In this paper, we tested the Gaussianity of the sleep EEG at each sleep stages. The test statistics we used are mainly bicoherence statistic and Kolmorgov-Smirnov statistic additively. We selected every five pages of each stage, which represent the specific stage. We apply the test to those pages. Then, we also applied the test to all the pages of sleep. The results shows that the EEG of every stage except SWS (slow wave sleep) can be accepted as Gaussian process. Comparing the result of representative data of each stage with that of the total pages of each stages, except SWS, some difference is founded. It means that the inter-variability of each stage except SWS is large. We think that it is due to the smoothing rules of R and K between obscure stages.
Electroencephalography* ; Sleep Stages

BACKGROUND: Up to now, sleep stages have traditionally been determined by the visual inspection of individual EEG waves. However, the exact physiological meaning of the sleep waves is not known. The purpose of this study was to try and find out the physiological parameters of the EEG of the sleep and wakefulness states by calculating one of the non-linear chaos parameter, the largest Lyapunov exponent (LLE), of EEG time series. METHODS: The digital EEG of the wakefulness with eye opening (WEO), wakefulness with eye closure (WEC), stage1 (S1), stage2 (S2), stage3 or 4 (S34) were recorded at centroparietal region (C4-P4 bipolar derivation) in 10 normal subjects. Lyapunov exponents of 50 EEG time series in different states were compared. RESULTS: LLE's of WEO, WEC, S1, S2, S34 showed an increas-ing tendency as states switched from wakefulness to sleep. LLE of sleep was larger than that of awake state. CONCLUSIONS: The EEG of the sleep state appeared to be more chaotic than that of the awake state. This nonlinear chaos parameter can be used as a physiological parameter of normal sleep and awake states.
Electroencephalography* ; Sleep Stages ; Wakefulness*

OBJECTIVES: We aimed to evaluate the effects of mattress type on both objective and subjective sleep quality in healthy good sleepers. METHODS: Fifteen healthy good sleepers with a mean age of 30.8 years participated in this study. A randomized crossover trial was carried out using two different mattress types: a standard mattress and a contour coil mattress. After a night of adaptation, all participants were randomized to either a standard mattress or a contour coil mattress. Then, nocturnal polysomnography was conducted for two consecutive nights. Subjective evaluations were obtained using a self-report questionnaire before and after the polysomnographic recording sessions. RESULTS: The polysomnographic showed no differences in total sleep time, sleep stage, or wake time after sleep onset between the two mattress conditions. Of the polysomnographic variables, only sleep onset latency was significantly reduced for the contour coil mattress. Additionally, participants reported better subjective sleep quality when sleeping on the contour coil mattress, according to the questionnaires. CONCLUSION: The type of mattress might affect not only subjective, but also objective sleep quality, including sleep onset latency.
Polysomnography ; Sleep Stages

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

Sleep staging is the basis for solving sleep problems. There's an upper limit for the classification accuracy of sleep staging models based on single-channel electroencephalogram (EEG) data and features. To address this problem, this paper proposed an automatic sleep staging model that mixes deep convolutional neural network (DCNN) and bi-directional long short-term memory network (BiLSTM). The model used DCNN to automatically learn the time-frequency domain features of EEG signals, and used BiLSTM to extract the temporal features between the data, fully exploiting the feature information contained in the data to improve the accuracy of automatic sleep staging. At the same time, noise reduction techniques and adaptive synthetic sampling were used to reduce the impact of signal noise and unbalanced data sets on model performance. In this paper, experiments were conducted using the Sleep-European Data Format Database Expanded and the Shanghai Mental Health Center Sleep Database, and achieved an overall accuracy rate of 86.9% and 88.9% respectively. When compared with the basic network model, all the experimental results outperformed the basic network, further demonstrating the validity of this paper's model, which can provide a reference for the construction of a home sleep monitoring system based on single-channel EEG signals.
China ; Sleep Stages ; Sleep ; Electroencephalography ; Databases, Factual

In clinical, manually scoring by technician is the major method for sleep arousal detection. This method is time-consuming and subjective. This study aimed to achieve an end-to-end sleep-arousal events detection by constructing a convolutional neural network based on multi-scale convolutional layers and self-attention mechanism, and using 1 min single-channel electroencephalogram (EEG) signals as its input. Compared with the performance of the baseline model, the results of the proposed method showed that the mean area under the precision-recall curve and area under the receiver operating characteristic were both improved by 7%. Furthermore, we also compared the effects of single modality and multi-modality on the performance of the proposed model. The results revealed the power of single-channel EEG signals in automatic sleep arousal detection. However, the simple combination of multi-modality signals may be counterproductive to the improvement of model performance. Finally, we also explored the scalability of the proposed model and transferred the model into the automated sleep staging task in the same dataset. The average accuracy of 73% also suggested the power of the proposed method in task transferring. This study provides a potential solution for the development of portable sleep monitoring and paves a way for the automatic sleep data analysis using the transfer learning method.
Sleep ; Sleep Stages ; Arousal ; Data Analysis ; Electroencephalography

Polysomnography (PSG) monitoring is an important method for clinical diagnosis of diseases such as insomnia, apnea and so on. In order to solve the problem of time-consuming and energy-consuming sleep stage staging of sleep disorder patients using manual frame-by-frame visual judgment PSG, this study proposed a deep learning algorithm model combining convolutional neural networks (CNN) and bidirectional gate recurrent neural networks (Bi GRU). A dynamic sparse self-attention mechanism was designed to solve the problem that gated recurrent neural networks (GRU) is difficult to obtain accurate vector representation of long-distance information. This study collected 143 overnight PSG data of patients from Shanghai Mental Health Center with sleep disorders, which were combined with 153 overnight PSG data of patients from the open-source dataset, and selected 9 electrophysiological channel signals including 6 electroencephalogram (EEG) signal channels, 2 electrooculogram (EOG) signal channels and a single mandibular electromyogram (EMG) signal channel. These data were used for model training, testing and evaluation. After cross validation, the accuracy was (84.0±2.0)%, and Cohen's kappa value was 0.77±0.50. It showed better performance than the Cohen's kappa value of physician score of 0.75±0.11. The experimental results show that the algorithm model in this paper has a high staging effect in different populations and is widely applicable. It is of great significance to assist clinicians in rapid and large-scale PSG sleep automatic staging.
Humans ; Polysomnography ; China ; Sleep Stages ; Sleep ; Algorithms

OBJECTIVES: This study was to investigate the EEG changes induced by Danhak which is one of the Korean traditional mediation. METHODS: Sixteen meditators and 9 controls were recruited. Spectral analysis and visual inspection of EEG during meditation(meditators) and relaxation(controls) were performed. The absolute power and interhemispheric coherence in earth frequency band were obtained. Ratio of change in absolute power and interhemispheric coherence was calculated to compare the EEG changes between meditators and controls. To evaluate episodic changes of EEG with time, all recorded EEGs were reviewed by visual inspection. RESULTS: Eleven meditators and 4 controls were excluded from the analysis due to drowsiness or poor compliance. Both meditators and controls showed various EEG changes and the degree of variability was more prominent in meditators than in controls. These differences were evident in absolute power of alpha and theta and coherence of beta at frontal, and coherence of theta at occipital. Meditators showed the increase in absolute power of alpha and theta at frontal, and interhemispheric coherence of theta at occipital. In visual inspection, a number of theta bursts were observed in three of 5 meditators and only one theta burst appeared in one control. CONCLUSION: These results suggest that great variability of EEG change and the appearance of theta bursts is the characteristics of EEG changes of meditators and that the state of meditation Is more diverse and dynamic than that of relaxation.
Compliance ; Electroencephalography ; Meditation* ; Negotiating ; Relaxation ; Sleep Stages

Clinical trials were done to obtain ifnormation on the clinical efficacy, tolerance and side effects of a new antihistarnine, Mecloprodine(Tavegyl), in various skin disordetrs. A total of 48 patients suffered from various skin disorders as urticaria, eczema etc. v ere treated with 2mg. daily for 3 days to 15 days according to state of disorders. The results are as follows. 1. Improvement was noticed in 79.2% of total patients(38/48). 2. Tavegyl was efiective in all 8 cases of acute urticaria and most cases(7/8) of urticaria factitia. 3. Among 19 cases of chronic urticaria, improvement was noticed in 13 cases(68. 49). Improvement was alsa noticed in all 6 cases of eczema. 5. Drowsiness and weakness cccurred in 6.2% of cases(3/48).
Clemastine* ; Eczema ; Humans ; Skin ; Sleep Stages ; Urticaria

OBJECTIVES: To compare the sleep quality in the view of polysomnography (PSG) and cardiopulmonary coupling (CPC) analysis in subjects with restless legs syndrome (RLS) versus with psychophysiological insomnia (PPI). METHODS: The PSG data of 109 subjects with RLS and 86 with PPI (apnea-hypopnea index <5 /h) were collected. All subjects reported sleep onset and maintenance insomnia. CPC parameters were obtained using CPC analyzer in RemLogic. Sleep spectrogram by CPC analyses categorized sleep as "stable" [high-frequency coupling (HFC), 0.1-0.4 Hz] and "unstable" [low-frequency coupling (LFC), 0.1-0.01 Hz], independent of sleep stages. We compared PSG and CPC parameters between two groups and performed correlation analyses to find the PSG parameters to affect CPC parameters. RESULTS: In PSG parameters, subjects with PPI showed significantly longer sleep latency (14.2+/-20.06 vs. 27.5+/-34.96, p<0.001), and decreased sleep efficiency (SE, 80.5+/-14.96 vs. 76.5+/-14.45, p=0.009) than RLS. CPC parameters were not significantly different between groups. In both groups HFC was positively correlated with total sleep time and SE and was negatively associated with time of wake after sleep onset in both groups. Meanwhile, very LFC showed the opposite results to HFC with the same PSG parameters. CONCLUSIONS: Although subjects with RLS or PPI present sleep onset and maintenance insomnia, objective sleep quality was worse in PPI than RLS. It suggests that CPC as a factor to differentiate sleep quality between the RLS and the PPI has a limited role.
Polysomnography* ; Restless Legs Syndrome* ; Sleep Initiation and Maintenance Disorders* ; Sleep Stages