Nonlinear behavior of human respiratory movement during different sleep stages

To investigate the nonlinear properties of respiratory movement during different sleep stages, we applied an algorithm proposed by Grassberger and Procaccia to calculate the correlation dimension in rapid eye movement and non-rapid eye movement sleep. We also tested for nonlinearity in respiratory movement by comparing the correlation dimension for the original data with that for surrogate data. The study population included eight healthy volunteers. We recorded respiratory movement and the sleep electroencephalogram for 8 h. The correlation dimension for respiratory movement was 3.28+/-0.19 (mean +/- SD) during rapid eye movement sleep, 2.31+/-0.21 during light sleep (stage I) and 1.64+/-0.25 during deep slow-wave sleep (stage IV). Thus, the correlation dimension differed significantly by sleep stage (p < 0.001): it was least during stage IV sleep and greatest during REM. The correlation dimension for the original data also differed from that for surrogate data, confirming nonlinearity in original data. The results suggest that the nonlinear dynamics of respiratory movement in sleep changes with sleep stage, presumably due to the information processing by the cerebral cortex. The increased correlation dimension for respiratory movement in REM sleep may be related to increased cortical information processing associated with dreaming.     

Effects of head cooling on human sleep stages and body temperature

The purpose of this study was to confirm the effect of head cooling on human sleep stages and body temperature. Nine healthy male volunteers with a mean age of 25 +/- 3.77 years served as subjects. The experiments were carried out under three different sets of conditions: 26 degrees C, relative humidity (RH) 50% (26/50); 32 degrees C, RH 80% (32/80); and 32 degrees C RH 80% with the use of a cooling pillow (32/80 HC). The subjects slept from 2300 hours to 0700 hours with a cotton blanket, wearing short-sleeved pyjamas and shorts on a bed, which was covered with a sheet. Electroencephalograms, electro-ouclogram, and mental electromyelograms were recorded through the night. Rectal temperature (Tre) and skin temperature (Tsk) were measured continuously. Whole-body sweat and the tympanic temperature (Tty) were measured before and after sleep. Wakefulness significantly increased at 32/80 than at 26/50; however, no significant difference was observed between 32/80 HC and 26/50. Tre and mean Tsk were higher both at 32/80 and 32/80 HC than at 26/50. The whole-body sweat loss was significantly greater and Tty in the morning was higher at 32/80 than 32/80 HC and 26/50. These results suggest that head cooling during sleep may help to decrease the whole-body sweat rate during sleep under humid heat conditions.     

An optimal monitor of the electroencephalographic sigma sleep state

A model has been proposed for a Markovjumping sleep depth that modulates a white-noise driven structure generating the sigma rhythm in the electroencephalogram. The corresponding maximum likelihood monitor, that continuously detects the current sleep stage from the observed electroencephalogram, has been derived and implemented. Simulations show high detection performances.     

Nocturnal variations in human lower leg subcutaneous blood flow related to sleep stages.

Nocturnal subcutaneous adipose tissue blood flow rate was measured in the lower legs of 10 normal human subjects together with systemic arterial blood pressure, heart rate, and registration of sleep stages under ambulatory conditions. The 133Xe washout technique, portable CdTe(Cl) detectors, and a portable data storage unit were used for measurement of blood flow rates. The sleep recordings were performed with a portable computerized sleep analysis system. In accordance with the results of previous studies, a hyperemic blood flow rate phase (mean increase 140%) for 100 min was observed approximately 60 min after the subjects went to bed. The moment of onset of the hyperemic phase was closely related to the moment of onset of the first episode of deep sleep (stages 3 and 4). There was a significant (P < 0.01) overrepresentation of deep sleep in the hyperemic phase compared with adjacent phases, and rapid-eye-movement sleep predominantly occurred in the latter part of the night, when the subcutaneous blood flow rate was stable. The results of the present study are in accordance with current theories of the interrelationship between the thermoregulatory and the arousal state control systems and, thus, might suggest that the nightly subcutaneous hyperemia represents a thermoregulatory effector mechanism.     

Optimality principles for model-based prediction of human gait

Although humans have a large repertoire of potential movements, gait patterns tend to be stereotypical and appear to be selected according to optimality principles such as minimal energy. When applied to dynamic musculoskeletal models such optimality principles might be used to predict how a patient's gait adapts to mechanical interventions such as prosthetic devices or surgery. In this paper we study the effects of different performance criteria on predicted gait patterns using a 2D musculoskeletal model. The associated optimal control problem for a family of different cost functions was solved utilizing the direct collocation method. It was found that fatigue-like cost functions produced realistic gait, with stance phase knee flexion, as opposed to energy-related cost functions which avoided knee flexion during the stance phase. We conclude that fatigue minimization may be one of the primary optimality principles governing human gait.     

Quantification of ECoG stages of sleep in the bottlenose dolphin

Single-hemisphere low-wave sleep was shown to be the dominant form of natural sleep according to quantitative analysis of duration of ECoG sleep phases. Combined variants of bilateral and unilateral ECoG synchronization total 33.4% of the 24-h cycle, of which unilateral slow-wave sleep accounts for 28.8%. Each of the bottlenose dolphin's two hemispheres remains in a state of slow-wave ECoG for an average of 19% of the cycle. The highest percentage duration of sleep occurred during the afternoon and nighttime. Overall duration of ECoG synchronization may differ in the two hemispheres but evens out in the dolphin over a number of 24-h cycles. Spells of single-hemisphere sleep tend to alternate between the two different hemispheres.A. N. Severtsov Institute of Evolutionary Morphology and Animal Ecology. Translated from Neirofiziologiya, Vol. 20, No. 4, pp. 532–538, July–August, 1988.     

A model-based scan statistic for identifying extreme chromosomal regions of gene expression in human tumors

MOTIVATION: The analysis of gene expression data in its chromosomal context has been a recent development in cancer research. However, currently available methods fail to account for variation in the distance between genes, gene density and genomic features (e.g. GC content) in identifying increased or decreased chromosomal regions of gene expression. RESULTS: We have developed a model-based scan statistic that accounts for these aspects of the complex landscape of the human genome in the identification of extreme chromosomal regions of gene expression. This method may be applied to gene expression data regardless of the microarray platform used to generate it. To demonstrate the accuracy and utility of this method, we applied it to a breast cancer gene expression dataset and tested its ability to predict regions containing medium-to-high level DNA amplification (DNA ratio values >2). A classifier was developed from the scan statistic results that had a 10-fold cross-validated classification rate of 93% and a positive predictive value of 88%. This result strongly suggests that the model-based scan statistic and the expression characteristics of an increased chromosomal region of gene expression can be used to accurately predict chromosomal regions containing amplified genes. AVAILABILITY: Functions in the R-language are available from the author upon request. CONTACT: fcouples@umich.edu.     

Thermoregulation, metabolism, and stages of sleep in cold-exposed men

Four naked men, selected for their ability to sleep in the cold, were exposed to an ambient temperature (Ta) of 21 degrees C for five consecutive nights. Electrophysiological stages of sleep, O2 consumption (VO2), and skin (Tsk), rectal (Tre), and tympanic (Tty) temperatures were recorded. Compared with five nights at a thermoneutral Ta of 29 degrees C, cold induced increased wakefulness and decreased stage 2 sleep, without significantly affecting other stages. Tre and Tty declined during each condition. The decrease in Tre was greater at 21 degrees C than at 29 degrees C, whereas Tty did not differ significantly between conditions. Increases in Tty following REM sleep onset at 21 degrees C were negatively correlated with absolute Tty. VO2 and forehead Tsk also increased during REM sleep at both TaS, whereas Tsk of the limb extremities declined at 21 degrees C. Unsuppressed REM sleep in association with peripheral vasoconstriction and increased Tty and VO2 in cold-exposed humans, do not signify an inhibition of thermoregulation during this sleep stage as has been observed in other mammals.     

Analysis and automatic identification of sleep stages using higher order spectra

Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%.