Sleep EEG Characteristics in Young and Elderly Patients with Obstructive Sleep Apnea Syndrome

OBJECTIVE: In the present study, it was hypothesized that the sleep electroencephalogram (EEG) characteristics of young (55 yrs) OSAS patients would differ. METHODS: We analyzed 76 sleep EEG recordings from OSAS patients (young group: n=40, mean age: 24.3±4.9 yrs; elderly group: n=36, mean age: 59.1±4.9 yrs), which were obtained during nocturnal polysomnography. The recordings were assessed via spectral analysis in the delta (0.5–4.5 Hz), theta (4.5–8 Hz), alpha (8–12 Hz), beta (12–32 Hz), slow sigma (11–13 Hz), and fast sigma (13–17 Hz) frequency bands. RESULTS: Apnea Hypopnea Index (AHI) and sleep efficiency (%) did not differ significantly between the two groups (19.8±14.4 vs. 25.9±16.0, p=0.085; 84.4±12.6 vs. 80.9±11.0, p=0.198, respectively). After adjusting for gender, the slow/fast sigma ratio was not significantly correlated with AHI in the elderly group (r=-0.047, p=0.790) but AHI was inversely correlated with the slow/fast sigma ratio in the young group (r=-0.423, p=0.007). A multiple linear regression analysis revealed that a higher AHI was related with a lower slow/fast sigma ratio in the young group (β=-0.392, p=0.028) but not the elderly. CONCLUSION: In the present study, sleep EEG activity differed between young and elderly OSAS patients. The slow/fast sigma ratio was associated with OSAS severity only in young patients, suggesting that young OSAS patients may have a distinctive brain plasticity compared with elderly patients.

Detrended Fluctuation Analysis on Sleep EEG of Healthy Subjects / 수면정신생리

INTRODUCTION: Detrended fluctuation analysis (DFA) is used as a way of studying nonlinearity of EEG. In this study, DFA is applied on sleep EEG of normal subjects to look into its nonlinearity in terms of EEG channels and sleep stages. METHOD: Twelve healthy young subjects (age: 23.8+/-2.5 years old, male:female=7:5) have undergone nocturnal polysomnography (nPSG). EEG from nPSG was classified in terms of its channels and sleep stages and was analyzed by DFA. Scaling exponents (SEs) yielded by DFA were compared using linear mixed model analysis. RESULTS: Scaling exponents (SEs) of sleep EEG were distributed around 1 showing long term temporal correlation and self-similarity. SE of C3 channel was bigger than that of O1 channel. As sleep stage progressed from stage 1 to slow wave sleep, SE increased accordingly. SE of stage REM sleep did not show significant difference when compared with that of stage 1 sleep. CONCLUSION: SEs of Normal sleep EEG showed nonlinear characteristic with scale-free fluctuation, long-range temporal correlation, self-similarity and self-organized criticality. SE from DFA differentiated sleep stages and EEG channels. It can be a useful tool in the research with sleep EEG.