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.
Electroencephalography* ; Polysomnography ; Sleep Stages ; Sleep, REM

OBJECTIVE: To realize the characteristics of clinical symptoms and PSG in elderly patients with obstructive sleep apnea hypopnea syndrome (OSAHS). METHOD: The clinical presentations, epworth sleepiness scale (ESS) and polysomnography findings were compared between elderly patients with OSAHS and middle age patients with OSAHS. RESULT: There were no significantly differences in clinical presentations including snoring, apnea and daytime sleepiness complaint between the elderly and middle aged patients with OSAHS, but the incidences of complications such as hypertension and other cardiovascular diseases was significantly higher in elderly patients than those in the middle aged patients (P<0.01). The sleep architecture disturbance was significantly worse in elderly OSAHS patients compared with the middle age patients. The percentages of non-rapid eye movement (NREM) stage I sleep were significantly increased, the rapid eye movement (REM) sleep were significantly decreased in elderly OSAHS patients than those in middle aged group (P<0.01 or P<0.05), but the percentages of awake, NREM stage II sleep and NREM stage II-IV sleep had no significantly difference in the two groups. The apnea hypopnea index (AHI), apnea index (AI), hypopnea index (HI), snoring index, ESS and body mass index (BMI) were significantly decreased, the lowest oxygen saturation (LSO2) and micro-arousal index were significantly increased in elderly OSAHS patients than those in middle aged group (P<0.01 or P<0.05). CONCLUSION The elderly OSAHS patients are less sever than the middle age, but the elderly patients have worse sleep architecture disturbance and more complications such as hypertension and other cardiovascular diseases.
Aged ; Humans ; Middle Aged ; Polysomnography ; Sleep ; Sleep Apnea, Obstructive ; diagnosis ; physiopathology ; Sleep Stages ; Sleep, REM

OBJECTIVES: A few studies have compared REM sleep-dependent obstructive sleep apnea syndrome (REM-OSA) with sleep stage non-dependent apnea syndrome (SND-OSA). Despite that REM-OSA might be more common in women than men, no studies have examined the probable characteristics of women patients with obstructive sleep apnea syndrome (OSAS). This study aimed at finding out the characteristics of REM-OSA in women by comparing it with SND-OSA. METHODS: Fifty-three subjects diagnosed as OSAS (AHI>5; AHI: apnea-hypopnea index) with nocturnal polysomnography at the Center for Sleep and Chronobiology of the Seoul National University Hospital between October 2004 and February 2006 were studied. Of them, 44 subjects with OSAS severity of mild (5REM-OSA were AHI-R/AHI-NR>2 and AHI-NR<15 (AHI-R: AHI during REM sleep, AHI-NR: AHI during non-REM sleep). We compared REM-OSA group with SND-OSA as well as the criteria-determined REM-OSA cases with the visually-determined ones. RESULTS: Among 44 subjects, 28 persons (63.6%) turned out to have REM-OSA by our criteria and 24 persons (54.5%) by visual determination. Statistically significant differences (p<0.05) were found between REM-OSA and SND-OSA groups in AHI, hypopnea index, total sleep time, total wake time, sleep efficiency index, percents of stage 1, 2 and REM sleep, and REM latency. Percent of stage REM sleep (%REM) turned out to have influence on AHI ratio (AHI-R/AHI-NR) (B=0.537, p=0.002). REM-OSA was likely to be diagnosed in milder severity of OSAS (chi-square=13.117, p<0.001) and those with higher %REM (chi-square=11.325, p=0.001). There was no significant difference between the criteria-determined and the visually-determined cases of REM-OSA. CONCLUSION: We suggest that REM-OSA and SND-OSA patients be differentiated in terms of pathophysiology and treatment strategies. Visual determination of REM-OSA might be useful as the screening procedure of REM-OSA. Further studies on women with OSAS and REM-OSA need to be done.
Apnea ; Female ; Humans ; Male ; Mass Screening ; Polysomnography ; Sleep Apnea, Obstructive ; Sleep Stages ; Sleep, REM

Unlike the case of adult obstructive sleep apnea syndrome (OSAS), there was no consistent finding on the changes of sleep architecture in childhood OSAS. Further understanding of the sleep electroencephalogram (EEG) should be needed. Non-linear analysis of EEG is particularly useful in giving us a new perspective and in understanding the brain system. The objective of the current study is to compare the sleep architecture and the scaling exponent (alpha) from detrended fluctuation analysis (DFA) on sleep EEG between OSAS and normal children. Fifteen normal children (8 boys/7 girls, 6.0+/-2.2 years old) and twelve OSAS children (10 boys/2 girls, 6.4+/-3.4 years old) were studied with polysomnography (PSG). Sleep-related variables and OSAS severity indices were obtained. Scaling exponent of DFA were calculated from the EEG channels (C3/A2, C4/A1, O1/A2, and O2/A1), and compared between normal and OSAS children. No difference in sleep architecture was found between OSAS and normal controls except stage 1 sleep (%) and REM sleep latency (min). Stage 1 sleep (%) was significantly higher and REM latency was longer in OSAS group (9.3+/-4.3%, 181.5+/-59.9 min) than in controls (5.6+/-2.8%, 133.5+/-42.0 min). Scaling exponent (alpha) showed that sleep EEG of OSAS children also followed the 'longrange temporal correlation' characteristics. Value of alpha increased as sleep stages increased from stage 1 to stage 4. Value of alpha from C3/A2, C4/A1, O1/A2, O2/A1 were significantly lower in OSAS than in control (1.36+/-0.05 vs. 1.41+/-0.04, 1.37+/-0.04 vs. 1.41+/-0.04, 1.37+/-0.05 vs. 1.41+/-0.05, and 1.36+/-0.07 vs. 1.41+/-0.05, p<0.05). Higher stage 1 sleep (%) in OSAS children was consistent finding with OSAS adults. Lower 'alpha' in OSAS children suggests decrease of self-organized criticality or the decreased piling-up energy of brain system during sleep in OSAS children.
Adult ; Brain ; Child ; Electroencephalography ; Humans ; Polysomnography ; Sleep Apnea, Obstructive ; Sleep Stages ; Sleep, REM

OBJECTIVE: To study the clinical characteristics and relationship between sleep body posture, sleep phase and occurrence in patients with various degree of obstructive sleep apnea-hypopnea syndrome (OSAHS). METHOD: Polysomnography recordings of 100 adults with OSAHS were divided into 3 groups according to AHI: mild with apnea-hypopnea indices (5 < or = AHI < or = 15), moderate (15 < AHI < or = 30) and severe (30 < AHI). The polysomnography data and clinical characteristics were compared between each groups. REM sleep-related OSAHS was defined as REM AHI/NREM AHI > or = 2. Positional OSAHS was defined as supine AHI/non-supine AHI > or = 2. RESULT: 90.91% (20/22) patients with mild and 82.35% (14/17) patients with moderate OSAHS were position dependent, 40.91% (9/22) patients with mild and 23.53% (4/17) patients with moderate OSAHS were REM sleep-related OSAHS. The percentage of REM sleep-related OSAHS and position dependent OSAHS were significantly higher in mild and moderate groups compared with in severe group (P < 0.05, respectively). In both mild and moderate groups, the supine AHI was significantly correlated with AHI (r = 0.491, 0.771, P < 0.05, respectively). In severe groups, the non-supine AHI was significantly correlated with AHI and Lowest oxygen saturation (LSaO2) (r = -0.424, 0.527,P < 0.01, respectively), NREM AHI was significantly correlated with LSaO2 (r = 0.470, P < 0.01). CONCLUSION Body position play significant effects in mild and moderate but not severe OSAHS. Patients with severe OSAHS are less likely to spend time in the supine position and REM compared with patients with mild and moderate OSAHS.
Adult ; Female ; Humans ; Male ; Middle Aged ; Polysomnography ; Posture ; Sleep Apnea, Obstructive ; etiology ; Sleep Stages ; Sleep, REM

BACKGROUND AND OBJECTIVES: Sleep has five different periods manifested by changes in the EEG activity and certain behavioral correlates. It has been proposed that the upper airway mechanics would be influenced by sleep stage. Although several methods have been used to evaluate the regions over which the upper airway collapses during sleep, there were seldom reports about the changes of upper airway according to the sleep stage. The present study was conducted to determine the effect of sleep stage on the upper airway dynamics. MATERIALS AND METHOD: Using electron beam, we studied ten normal subjects who did not have any sleep-disordered breathing. Each patient being monitored with EEG was scanned while sleeping naturally. The images were acquired during light sleep, deep sleep and REM sleep during at least two full respiratory cycles. RESULTS: Upper airway collapse was increased with the progression of sleep, but the level of stenosis was relatively constant throughout the sleep. Sleep stage had differential effects on the upper airway size depending on the investigated site. CONCLUSION: Our data suggest that upper airway mechanics are influenced by each sleep stage. This would indicate that the study of either point of sleep or either site of airway in isolation may not allow a proper insight on the overall upper airway pathophysiology.
Airway Obstruction ; Constriction, Pathologic ; Diagnostic Imaging ; Electroencephalography ; Humans ; Mechanics ; Sleep Apnea Syndromes ; Sleep Stages* ; Sleep, REM

OBJECTIVES.: To investigate the apnea-hypopnea index (AHI) according to the sleep stage in more detail after control of posture. METHODS.: Patients who underwent nocturnal polysomnography between December 2007 and July 2018 were retrospectively evaluated. Inclusion criteria were as follows: age >18 years, sleep efficacy >80%, and patients who underwent polysomnography only in the supine position (100% of the time). Patients were classified into different groups according to the methods: the first, rapid eye movement (REM)-dominant group (AHIREM/AHINREM >2), non-rapid eye movement (NREM)-dominant group (AHINREM/AHIREM >2), and non-dominant group; and the second, light sleep group (AHIN1N2>AHISWS) and slow wave sleep (SWS) group (AHISWS>AHIN1N2). RESULTS.: A total of 234 patients (mean age, 47.4±13.9 years) were included in the study. There were 108 patients (46.2%) in the REM-dominant group, 88 (37.6%) in the non-dominant group, and 38 (16.2%) in the NREM-dominant group. The AHI was significantly higher in the NREM-dominant group than in the REM-dominant group (32.9±22.9 events/hr vs. 18.3±9.5 events/hr, respectively). There were improvements in the AHI from stage 1 to SWS in NREM sleep with the highest level in REM sleep. A higher AHISWS than AHIN1N2 was found in 16 of 234 patients (6.8%); however, there were no significant predictors of these unexpected results except AHI. CONCLUSION.: Our results demonstrated the highest AHI during REM sleep stage in total participants after control of posture. However, there were 16.2% of patients showed NREM-dominant pattern (AHINREM/AHIREM >2) and 6.8% of patients showed higher AHISWS than AHIN1N2. Therefore, each group might have a different pathophysiology of obstructive sleep apnea (OSA), and we need to consider this point when we treat the patients with OSA.
Eye Movements ; Humans ; Polysomnography ; Posture ; Retrospective Studies ; Sleep Apnea, Obstructive ; Sleep Stages ; Sleep, REM ; Supine Position

INTRODUCTION: In this study, we compared sleep structure, EEG characteristic of pediatric obstructive sleep apnea (OSA) and normal controls which were matched in sex and age. METHODS: Fifteen children (male:female=4:11) who complained snoring and were suspected to have sleep apnea and their age and sex matched normal controls (male:female=5:10) have been done nocturnal polysomnography (NPSG). Sleep parameters, sleep apnea variables and relative spectral components of EEG from NPSG have been compared between both groups. RESULTS: Pediatric OSA group were distinguished from normal controls in terms of apnea index, respiratory disturbance index and nadir of oxyhemoglobulin desaturation. Pediatric OSA group showed increased percent of sleep stage 1, decreased rapid eye movement sleep percent and increased delta power in O1 EEG channel. However other sleep parameters and spectral powers were not different between two groups. CONCLUSION: In pediatric OSA group, sleep structure parameter disruption may be not prominent as the previous studies for adult OSA group because of including mild OSA data in diagnostic criteria. In addition, EEG changes might not be distinct due to low arousal index compared to adult OSA patients. We can observe general characteristics and particularity of pediatric OSA through this study.
Adult ; Apnea ; Arousal ; Child ; Electroencephalography ; Humans ; Polysomnography ; Sleep Apnea Syndromes ; Sleep Apnea, Obstructive ; Sleep Stages ; Sleep, REM ; Snoring

Sleep is a highly organized and complicated state that is fundamental to life. We have an absolute need to sleep during about one-third of our lives. There are two types of sleep, non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. NREM sleep is divided into stages 1, 2, and 3 which is representing a degree of relative depth in sleep. Each sleep stage shows unique features including some variations in electroencephalographic waves, eye movements, and muscle tone. Although sleep pattern changes are associated with aging, how sleep physiology and sleep patterns change over an individual's life span is not well-defined. Circadian rhythms, which are the daily rhythms in physiology and behavior, regulate the sleep-wake cycle. Comprehensive understanding of normal sleep physiology should be very important to better understand not only the effects of sleep related diseases but also the impacts of pathological sleep on various diseases of other systemic organs. This review aims to enhance knowledge focused on normal sleep physiology and its regulation.
Aging ; Circadian Rhythm ; Eye Movements ; Muscles ; Neurobiology ; Physiology* ; Sleep Stages ; Sleep, REM

OBJECTIVES: Soft-computing techniques are commonly used to detect medical phenomena and to help with clinical diagnoses and treatment. The purpose of this paper is to analyze the single electroencephalography (EEG) signal with the chaotic methods in order to identify the sleep stages. METHODS: Data acquisition (polysomnography) was performed on four healthy young adults (all males with a mean age of 27.5 years). The evaluated algorithm was designed with a correlation dimension and Lyapunov's exponent using a single EEG signal that detects differences in chaotic characteristics. RESULTS: The change of the correlation dimension and the largest Lyapunov exponent over the whole night sleep EEG was performed. The results show that the correlation dimension and largest Lyapunov exponent decreased from light sleep to deep sleep and they increased during the rapid eye movement stage. CONCLUSIONS: These results suggest that chaotic analysis may be a useful adjunct to linear (spectral) analysis for identifying sleep stages. The single EEG based nonlinear analysis is suitable for u-healthcare applications for monitoring sleep.
Delivery of Health Care ; Electroencephalography ; Humans ; Light ; Male ; Regression Analysis ; Sleep Stages ; Sleep, REM ; Young Adult