Wavelet Entropy Analysis of Electroencephalogram Signals During Wake and Different Sleep Stages in Patients with Insomnia Disorder

Qian Yang; Lingfeng Liu; Jing Wang; Ying Zhang; Nan Jiang; Meiyun Zhang

doi:10.2147/NSS.S452017

Wavelet Entropy Analysis of Electroencephalogram Signals During Wake and Different Sleep Stages in Patients with Insomnia Disorder

Nat Sci Sleep. 2024 Apr 6:16:347-358. doi: 10.2147/NSS.S452017. eCollection 2024.

Authors

Qian Yang¹, Lingfeng Liu¹, Jing Wang², Ying Zhang², Nan Jiang³, Meiyun Zhang²

Affiliations

¹ Tianjin Union Medical Center, Tianjin Medical University, Tianjin, 300070, People's Republic of China.
² Department of Neurology, Tianjin Union Medical Center, Tianjin, 300121, People's Republic of China.
³ School of Mechanical Engineering, Tianjin University, Tianjin, 300354, People's Republic of China.

Abstract

Objective: To investigate the changes in the wavelet entropy during wake and different sleep stages in patients with insomnia disorder.

Methods: Sixteen patients with insomnia disorder and sixteen normal controls were enrolled. They underwent scale assessment and two consecutive nights of polysomnography (PSG). Wavelet entropy analysis of electroencephalogram (EEG) signals recorded from all participants in the two groups was performed. The changes in the integral wavelet entropy (En) and individual-scale wavelet entropy (En(a)) during wake and different sleep stages in the two groups were observed, and the differences between the two groups were compared.

Results: The insomnia disorder group exhibited lower En during the wake stage, and higher En during the N3 stage compared with the normal control group (all P < 0.001). In terms of En(a), patients with insomnia disorder exhibited lower En(a) in the β and α frequency bands during the wake stage compared with normal controls (β band, P < 0.01; α band, P < 0.001), whereas they showed higher En(a) in the β and α frequency bands during the N3 stage than normal controls (β band, P < 0.001; α band, P < 0.001).

Conclusion: Wavelet entropy can reflect the changes in the complexity of EEG signals during wake and different sleep stages in patients with insomnia disorder, which provides a new method and insights about understanding of pathophysiological mechanisms of insomnia disorder. Wavelet entropy provides an objective indicator for assessing sleep quality.

Keywords: insomnia disorder; polysomnography; sleep stages; wavelet entropy.

Grants and funding

This study was sponsored by Tianjin Applied Basic and Frontier Technology Research Plan (No.14JCYBJC27000); Tianjin Health Research Project (No. TJWJ2022ZD005); Tianjin Union Medical Center Research Project (No.2023YJ026).