Obstructive Sleep Apnea Alters Sleep Stage Transition Dynamics

Enhanced characterization of sleep architecture, compared with routine polysomnographic metrics such as stage percentages and sleep efficiency, may improve the predictive phenotyping of fragmented sleep. One approach involves using stage transition analysis to characterize sleep continuity.We analyzed hypnograms from Sleep Heart Health Study (SHHS) participants using the following stage designations: wake after sleep onset (WASO), non-rapid eye movement (NREM) sleep, and REM sleep. We show that individual patient hypnograms contain insufficient number of bouts to adequately describe the transition kinetics, necessitating pooling of data. We compared a control group of individuals free of medications, obstructive sleep apnea (OSA), medical co-morbidities, or sleepiness (n = 374) with mild (n = 496) or severe OSA (n = 338). WASO, REM sleep, and NREM sleep bout durations exhibited multi-exponential temporal dynamics. The presence of OSA accelerated the "decay" rate of NREM and REM sleep bouts, resulting in instability manifesting as shorter bouts and increased number of stage transitions. For WASO bouts, previously attributed to a power law process, a multi-exponential decay described the data well. Simulations demonstrated that a multi-exponential process can mimic a power law distribution.OSA alters sleep architecture dynamics by decreasing the temporal stability of NREM and REM sleep bouts. Multi-exponential fitting is superior to routine mono-exponential fitting, and may thus provide improved predictive metrics of sleep continuity. However, because a single night of sleep contains insufficient transitions to characterize these dynamics, extended monitoring of sleep, probably at home, would be necessary for individualized clinical application

Obstructive Sleep Apnea Alters Sleep Stage Transition Dynamics

Introduction: Enhanced characterization of sleep architecture, compared with routine polysomnographic metrics such as stage percentages and sleep efficiency, may improve the predictive phenotyping of fragmented sleep. One approach involves using stage transition analysis to characterize sleep continuity. Methods and Principal Findings: We analyzed hypnograms from Sleep Heart Health Study (SHHS) participants using the following stage designations: wake after sleep onset (WASO), non-rapid eye movement (NREM) sleep, and REM sleep. We show that individual patient hypnograms contain insufficient number of bouts to adequately describe the transition kinetics, necessitating pooling of data. We compared a control group of individuals free of medications, obstructive sleep apnea (OSA), medical co-morbidities, or sleepiness (n = 374) with mild (n = 496) or severe OSA (n = 338). WASO, REM sleep, and NREM sleep bout durations exhibited multi-exponential temporal dynamics. The presence of OSA accelerated the “decay” rate of NREM and REM sleep bouts, resulting in instability manifesting as shorter bouts and increased number of stage transitions. For WASO bouts, previously attributed to a power law process, a multi-exponential decay described the data well. Simulations demonstrated that a multi-exponential process can mimic a power law distribution. Conclusion and Significance: OSA alters sleep architecture dynamics by decreasing the temporal stability of NREM and REM sleep bouts. Multi-exponential fitting is superior to routine mono-exponential fitting, and may thus provide improved predictive metrics of sleep continuity. However, because a single night of sleep contains insufficient transitions to characterize these dynamics, extended monitoring of sleep, probably at home, would be necessary for individualized clinical application

Preserved Sleep Quality under Simulated Altitude as Assessed by Electroencephalography Power and the Electrocardiogram-Derived Sleep Spectrogram

Background and Objective Simulated altitude as a model for hypoxia has shown inconsistent results in terms of impaired cognition. We hypothesized that preserved periods of stable sleep even under hypoxia could explain stable cognitive function. Delta spectral power on electroencephalography during stable sleep as well as high frequency coupling on the electrocardiogram-based spectrogram was adopted as measures of sleep quality. Methods Eleven healthy, non-smoking subjects (7 men, 27 ± 1.5 years) were exposed to 9 hours of continuous hypoxia for 13 consecutive nights. Polysomnography was done at baseline and during 3 time points, at night 3, 7, and 14. In each study, delta spectral power was obtained during stable N2 and N3 sleep. Stable sleep was defined when there was no significant fragmentation in electroencephalography and fluctuation in electromyography and cardiorespiratory signals. The time threshold was 2 or 5 continuous minutes for N2 and 2 minutes for N3. The amount of high frequency coupling for the sleep period on the electrocardiogram-based spectrogram was computed. Randomized block ANOVA was used with electroencephalography delta power and high frequency coupling as dependent variables with post hoc Tukey test. Results Delta spectral power during stable sleep was not significantly different across the entire hypoxic exposures (p = 0.98 for N2; p = 0.32 for N3). High frequency coupling was different between pre-exposure and mid-exposure (night 7; 52.5 ± 23.6% vs. 39.0 ± 16.7%, p = 0.02) but returned to the baseline level at the post-exposure (night 14; 45.4 ± 18.2%, p = 0.39). Conclusions Both preservation of the proportion of stable sleep and unchanged delta power during these periods may help explain maintained cognition in conditions of chronic nocturnal hypoxic exposures

Gender- and age-related differences in heart rate dynamics: Are women more complex than men?

AbstractObjectives. This study aimed to quantify the complex dynamics of beat-to-beat sinus rhythm heart rate fluctuations and to determine their differences as a function of gender and age.Background. Recently, measures of heart rate variability and the nonlinear “complexity” of heart rate dynamics have been used as indicators of cardiovascular health. Because women have lower cardiovascular risk and greater longevity than men, we postulated that there are important gender-related differences in beat-to-beat heart rate dynamics.Methods. We analyzed heart rate dynamics during 8-min segments of continuous electrocardiographic recording in healthy young (20 to 39 years old), middle-aged (40 to 64 years old) and elderly (65 to 90 years old) men (n = 40) and women (n = 27) while they performed spontaneous and metronomic (15 breaths/ min) breathing. Relatively high (0.15 to 0.40 Hz) and low (0.01 to 0.15 Hz) frequency components of heart rate variability were computed using spectral analysis. The overall “complexity” of each heart rate time series was quantified by its approximate entropy, a measure of regularity derived from nonlinear dynamics (“chaos” theory).Results. Mean heart rate did not differ between the age groups or genders. High frequency heart rate power and the high/low frequency power ratio decreased with age in both men and women (p < 0.05). The high/low frequency power ratio during spontaneous and metronomic breathing was greater in women than men (p < 0.05). Heart rate approximate entropy decreased with age and was higher in women than men (p < 0.05).Conclusions. High frequency heart rate spectral power (associated with parasympathetic activity) and the overall complexity of heart rate dynamics are higher in women than men. These complementary findings indicate the need to account for genderas well as age-related differences in heart rate dynamics. Whether these gender differences are related to lower cardiovascular disease risk and greater longevity in women requires further study

Decreased neuroautonomic complexity in men during an acute major depressive episode: Analysis of heart rate dynamics

Major depression affects multiple physiologic systems. Therefore, analysis of signals that reflect integrated function may be useful in probing dynamical changes in this syndrome. Increasing evidence supports the conceptual framework that complex variability is a marker of healthy, adaptive control mechanisms and that dynamical complexity decreases with aging and disease. We tested the hypothesis that heart rate (HR) dynamics in non-medicated, young to middle-aged males during an acute major depressive episode would exhibit lower complexity compared with healthy counterparts. We analyzed HR time series, a neuroautonomically regulated signal, during sleep, using the multiscale entropy method. Our results show that the complexity of the HR dynamics is significantly lower for depressed than for non-depressed subjects for the entire night (Po0.02) and combined sleep stages 1 and 2 (P<0.02). These findings raise the possibility of using the complexity of physiologic signals as the basis of novel dynamical biomarkers of depression. © 2011 Macmillan Publishers Limited.SCOPUS: ar.jinfo:eu-repo/semantics/publishe