Multiscale Entropy Analysis of Electroencephalography During Sleep in Patients With Parkinson Disease

Abstract

Sleep disorders are frequently seen in patients with Parkinson disease (PD), including rapid eye movement (REM) behavior disorder and periodic limb movement disorder. However, knowledge about changes in non-REM sleep in patients with PD is limited. This study explored the characteristics of electroencephalography (EEG) during sleep in patients with PD and non-PD controls. We further conducted multiscale entropy (MSE) analysis to evaluate and compare the complexity of sleep EEG for the 2 groups. There were 9 patients with PD (Hoehn-Yahr stage 1 or 2) and 11 non-PD controls. All participants underwent standard whole-night polysomnography (PSG), which included 23 channels, 6 of which were for EEG. The raw data of the EEG were extracted and subjected to MSE analysis. Patients with PD had a longer sleep onset time and a higher spontaneous EEG arousal index. Sleep stage-specific increased MSE was observed in patients with PD during non-REM sleep. The difference was more marked and significant at higher time scale factors (TSFs). In conclusion, increased biosignal complexity, as revealed by MSE analysis, was found in patients with PD during non-REM sleep at high TSFs. This finding might reflect a compensatory mechanism for early defects in neuronal network control machinery in PD.

Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease

“…In addition, in comparison with other non-invasive brain imaging approaches, EEG irregularity and complexity analyses have been successfully and widely employed and provide a new view to understand physiological processes in both healthy and pathological conditions in AD [1,11,13,[26][27][28]. The MSE-and mvMSE-based methods have been successfully used to characterise biomedical signals to detect different pathological states like epilepsy, schizophrenia, Parkinson's disease, and AD [13,[29][30][31][32].…”

Multi-scale sample entropy as a feature for working memory study

“…[4] used MSE for determining automatic sleep score by analyzing EEG data from C3-A2 channel. Using MSE on sleep EEG of Parkinson patients, Chung et al [5] have found that the complexity of EEG was significantly distinguishable between normal group and Parkinson patients at higher time scale for central electrodes.…”

Performance Analysis of Multiscale Entropy for the Assessment of ECG Signal Quality

“…Multiscale entropy (MSE), a nonlinear analysis measure, has been applied to a variety of biomedical signals [9][10][11][12]. Costa et al firstly proposed and employed MSE to biomedical signals analysis [9,10].…”

“…Costa et al firstly proposed and employed MSE to biomedical signals analysis [9,10]. Chung et al analysed the electroencephalography (EEG) during sleep in patients with Parkinson's disease using MSE [11]. Zhang et al employed MSE to analyse characterizing different patterns of spontaneous electromyogram (EMG) signals [12] and then got the nonlinear features of these patterns.…”