Affiliated Hospital of Nankai University, Tianjin, 300121, People’s Republic of China, Email [email protected]: 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.Keywords: dementia, sleep stages, brain electrophysiology, wavelet entropy, cognitive function