Human Sleep Stages Research Articles

Functional burst imaging.

A quiet magnetic resonance (MR) imaging technique for detecting changes in cerebral activity functions is presented. This single-shot method, functional Burst imaging (FBI), combines elements of Burst imaging with an offset technique known as asymmetric spin echo (ASE). The FBI sequence has the unique feature of being nearly silent, because of the low number of gradient switching steps involved. Furthermore, this approach has the key advantage that it can be implemented on conventional MR systems. Established auditory and visual paradigms were used to evaluate whether FBI can detect changes in cerebral activity using a 1.5 Tesla MR system. In a second set of experiments, the FBI technique was used to evaluate cerebral activity changes during different sleep stages in humans. The results obtained demonstrate that the FBI sequence provides an alternative approach for functional imaging of brain activity in primary and secondary sensory areas of the human brain. Furthermore, in using this quiet MR technique, it was possible to scan continuously during different stages of human sleep without acoustic noise perturbation.

Magnetic motor-evoked potentials and F waves in human sleep stage

Magnetic motor-evoked potentials and F waves in human sleep stage

Automatic real-time analysis of human sleep stages by an interval histogram method

A new interval histogram method for automatic, all-night sleep stage scoring, simulated on a digital computer, is described. The system consists of a 2-step analysis. The first step is recognition of elementary patterns in EEG, EOG and EMG, and the second step is the determination of sleep stages based on these parameters. Correlation of this method with power spectral analysis of the dominant EEG patterns during each sleep stage supported the reliability of the first step analysis. Overall agreement (89.1%) between the computer and human judges was only 3% less than the agreement (92.1%) among the scorers, indicating considerable reliability of the second step. The primary areas of disagrement that arose in the identification of sleep stages occurred with a stages 1, 2 and REM. To improve scoring accuracy, the system may require epoch sequence information. The profile of the elementary parameters of the EEG signals clearly illustrated the cyclic nature of these activities throughout the night. The alpha and delta 2 waves clearly separated the awake state from sleep stages. Beta 2 can discriminate stages 1 and REM from stage 2, and the best indicator for distincting stage 1 from REM was muscle activity. Sigma and spindles were prominent during stage 2 sleep. Both delta 2 and high voltage delta waves distinguished stage 3 from stage 4. On the other hand, delta 1 was evenly distributed and seemed to be common to all sleep stages.

Effect of continuous heat exposure on sleep stages in humans.

Six young men were exposed to a thermoneutral environment of air temperature (Ta) 20 degrees C for 5 days and nights followed by an acclimation period of 5 days and nights at Ta 35 degrees C and 2 recovery days and nights at Ta 20 degrees C. Electrophysiological measures of sleep, esophageal temperature, and mean skin temperature were continuously monitored. The total nocturnal body weight loss was measured by a sensitive platform scale. Compared with the 5 nights of the baseline period at 20 degrees C, sleep patterns showed disturbances at 35 degrees C. Total sleep time was significantly reduced, while the amount of wakefulness increased. The subjects exhibited fragmented sleep patterns. The mean duration of REM episodes was shorter at 35 degrees C than at 20 degrees C of Ta, while the REM cycle length shortened. In the acclimation period, there was no change in sleep pattern from night to night, despite adaptative adjustments of the thermoregulatory response. The protective mechanisms of deep body temperature occurring with heat adaptation did not interact with sleep processes. Upon return to baseline condition, a recovery effect was observed on a number of sleep parameters which were not significantly affected by the preceding exposure to prolonged heat. This would suggest that during exposure to dry heat, the demand for sleep could overcome that of other regulatory functions that are temperature-dependent. Therefore, a complete analysis of the effect of heat on sleep parameters can be assessed only if heat exposure is compared with both baseline and recovery periods.

A model-based monitor of human sleep stages

Stochastic models are proposed for sleep and for the sleep related electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG). The evolution of sleep through its various stages is described as a Markov chain. The EEG is modelled using Wiener processes. The EOG and EMG are modelled as combinations of Poisson point processes and Gaussian processes, respectively. The EEG models contain a feedback structure that is based on physiological data. The maximum likelihood sleep stage monitor, that uses the sleep-related observations, has been derived and implemented. The agreement between automatic and human stage classifications of six sleep recordings was 70.6%, which was 4.5% worse than the average agreement between six human classifiers. Monitoring of simulated sleep suggests that the difficulty in separating wakefulness from stage 1 is due to poor modelling. If one ignores this difference, which, from a diagnostic point of view is fairly unimportant, the above mentioned agreement reaches 81.8%, which is 0.5% better than the corresponding average human vs human agreement.

Simulation of human hypnograms using a Markov chain model.

A Markov chain model has been proposed as a mechanism that generates human sleep stages. A method for estimating the parameters of the model, i.e., the transition probabilities (rates) between sleep stages, has been introduced and applied to 95 hypnograms taken from 23 subjects. The rates characterize interindividual differences and nightly variations of the sleep mechanism, related to sleep-onset behavior, to the decreasing amount of slow wave sleep in the course of the night, and to the REM-NREM periodicity. The model simulates both probabilistic and the above-mentioned predictable dynamics of sleep, but only if these time-varying, individual rates are applied.

The effects of high and low ambient temperatures on human sleep stages

Six male subjects slept nude except for shorts on a bed made from nylon webbing at 5 different ambient temperatures (T as): 21, 24, 29 (thermoneutrality), 34 and 37°C. Standard electrophysiological recordings were obtained and analyzed for sleep stages. Temperature displayed significant quadratic trends for nearly every sleep variable, such that T as above or below thermoneutrality had similar effects on sleep patterns. Multiple comparisons showed that 21°C was the most disruptive condition, and that cold T as were generally more disruptive to sleep than warm T as. There were marked individual differences in sensitivity of sleep to cold. Decreases in REM sleep in humans produced by heat or cold probably result from a general disruption of sleep processes rather than being specifically related to the status of the thermoregulatory system during REM sleep.

A real-time system for pattern recognition of human sleep stages by fuzzy system analysis

A pattern recognition system for the analysis of human sleep stages using the EEG data is presented. The system is designed by using concepts of the fuzzy system analysis and it is implemented in hardware for real-time applications.