Journal articles: 'Human sleep EEG analysis'

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Kobayashi, Toshio, Shigeki Madokoro, Yuji Wada, Kiwamu Misaki, and Hiroki Nakagawa. "Human Sleep EEG Analysis Using the Correlation Dimension." Clinical Electroencephalography 32, no. 3 (July 2001): 112–18. http://dx.doi.org/10.1177/155005940103200305.

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Sleep electroencephalograms (EEG) were analyzed by non-linear analysis. Polysomnography (PSG) of nine healthy male subjects was analyzed and the correlation dimension (D2) was calculated. The D2 characterizes the dynamics of the sleep EEG, estimates the degrees of freedom, and describes the complexity of the signal. The mean D2 decreased from the awake stage to stages 1,2,3 and 4 and increased during rapid eye movement (REM) sleep. The D2 during each REM sleep stage were high and those during each slow wave sleep stage were low, respectively, for each sleep cycle. The mean D2 of the sleep EEG in the second half of the night was significantly higher than those in the first half of the night. Significant changes were also observed during sleep stage 2, but were not seen during REM sleep and sleep stages 3 and 4. The D2 may be a useful method in the analysis of the entire sleep EEG.

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Kim, Hyungrae, Christian Guilleminault, Seungchul Hong, Daijin Kim, Sooyong Kim, Hyojin Go, and Sungpil Lee. "Pattern analysis of sleep-deprived human EEG." Journal of Sleep Research 10, no. 3 (September 26, 2001): 193–201. http://dx.doi.org/10.1046/j.1365-2869.2001.00258.x.

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SHENG, HU, YANGQUAN CHEN, and TIANSHUANG QIU. "MULTIFRACTIONAL PROPERTY ANALYSIS OF HUMAN SLEEP EEG SIGNALS." International Journal of Bifurcation and Chaos 22, no. 04 (April 2012): 1250080. http://dx.doi.org/10.1142/s0218127412500800.

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Electroencephalogram (EEG), the measures and records of the electrical activity of the brain, exhibits evidently nonlinear, nonstationary, chaotic and complex dynamic properties. Based on these properties, many nonlinear dynamical analysis techniques have emerged, and much valuable information has been extracted from complex EEG signals using these nonlinear analysis techniques. Among these techniques, the Hurst exponent estimation was widely used to characterize the fractional or scaling property of the EEG signals. However, the constant Hurst exponent H cannot capture the detailed information of dynamic EEG signals. In this research, the multifractional property of the normal human sleep EEG signals is investigated and characterized using local Hölder exponent H(t). The comparison of the analysis results for human sleep EEG signals in different stages using constant Hurst exponent H and the local Hölder exponent H(t) are summarized with tables and figures in the paper. The results of the analysis show that local Hölder exponent provides a novel and valid tool for dynamic assessment of brain activities in different sleep stages.

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Vincze, Janos. "The Sleep Modeling in the Human Organism." Clinical Research and Clinical Trials 3, no. 4 (May 28, 2021): 01–04. http://dx.doi.org/10.31579/2693-4779/039.

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There are three alternating states of vigilance throughout our lives: wakefulness, NREM, and REM sleep. We usually yawn before falling asleep. Yawning is an ancient reaction, an instinctive action, manifested in a person by drowsiness or boredom. Yawning is often associated with the need for stretching. Yawning is a less strong territorial reflex. During deep sleep muscular tone is sharply reduced. Relaxation of the muscles and the lowering of their tone, howeever, are not constant and necessary components of sleep. Analysis of EEG recordings soon revealed that sleep is by no means a uniform process, but can be divided into at least two sharply separated states: one is characterized by slow waves in the EEG that are completely separate from the activity of wakefulness: this so-called slow wave sleep; the other is the so-called paradoxical sleep. Hypnopedia, as a discipline, deals with the input of fixed information introduced during the period of natural sleep, also known as sleep learning. Our hypnopedia researches was a pleasant surprise, because they were able to reproduce texts they did not know with an efficiency of approx. 25%.

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Lee, Jong-Min, Dae-Jin Kim, In-Young Kim, Kwang Suk Park, and Sun I. Kim. "Nonlinear-analysis of human sleep EEG using detrended fluctuation analysis." Medical Engineering & Physics 26, no. 9 (November 2004): 773–76. http://dx.doi.org/10.1016/j.medengphy.2004.07.002.

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Tosun, Pinar Deniz, Derk-Jan Dijk, Raphaelle Winsky-Sommerer, and Daniel Abasolo. "Effects of Ageing and Sex on Complexity in the Human Sleep EEG: A Comparison of Three Symbolic Dynamic Analysis Methods." Complexity 2019 (January 2, 2019): 1–12. http://dx.doi.org/10.1155/2019/9254309.

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Symbolic dynamic analysis (SDA) methods have been applied to biomedical signals and have been proven efficient in characterising differences in the electroencephalogram (EEG) in various conditions (e.g., epilepsy, Alzheimer’s, and Parkinson’s diseases). In this study, we investigated the use of SDA on EEGs recorded during sleep. Lempel-Ziv complexity (LZC), permutation entropy (PE), and permutation Lempel-Ziv complexity (PLZC), as well as power spectral analysis based on the fast Fourier transform (FFT), were applied to 8-h sleep EEG recordings in healthy men (n=31) and women (n=29), aged 20-74 years. The results of the SDA methods and FFT analysis were compared and the effects of age and sex were investigated. Surrogate data were used to determine whether the findings with SDA methods truly reflected changes in nonlinear dynamics of the EEG and not merely changes in the power spectrum. The surrogate data analysis showed that LZC merely reflected spectral changes in EEG activity, whereas PE and PLZC reflected genuine changes in the nonlinear dynamics of the EEG. All three SDA techniques distinguished the vigilance states (i.e., wakefulness, REM sleep, NREM sleep, and its sub-stages: stage 1, stage 2, and slow wave sleep). Complexity of the sleep EEG increased with ageing. Sex on the other hand did not affect the complexity values assessed with any of these three SDA methods, even though FFT detected sex differences. This study shows that SDA provides additional insights into the dynamics of sleep EEG and how it is affected by ageing.

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Stassen, H. H. "The octave approach to EEG analysis." Methods of Information in Medicine 30, no. 04 (1991): 304–10. http://dx.doi.org/10.1055/s-0038-1634849.

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Abstract:A “tonal” approach to EEG spectral analysis is presented which is compatible with the concept of physical octaves, thus providing a constant resolution of partial tones over the full frequency range inherent to human brain waves, rather than for equidistant frequency steps in the spectral domain. The specific advantages of the tonal approach, however, mainly pay off in the field of EEG sleep analysis where the interesting information is predominantly located in the lower octaves. In such cases the proposed method reveals a fine structure which displays regular maxima possessing typical properties of “overtones” within the three octaves 1-2 Hz, 2-4 Hz and 4-8 Hz. Accordingly, spectral patterns derived from tonal spectral analyses are particularly suited to measure the fine gradations of mutual differences between individual EEG sleep patterns and will therefore allow a more efficient investigation of the genetically determined proportion of sleep EEGs. On the other hand, we also tested the efficiency of tonal spectral analyses on the basis of our 5-year follow-up data of 30 healthy volunteers. It turned out that 28 persons (93.3%) could be uniquely recognized after five years by means of their EEG spectral patterns. Hence, tonal spectral analysis proved to be a powerful tool also in cases where the main EEG information is typically located in the medium octave 8-16 Hz.

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Kobayashi, Toshio, Shigeki Madokoro, Yuji Wada, Kiwamu Misaki, and Hiroki Nakagawa. "Effect of Ethanol on Human Sleep EEG Using Correlation Dimension Analysis." Neuropsychobiology 46, no. 2 (2002): 104–10. http://dx.doi.org/10.1159/000065420.

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Inoue, Katsuhiro, Tomohiro Tsujihata, Yuma Aso, Kousuke Kumamaru, and Shigeki Matsuoka. "Feature Extraction of Human EEG Sleep Stages by Using Wavelet Analysis." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2004 (May 5, 2004): 140–45. http://dx.doi.org/10.5687/sss.2004.140.

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Inoue, Katsuhiro, Tomohiro Tsujihata, Kousuke Kumamaru, and Shigeaki Matsuoka. "FEATURE EXTRACTION OF HUMAN SLEEP EEG BASED ON A PEAK FREQUENCY ANALYSIS." IFAC Proceedings Volumes 38, no. 1 (2005): 1059–64. http://dx.doi.org/10.3182/20050703-6-cz-1902.00177.

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Jin, Seung-Hyun, Sun Hee Na, Soo Yong Kim, and Dai-Jin Kim. "Effects of total sleep-deprivation on waking human EEG: functional cluster analysis." Clinical Neurophysiology 115, no. 12 (December 2004): 2825–33. http://dx.doi.org/10.1016/j.clinph.2004.07.001.

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Crespo-Garcia, Maite, Mercedes Atienza, and Jose L. Cantero. "Muscle Artifact Removal from Human Sleep EEG by Using Independent Component Analysis." Annals of Biomedical Engineering 36, no. 3 (January 29, 2008): 467–75. http://dx.doi.org/10.1007/s10439-008-9442-y.

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Jenni, Oskar G., Alexander A. Borbély, and Peter Achermann. "Development of the nocturnal sleep electroencephalogram in human infants." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 286, no. 3 (March 2004): R528—R538. http://dx.doi.org/10.1152/ajpregu.00503.2003.

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The development of nocturnal sleep and the sleep electroencephalogram (EEG) was investigated in a longitudinal study during infancy. All-night polysomnographic recordings were obtained at home at 2 wk and at 2, 4, 6, and 9 mo after birth (analysis of 7 infants). Total sleep time and the percentage of quiet sleep or non-rapid eye movement sleep (QS/NREMS) increased with age, whereas the percentage of active sleep or rapid eye movement sleep (AS/REMS) decreased. Spectral power of the sleep EEG was higher in QS/NREMS than in AS/REMS over a large part of the 0.75- to 25-Hz frequency range. In both QS/NREMS and AS/REMS, EEG power increased with age in the frequency range <10 Hz and >17 Hz. The largest rise occurred between 2 and 6 mo. A salient feature of the QS/NREMS spectrum was the emergence of a peak in the sigma band (12-14 Hz) at 2 mo that corresponded to the appearance of sleep spindles. Between 2 and 9 mo, low-frequency delta activity (0.75-1.75 Hz) showed an alternating pattern with a high level occurring in every other QS/NREMS episode. At 6 mo, sigma activity showed a similar pattern. In contrast, theta activity (6.5-9 Hz) exhibited a monotonic decline over consecutive QS/NREMS episodes, a trend that at 9 mo could be closely approximated by an exponential function. The results suggest that 1) EEG markers of sleep homeostasis appear in the first postnatal months, and 2) sleep homeostasis goes through a period of maturation. Theta activity and not delta activity seems to reflect the dissipation of sleep propensity during infancy.

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Helland, V. C., A. Gapelyuk, A. Suhrbier, M. Riedl, T. Penzel, J. Kurths, and N. Wessel. "Investigation of an Automatic Sleep Stage Classification by Means of Multiscorer Hypnogram." Methods of Information in Medicine 49, no. 05 (2010): 467–72. http://dx.doi.org/10.3414/me09-02-0052.

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Summary Objectives: Scoring sleep visually based on polysomnography is an important but time-consuming element of sleep medicine. Whereas computer software assists human experts in the assignment of sleep stages to polysomnogram epochs, their performance is usually insufficient. This study evaluates the possibility to fully automatize sleep staging considering the reliability of the sleep stages available from human expert sleep scorers. Methods: We obtain features from EEG, ECG and respiratory signals of polysomnograms from ten healthy subjects. Using the sleep stages provided by three human experts, we evaluate the performance of linear discriminant analysis on the entire polysomnogram and only on epochs where the three experts agree in their sleep stage scoring. Results: We show that in polysomnogram intervals, to which all three scorers assign the same sleep stage, our algorithm achieves 90% accuracy. This high rate of agreement with the human experts is accomplished with only a small set of three frequency features from the EEG. We increase the performance to 93% by including ECG and respiration features. In contrast, on intervals of ambiguous sleep stage, the sleep stage classification obtained from our algorithm, agrees with the human consensus scorer in approximately 61%. Conclusions: These findings suggest that machine classification is highly consistent with human sleep staging and that error in the algorithm’s assignments is rather a problem of lack of well-defined criteria for human experts to judge certain polysomnogram epochs than an insufficiency of computational procedures.

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Gurrala, Vijayakumar, Padmasai Yarlagadda, and Padmaraju Koppireddi. "Detection of Sleep Apnea Based on the Analysis of Sleep Stages Data Using Single Channel EEG." Traitement du Signal 38, no. 2 (April 30, 2021): 431–36. http://dx.doi.org/10.18280/ts.380221.

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Sleep is a basic need for a human being’s intellectual and physiological restoration and overlaying nearly one 1/3 length of a daytime. A first-rate and deep sleep is required for green regeneration of the body. Sleep disorders hamper the performance of an individual. Sleep Apnea is the one amongst the disorders that affect many. Most of Apnea related works consider Electrocardiogram (ECG) and respiratory signals /or combinations, instead of considering all Polysomnographic signals (PSG). It is evident that for the detection of Apnea related sleep disorders it is required to consider one or few signals rather considering all PSG signals. In this work, we advocate a way that might be carried out to perceive the information of sleep stages which might be crucial in diagnosing and treating sleep disorders. It differentiates sleep stages and derives new features from the sleep EEG that allows helping physicians with the analysis and treatment of associated sleep issues. This theory depends on exclusive EEG datasets from Physionet with the use of MIT-BIH polysomnographic database that have been received and described through scientists for the analysis and prognosis of sleep ranges. Experimental results on 18 records with 10197 epochs show that an Apnea detection accuracy of 95.9% obtained for Machine learning classifier with Ensemble Bagged Tree classifier.

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Kaplan, A. "State-shift analysis of the sleep EEG in humans." Electroencephalography and Clinical Neurophysiology 103, no. 1 (July 1997): 178. http://dx.doi.org/10.1016/s0013-4694(97)88836-4.

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Zheng, Xiangwei, Xiaochun Yin, Xuexiao Shao, Yalin Li, and Xiaomei Yu. "Collaborative Sleep Electroencephalogram Data Analysis Based on Improved Empirical Mode Decomposition and Clustering Algorithm." Complexity 2020 (June 13, 2020): 1–14. http://dx.doi.org/10.1155/2020/1496973.

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Sleep-related diseases seriously affect the life quality of patients. Sleep stage classification (or sleep staging), which studies the human sleep process and classifies the sleep stages, is an important reference to the diagnosis and study of sleep disorders. Many scholars have conducted a series of sleep staging studies, but the correlation between different sleep stages and the accuracy of classification still needs to be improved. Therefore, this paper proposes an automatic sleep stage classification based on EEG. By constructing an improved empirical mode decomposition and K-means experimental model, the concept of “frequency-domain correlation coefficient” is defined. In the process of feature extraction, the feature vector with the best correlation in the time-frequency domain is selected. Extraction and classification of EEG features are realized based on the K-means clustering algorithm. Experimental results demonstrate that the classification accuracy is significantly improved, and our proposed algorithm has a positive impact on sleep staging compared with other algorithms.

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Weiss, Béla, Zsófia Clemens, Róbert Bódizs, Zsuzsanna Vágó, and Péter Halász. "Spatio-temporal analysis of monofractal and multifractal properties of the human sleep EEG." Journal of Neuroscience Methods 185, no. 1 (December 2009): 116–24. http://dx.doi.org/10.1016/j.jneumeth.2009.07.027.

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Lv, Jun, Dongdong Liu, Jing Ma, Xiaoying Wang, and Jue Zhang. "Graph Theoretical Analysis of BOLD Functional Connectivity during Human Sleep without EEG Monitoring." PLOS ONE 10, no. 9 (September 11, 2015): e0137297. http://dx.doi.org/10.1371/journal.pone.0137297.

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Dijk, D. J., D. G. Beersma, S. Daan, and A. J. Lewy. "Bright morning light advances the human circadian system without affecting NREM sleep homeostasis." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 256, no. 1 (January 1, 1989): R106—R111. http://dx.doi.org/10.1152/ajpregu.1989.256.1.r106.

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Eight male subjects were exposed to either bright light or dim light between 0600 and 0900 h for 3 consecutive days each. Relative to the dim light condition, the bright light treatment advanced the evening rise in plasma melatonin and the time of sleep termination (sleep onset was held constant) for an average approximately 1 h. The magnitude of the advance of the plasma melatonin rise was dependent on its phase in dim light. The reduction in sleep duration was at the expense of rapid-eye-movement (REM) sleep. Spectral analysis of the sleep electroencephalogram (EEG) revealed that the advance of the circadian pacemaker did not affect EEG power densities between 0.25 and 15.0 Hz during either non-REM or REM sleep. The data show that shifting the human circadian pacemaker by 1 h does not affect non-REM sleep homeostasis. These findings are in accordance with the predictions of the two-process model of sleep regulation.

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Chylinski, Daphne, Franziska Rudzik, Dorothée Coppieters ‘t Wallant, Martin Grignard, Nora Vandeleene, Maxime Van Egroo, Laurie Thiesse, et al. "Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings." Clocks & Sleep 2, no. 3 (July 16, 2020): 258–72. http://dx.doi.org/10.3390/clockssleep2030020.

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Arousals during sleep are transient accelerations of the EEG signal, considered to reflect sleep perturbations associated with poorer sleep quality. They are typically detected by visual inspection, which is time consuming, subjective, and prevents good comparability across scorers, studies and research centres. We developed a fully automatic algorithm which aims at detecting artefact and arousal events in whole-night EEG recordings, based on time-frequency analysis with adapted thresholds derived from individual data. We ran an automated detection of arousals over 35 sleep EEG recordings in healthy young and older individuals and compared it against human visual detection from two research centres with the aim to evaluate the algorithm performance. Comparison across human scorers revealed a high variability in the number of detected arousals, which was always lower than the number detected automatically. Despite indexing more events, automatic detection showed high agreement with human detection as reflected by its correlation with human raters and very good Cohen’s kappa values. Finally, the sex of participants and sleep stage did not influence performance, while age may impact automatic detection, depending on the human rater considered as gold standard. We propose our freely available algorithm as a reliable and time-sparing alternative to visual detection of arousals.

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NAMAZI, HAMIDREZA, and SAJAD JAFARI. "ESTIMATING OF BRAIN DEVELOPMENT IN NEWBORNS BY FRACTAL ANALYSIS OF SLEEP ELECTROENCEPHALOGRAPHIC (EEG) SIGNAL." Fractals 27, no. 03 (May 2019): 1950021. http://dx.doi.org/10.1142/s0218348x1950021x.

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Analysis of the brain development is one of the major research areas in human neuroscience. In order to analyze the human brain development, scientists employ different brain imaging techniques. One of the typical techniques to measure the brain activity is electroencephalography (EEG). In this paper, we do complexity analysis on the EEG signal recorded from the newborns during their sleep, in different weeks of post conception. We analyze how the nonlinear structure of EEG signal changes for newborns with their ages by using fractal theory. The result of our analysis showed that the EEG signals for newborn in 45 weeks have the highest fractal dimension. The lowest fractal dimension of EEG signal was obtained for newborns in 36 weeks. Based on our analysis, we conclude that the complexity of brain signal significantly changes with the newborn age. The proposed method is not limited to the analysis of the brain development, and can be applied to investigate the brain activity in different tasks.

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Achermann, Peter, Thomas Rusterholz, Roland Dürr, Thomas König, and Leila Tarokh. "Global field synchronization reveals rapid eye movement sleep as most synchronized brain state in the human EEG." Royal Society Open Science 3, no. 10 (October 2016): 160201. http://dx.doi.org/10.1098/rsos.160201.

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Sleep is characterized by a loss of consciousness, which has been attributed to a breakdown of functional connectivity between brain regions. Global field synchronization (GFS) can estimate functional connectivity of brain processes. GFS is a frequency-dependent measure of global synchronicity of multi-channel EEG data. Our aim was to explore and extend the hypothesis of disconnection during sleep by comparing GFS spectra of different vigilance states. The analysis was performed on eight healthy adult male subjects. EEG was recorded during a baseline night, a recovery night after 40 h of sustained wakefulness and at 3 h intervals during the 40 h of wakefulness. Compared to non-rapid eye movement (NREM) sleep, REM sleep showed larger GFS values in all frequencies except in the spindle and theta bands, where NREM sleep showed a peak in GFS. Sleep deprivation did not affect GFS spectra in REM and NREM sleep. Waking GFS values were lower compared with REM and NREM sleep except for the alpha band. Waking alpha GFS decreased following sleep deprivation in the eyes closed condition only. Our surprising finding of higher synchrony during REM sleep challenges the view of REM sleep as a desynchronized brain state and may provide insight into the function of REM sleep.

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Olbrich, Eckehard, Thomas Rusterholz, Monique K. LeBourgeois, and Peter Achermann. "Developmental Changes in Sleep Oscillations during Early Childhood." Neural Plasticity 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/6160959.

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Although quantitative analysis of the sleep electroencephalogram (EEG) has uncovered important aspects of brain activity during sleep in adolescents and adults, similar findings from preschool-age children remain scarce. This study utilized our time-frequency method to examine sleep oscillations as characteristic features of human sleep EEG. Data were collected from a longitudinal sample of young children (n=8; 3 males) at ages 2, 3, and 5 years. Following sleep stage scoring, we detected and characterized oscillatory events across age and examined how their features corresponded to spectral changes in the sleep EEG. Results indicated a developmental decrease in the incidence of delta and theta oscillations. Spindle oscillations, however, were almost absent at 2 years but pronounced at 5 years. All oscillatory event changes were stronger during light sleep than slow-wave sleep. Large interindividual differences in sleep oscillations and their characteristics (e.g., “ultrafast” spindle-like oscillations, theta oscillation incidence/frequency) also existed. Changes in delta and spindle oscillations across early childhood may indicate early maturation of the thalamocortical system. Our analytic approach holds promise for revealing novel types of sleep oscillatory events that are specific to periods of rapid normal development across the lifespan and during other times of aberrant changes in neurobehavioral function.

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Mukai, Junko, Sunao Uchida, Shinichi Miyazaki, Kyoko Nishihara, and Yutaka Honda. "Spectral analysis of all-night human sleep EEG in narcoleptic patients and normal subjects." Journal of Sleep Research 12, no. 1 (March 2003): 63–71. http://dx.doi.org/10.1046/j.1365-2869.2003.00331.x.

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OLBRICH, E., and P. ACHERMANN. "Analysis of oscillatory patterns in the human sleep EEG using a novel detection algorithm." Journal of Sleep Research 14, no. 4 (December 2005): 337–46. http://dx.doi.org/10.1111/j.1365-2869.2005.00475.x.

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Dijk, D. J., D. P. Brunner, and A. A. Borbely. "Time course of EEG power density during long sleep in humans." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 258, no. 3 (March 1, 1990): R650—R661. http://dx.doi.org/10.1152/ajpregu.1990.258.3.r650.

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In nine subjects sleep was recorded under base-line conditions with a habitual bedtime (prior wakefulness 16 h; lights off at 2300 h) and during recovery from sleep deprivation with a phase-advanced bedtime (prior wakefulness 36 h; lights off at 1900 h). The duration of phase-advanced recovery sleep was greater than 12 h in all subjects. Spectral analysis of the sleep electroencephalogram (EEG) revealed that slow-wave activity (SWA; 0.75-4.5 Hz) in non-rapid-eye-movement (NREM) sleep was significantly enhanced during the first two NREM-REM sleep cycles of displaced recovery sleep. The sleep stages 3 and 4 (slow-wave sleep) and SWA decreased monotonically over the first three and four NREM-REM cycles of, respectively, base-line and recovery sleep. The time course of SWA in base-line and recovery sleep could be adequately described by an exponentially declining function with a horizontal asymptote. The results are in accordance with the two-process model of sleep regulation in which it is assumed that SWA rises as a function of the duration of prior wakefulness and decreases exponentially as a function of prior sleep. We conclude that the present data do not provide evidence for a 12.5-h sleep-dependent rhythm of deep NREM sleep.

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Webster, Kate E., and Ian M. Colrain. "Multichannel EEG analysis of respiratory evoked-potential components during wakefulness and NREM sleep." Journal of Applied Physiology 85, no. 5 (November 1, 1998): 1727–35. http://dx.doi.org/10.1152/jappl.1998.85.5.1727.

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Airway occlusion in awake humans produces a somatosensory evoked response called the respiratory-related evoked potential (RREP). In the present study, 29 channel evoked-potential recordings were obtained from seven men who were exposed to 250-ms inspiratory airway occlusions during wakefulness, stage 1, stage 2, and slow-wave sleep. The RREP recorded during wakefulness was similar to previous reports, with the unique observation of an additional short-latency positive peak with a mean latency of 25 ms. Short-latency RREP components were maintained in non-rapid-eye-movement (NREM) sleep. The clearly seen N1 vertex and late positive complex components during wakefulness were markedly attenuated during NREM sleep, and two large negative components (N300 and N550) dominated the sleep RREP. These findings indicate the maintenance of central nervous system monitoring of respiratory afferent information during NREM sleep, presumably to facilitate protective arousal responses to pathophysiological respiratory phenomena.

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Uchida, Sunao, Irwin Feinberg, Jonathan D. March, Yoshikata Atsumi, and Tom Maloney. "A Comparison of Period Amplitude Analysis and FFT Power Spectral Analysis of All-Night Human Sleep EEG." Physiology & Behavior 67, no. 1 (August 1999): 121–31. http://dx.doi.org/10.1016/s0031-9384(99)00049-9.

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Herrero, Miguel A., Rebeca Gallego, Milagros Ramos, Juan Manuel Lopez, Guillermo de Arcas, and Daniel Gonzalez-Nieto. "Sleep–Wake Cycle and EEG-Based Biomarkers during Neonate to Adult Transition in C57BL/6 Mice." Proceedings 71, no. 1 (December 3, 2020): 4. http://dx.doi.org/10.3390/iecbs-08871.

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During the transition from neonate to adulthood, brain maturation establishes coherence between behavioral states—wakefulness, non-rapid eye movement, and rapid eye movement sleep. Few studies have characterized and analyzed cerebral rhythms and the sleep–wake cycle in early ages, in relation to adulthood. Since the analysis of sleep in early ages can be used as a predictive model of brain development and the subsequent emergence of neural disturbances in adults, we performed a study on late neonatal and adult wild-type C57BL/6 mice. We acquired longitudinal 24 h electroencephalogram and electromyogram recordings and performed time and spectral analyses. We compared both age groups and found that late neonates: (i) spent more time in wakefulness and less time in non-rapid eye movement sleep, (ii) showed an increased relative band power in delta, which, however, reduced in theta during each behavioral state, (iii) showed a reduced relative band power in beta during wakefulness and non-rapid eye movement sleep, and (iv) manifested an increased total power over all frequencies. Given the mice–human age equivalence, the data presented here might have implications for the clinical context in the analysis of electroencephalogram and sleep-based early and late diagnosis after injury or neurodegeneration.

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Loh, Hui Wen, Chui Ping Ooi, Jahmunah Vicnesh, Shu Lih Oh, Oliver Faust, Arkadiusz Gertych, and U. Rajendra Acharya. "Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020)." Applied Sciences 10, no. 24 (December 15, 2020): 8963. http://dx.doi.org/10.3390/app10248963.

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Sleep is vital for one’s general well-being, but it is often neglected, which has led to an increase in sleep disorders worldwide. Indicators of sleep disorders, such as sleep interruptions, extreme daytime drowsiness, or snoring, can be detected with sleep analysis. However, sleep analysis relies on visuals conducted by experts, and is susceptible to inter- and intra-observer variabilities. One way to overcome these limitations is to support experts with a programmed diagnostic tool (PDT) based on artificial intelligence for timely detection of sleep disturbances. Artificial intelligence technology, such as deep learning (DL), ensures that data are fully utilized with low to no information loss during training. This paper provides a comprehensive review of 36 studies, published between March 2013 and August 2020, which employed DL models to analyze overnight polysomnogram (PSG) recordings for the classification of sleep stages. Our analysis shows that more than half of the studies employed convolutional neural networks (CNNs) on electroencephalography (EEG) recordings for sleep stage classification and achieved high performance. Our study also underscores that CNN models, particularly one-dimensional CNN models, are advantageous in yielding higher accuracies for classification. More importantly, we noticed that EEG alone is not sufficient to achieve robust classification results. Future automated detection systems should consider other PSG recordings, such as electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals, along with input from human experts, to achieve the required sleep stage classification robustness. Hence, for DL methods to be fully realized as a practical PDT for sleep stage scoring in clinical applications, inclusion of other PSG recordings, besides EEG recordings, is necessary. In this respect, our report includes methods published in the last decade, underscoring the use of DL models with other PSG recordings, for scoring of sleep stages.

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González, Julián, Antoni Gamundi, Rubén Rial, M. Cristina Nicolau, Luis de Vera, and Ernesto Pereda. "Nonlinear, fractal, and spectral analysis of the EEG of lizard, Gallotia galloti." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 277, no. 1 (July 1, 1999): R86—R93. http://dx.doi.org/10.1152/ajpregu.1999.277.1.r86.

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Electroencephalogram (EEG) from dorsal cortex of lizard Gallotia galloti was analyzed at different temperatures to test the presence of fractal or nonlinear structure during open (OE) and closed eyes (CE), with the aim of comparing these results with those reported for human slow-wave sleep (SWS). Two nonlinear parameters characterizing EEG complexity [correlation dimension (D2)] and predictability [largest Lyapunov exponent (λ1)] were calculated, and EEG spectrum and fractal exponent β were determined via coarse graining spectral analysis. At 25°C, evidence of nonlinear structure was obtained by the surrogate data test, with EEG phase space structure suggesting the presence of deterministic chaos (D2 ∼6, λ1 ∼1.5). Both nonlinear parameters were greater in OE than in CE and for the right hemisphere in both situations. At 35°C the evidence of nonlinearity was not conclusive and differences between states disappeared, whereas interhemispheric differences remained for λ1. Harmonic power always increased with temperature within the band 8–30 Hz, but only with OE within the band 0.3–7.5 Hz. Qualitative similarities found between lizard and human SWS EEG support the hypothesis that reptilian waking could evolve into mammalian SWS.

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Cajochen, Christian, Rosalba Di Biase, and Makoto Imai. "Interhemispheric EEG asymmetries during unilateral bright-light exposure and subsequent sleep in humans." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 294, no. 3 (March 2008): R1053—R1060. http://dx.doi.org/10.1152/ajpregu.00747.2007.

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We tested whether evening exposure to unilateral photic stimulation has repercussions on interhemispheric EEG asymmetries during wakefulness and later sleep. Because light exerts an alerting response in humans, which correlates with a decrease in waking EEG theta/alpha-activity and a reduction in sleep EEG delta activity, we hypothesized that EEG activity in these frequency bands show interhemispheric asymmetries after unilateral bright light (1,500 lux) exposure. A 2-h hemi-field light exposure acutely suppressed occipital EEG alpha activity in the ipsilateral hemisphere activated by light. Subjects felt more alert during bright light than dim light, an effect that was significantly more pronounced during activation of the right than the left visual cortex. During subsequent sleep, occipital EEG activity in the delta and theta range was significantly reduced after activation of the right visual cortex but not after stimulation of the left visual cortex. Furthermore, hemivisual field light exposure was able to shift the left predominance in occipital spindle EEG activity toward the stimulated hemisphere. Time course analysis revealed that this spindle shift remained significant during the first two sleep cycles. Our results reflect rather a hemispheric asymmetry in the alerting action of light than a use-dependent recovery function of sleep in response to the visual stimulation during prior waking. However, the observed shift in the spindle hemispheric dominance in the occipital cortex may still represent subtle local use-dependent recovery functions during sleep in a frequency range different from the delta range.

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Lopes, M., and S. Roizenblatt. "0937 The Evaluation of Brain Maturation by REM Sleep Analyses During Puberty Using Fast Fourier Transform." Sleep 43, Supplement_1 (April 2020): A356. http://dx.doi.org/10.1093/sleep/zsaa056.933.

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Abstract Introduction Brain maturation has been associated with electroencephalogram (EEG) changes during rapid eye movement (REM) sleep. There is a higher delta power during sleep in the first decade of the human EEG and this fact might be related to puberty period. Most studies assessed EEG during wakefulness and NREM sleep. The aim of this study was to evaluate changes in the REM sleep EEG spectral analysis across puberty. Methods Twenty healthy children were studied. They were divided into two groups: early puberty (n=10, ageranging from 6 to 12) and late puberty (n=10, age= ranging from 13 to 18). Polysomnography was performed in 2 nights, one for adaptation purpose. The Tanner scales were obtained and exclusion criteria were the presence of sleep and daytime complaints at least 14 days before recruitment. Fast Fourier Transform (FFT) was performed in C3-A2 derivation throughout all night. The FFT was calculated in 4s windows and the mean of delta (0.5-2.0 Hz), delta 2 (2.0-4.0 Hz), theta (4.0-8.0 Hz), alpha (8.0 - 12.0 Hz), sigma (12.0-16.0 Hz) and beta (16.0 - 20.0 Hz) were obtained. Results We found differences during NREM and REM sleep between two groups (U-test, p<0.05). In REM sleep, the delta 2 (U-test, p=0.02)and theta power were higher in early puberty group (U-test p=0.04). The delta power correlated negatively with the duration in minutes of stage 1 (rs=-0.46 p<0.05), and the wake time after sleep onset (rs=-0.48, p<0.05) and correlated positively with sleep efficiency (rs=0.45, p<0.05). Theta power during REM sleep also correlated positively with N3 sleep stage (rs=0.45, p<0.05). Conclusion The REM sleep can be an extremely useful biomarker of brain function for future therapeutic protocols. The present results suggest that there are changes in REM sleep EEG throughout puberty, and that they may be related to puberty brain maturation. The hormone therapy may have an action in the REM behavioral Sleep Disorder. Future studies are need to evaluate this hypothesis. Support N/A

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Olbrich, Eckehard, Jens Christian Claussen, and Peter Achermann. "The multiple time scales of sleep dynamics as a challenge for modelling the sleeping brain." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 369, no. 1952 (October 13, 2011): 3884–901. http://dx.doi.org/10.1098/rsta.2011.0082.

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A particular property of the sleeping brain is that it exhibits dynamics on very different time scales ranging from the typical sleep oscillations such as sleep spindles and slow waves that can be observed in electroencephalogram (EEG) segments of several seconds duration over the transitions between the different sleep stages on a time scale of minutes to the dynamical processes involved in sleep regulation with typical time constants in the range of hours. There is an increasing body of work on mathematical and computational models addressing these different dynamics, however, usually considering only processes on a single time scale. In this paper, we review and present a new analysis of the dynamics of human sleep EEG at the different time scales and relate the findings to recent modelling efforts pointing out both the achievements and remaining challenges.

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Sharma, Manish, Jainendra Tiwari, and U. Rajendra Acharya. "Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals." International Journal of Environmental Research and Public Health 18, no. 6 (March 17, 2021): 3087. http://dx.doi.org/10.3390/ijerph18063087.

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Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet’s cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen’s Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen’s Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects.

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Na, Sun Hee, Seung-Hyun Jin, and Soo Yong Kim. "The effects of total sleep deprivation on brain functional organization: Mutual information analysis of waking human EEG." International Journal of Psychophysiology 62, no. 2 (November 2006): 238–42. http://dx.doi.org/10.1016/j.ijpsycho.2006.03.006.

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Korthas, H. T., B. S. Main, A. C. Harvey, E. W. Wicker, S. S. Sloley, and M. P. Burns. "0169 Disruption of Sleep Architecture and Circadian Rhythms Following High Frequency Head Impacts." Sleep 43, Supplement_1 (April 2020): A67. http://dx.doi.org/10.1093/sleep/zsaa056.167.

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Abstract Introduction Mild Traumatic Brain Injury (mTBI) can cause a broad array of behavioral problems including cognitive and emotional deficits. Sleep disturbances including disrupted sleep latency and efficiency are common amongst human mTBI patients. Crucially, sleep plays a key role in hippocampal learning and memory consolidation, yet the contribution of single and repetitive mTBIs influencing sleep related cognitive outcomes remains unclear. Methods To study the effect of repetitive mTBI on sleep and circadian rhythms, C57Bl/6 mice underwent sham or High Frequency-Head Impact (HF-HI, 30 closed head impacts, 5/per day for 6-days) procedures before brains were assessed at 1d, 1m and 2m using a combination of molecular neurobiology (RNA/protein), EEG/ EMG recordings and behavioral analysis. Results HF-HI induces learning and memory deficits in the Barnes and T-Maze at both 1d and 1m post injury, in the absence of axonal injury, inflammation, or protein deposition. Disruptions in circadian mRNA expression was identified at multiple time points post HF-HI. RNA analysis of mouse cortex, hippocampus, and hypothalamus for core circadian rhythm genes (Bmal1, Clock, Cryptochrome 2, Period1 and Period 2) was conducted at 1d and 1m post HF-HI. We found dysregulated expression of these core biological clock genes in these regions at both time points. Furthermore, we find distinct changes to sleep architecture chronically post injury. Animals were implanted with EEG and EMG for monitoring at 2m post injury. EEG and EMG signals were coded for wake, NREM, and REM. One-month post injury, HFHI injured mice showed dysregulated sleep architecture compared to sham mice, while both groups had the same total sleep time. We also demonstrate that HF-HI alters EEG activity in awake animals. Conclusion Overall, our data shows disruptions in both sleep architecture and expression of circadian rhythm genes following HF-HI. This opens up an important avenue of potential therapeutic intervention following injury. Support If deficits in sleep and circadian rhythms can be rescued after mTBI, it may assist in improving symptoms and chronic outcomes after injury.

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Myers, M. M., R. I. Stark, W. P. Fifer, P. G. Grieve, J. Haiken, K. Leung, and K. F. Schulze. "A quantitative method for classification of EEG in the fetal baboon." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 265, no. 3 (September 1, 1993): R706—R714. http://dx.doi.org/10.1152/ajpregu.1993.265.3.r706.

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Electroencephalographic (EEG) activity is used as a primary indicator of sleep states in adults and infants of many species and in the ovine fetus. We recently reported that the baboon fetus exhibits visually discernable patterns of EEG activity. One pattern of activity, characterized by the intermittent presence of repetitive bursts of high-voltage EEG, is indistinguishable from trace alternant (TA). TA is a distinctive pattern of EEG activity found only during early stages of development in primates. TA is the predominant pattern of EEG activity during quiet sleep in human infants < 2 mo of age. The focus of this study was to derive quantitative parameters that would discriminate TA from other activity and then to develop a method for automated categorization of EEG patterns. Results demonstrate that several parameters derived from frequency-domain analyses are related to visually coded EEG states. Among these parameters, high-frequency power (12-24 Hz) and spectral-edge frequency are good discriminators of EEG patterns. This paper describes a new parameter, EEG ratio, computed as spectral power in the rectified EEG within a band that corresponds to the frequency of bursts of activity during TA (0.03-0.20 Hz) divided by power in the 12- to 24-Hz band. This new composite parameter of EEG activity provides a markedly better correlate of visually coded EEG than any of the individual parameters tested. Using cluster analysis, we devised a method for objective minute-by-minute dichotomization of EEG ratio. The method produces results that agree with visual coding of EEG activity 87.1% of the time.(ABSTRACT TRUNCATED AT 250 WORDS)

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Friess, E., H. Tagaya, L. Trachsel, F. Holsboer, and R. Rupprecht. "Progesterone-induced changes in sleep in male subjects." American Journal of Physiology-Endocrinology and Metabolism 272, no. 5 (May 1, 1997): E885—E891. http://dx.doi.org/10.1152/ajpendo.1997.272.5.e885.

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Progesterone administration induces a reduction of the vigilance state in humans during wakefulness. It has been been suggested that this effect is mediated via neuroactive metabolites that interact with the gamma-aminobutyric, acidA (GABAA) receptor complex. To investigate the effects of progesterone administration on the sleep electroencephalogram (EEG) in humans we made polysomnographic recordings, including sleep stage-specific spectral analysis, and concomitantly measured plasma concentrations of progesterone and its GABA-active metabolites 3 alpha-hydroxy-5 alpha-dihydroprogesterone (allopregnanolone) and 3 alpha-hydroxy-5 beta-dihydroprogesterone (pregnanolone) in nine healthy male subjects in a double-blind placebo-controlled crossover study. Progesterone administration at 9:30 PM induced a significant increase in the amount of non-rapid eye movement (REM) sleep. The EEG spectral power during non-REM sleep showed a significant decrease in the slow wave frequency range (0.4-4.3 Hz), whereas the spectral power in the higher frequency range (> 15 Hz) tended to be elevated. Some of the observed changes in sleep architecture and sleep-EEG power spectra are similar to those induced by agonistic modulators of the GABAA receptor complex and appear to be mediated in part via the conversion of progesterone into its GABA-active metabolites.

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Danilenko, Konstantin V., Evgenii Kobelev, Sergei V. Yarosh, Grigorii R. Khazankin, Ivan V. Brack, Polina V. Miroshnikova, and Lyubomir I. Aftanas. "Effectiveness of Visual vs. Acoustic Closed-Loop Stimulation on EEG Power Density during NREM Sleep in Humans." Clocks & Sleep 2, no. 2 (April 30, 2020): 172–81. http://dx.doi.org/10.3390/clockssleep2020014.

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The aim of the study was to investigate whether visual stimuli have the same potency to increase electroencephalography (EEG) delta wave power density during non-rapid eye movement (NREM) sleep as do auditory stimuli that may be practical in the treatment of some sleep disturbances. Nine healthy subjects underwent two polysomnography sessions—adaptation and experimental—with EEG electrodes positioned at Fz–Cz. Individually adjusted auditory (pink noise) and visual (light-emitting diode (LED) red light) paired 50-ms signals were automatically presented via headphones/eye mask during NREM sleep, shortly (0.75–0.90 s) after the EEG wave descended below a preset amplitude threshold (closed-loop in-phase stimulation). The alternately repeated 30-s epochs with stimuli of a given modality (light, sound, or light and sound simultaneously) were preceded and followed by 30-s epochs without stimulation. The number of artifact-free 1.5-min cycles taken in the analysis was such that the cycles with stimuli of different modalities were matched by number of stimuli presented. Acoustic stimuli caused an increase (p < 0.01) of EEG power density in the frequency band 0.5–3.0 Hz (slow waves); the values reverted to baseline at post-stimuli epochs. Light stimuli did not influence EEG slow wave power density (p > 0.01) and did not add to the acoustic stimuli effects. Thus, dim red light presented in a closed-loop in-phase fashion did not influence EEG power density during nocturnal sleep.

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Peters, S. R., and B. Hu. "EVALUATION OF A NOVEL EEG ANALYSIS METHOD WITH POTENTIAL DIAGNOSTIC APPLICATIONS." Clinical & Investigative Medicine 31, no. 4 (August 1, 2008): 20. http://dx.doi.org/10.25011/cim.v31i4.4822.

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Background: Cerebral cortex oscillations as recorded on electroencephalograms involve multiple frequency bands. Phase locking of oscillations of these different frequencies may provide a mechanism by which regions of the brain communicate efficiently. Differences in the character of such phase locking may potentially be a diagnostic tool to differentiate seizure types, as traditional analysis of clinical EEG recordings has seldom considered phase-clocking as a diagnostic indicator. Recently, Canolty et al^1 used a novel metric to quantify cross-frequency phase-amplitude coupling during both spontaneous and induced EEG activity. The technique holds advantages over traditional measures, including easy comparison across trials, robustness to amplitude variation, and simple quantification of preferred phase. Traditional analysis of clinical EEG recordings has seldom considered phase-clocking as a diagnostic indicator. Methods: We adapted the metric of Canolty^1 to perform better with highly rhythmic oscillations, such as those in seizures, by adding multi-segment reshuffling of phase traces. To validate our modified technique, we used artificial sinusoid traces with a known degree of coupling to test the response of our modified analysis method, and to derive empirically, appropriate values for important numerical parameters. Frequency and phase information was acquired with both the Hilbert and wavelet transforms, with similar qualitative results achieved with either. Results: As an initial exploration of diagnostic potential, we applied our metric to field potential data obtained from an anaesthetized rat preparation. We compared the phase-amplitude coupling profiles of sleep oscillations with those of simulated absence seizures and showed consistent differences in the phase amplitude coupling profiles. The data suggest that such differences may be useful in evaluating human seizure data. Conclusions: We conclude that our modified method of data analysis provides an effective approach for measuring normalized phase-amplitude coupling in field potential recordings. Future work will aim to evaluate the possible diagnostic uses of phase-amplitude coupling analysis with data from human seizure patients. Reference: Canolty et al. Science 2006;313:1626. Supported by CIHR, NSERC, and the Health Research Foundation.

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Nakakita, Y., N. Tsuchimoto, Y. Takata, and T. Nakamura. "Effect of dietary heat-killed Lactobacillus brevis SBC8803 (SBL88™) on sleep: a non-randomised, double blind, placebo-controlled, and crossover pilot study." Beneficial Microbes 7, no. 4 (September 1, 2016): 501–9. http://dx.doi.org/10.3920/bm2015.0118.

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We previously reported that dietary heat-killed Lactobacillus brevis SBC8803 affects sleep rhythms in mice. The present study evaluated the effect of consumption of heat-killed SBC8803 on sleep architecture in humans. A non-randomised, placebo-controlled, double blind, and crossover pilot study was conducted using volunteers who scored at a slightly high level (i.e. ≥6) on the Athens Insomnia Scale (AIS). Male subjects (n=17; age 41-69 y) consumed placebo or SBC8803 capsules (25 mg/day of heat-killed SBC8803) for 10 days. Electroencephalograms (EEG) were recorded using a mobile, one-channel system, providing objective data on sleep. Subjects’ sleep journals and administration of the AIS provided subjective data on sleep. Three subjects were excluded from the statistical analysis. Analysis of the remaining 14 volunteers revealed no significant differences between placebo and SBC8803 consumption in either the AIS or the sleep EEG. The sleep journals revealed an improvement in ‘waking’ for the SBC8803 consumption periods (P=0.047), and there was a marginally significant effect on ‘drowsiness during the following day’ (P=0.067). Effects on the EEG delta power value (μV2/min) were revealed by a stratified analysis based on age, AIS, and the Beck Depression Inventory (BDI). Specifically, effects were found among subjects in their 40s who consumed the SBC8803 capsules (P=0.049) and among subjects with a BDI score less than the all-subjects average (13.3) (P=0.045). A marginally significant effect was found among subjects with an AIS score less than the all-subjects average (11.6) (P=0.065). The delta power value of 5 subjects with both BDI and AIS scores less than the average increased significantly (P=0.017). While the number of subjects was limited, a beneficial effect on sleep due to consumption of heat-killed L. brevis SBC8803 was found in subjects with slightly challenged sleep.

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Nesterenko, I. "Sleep Physiology and Dreams Subjective Meaning." Herald of Kiev Institute of Business and Technology 39, no. 1 (March 28, 2019): 44–48. http://dx.doi.org/10.37203/kibit.2019.39.09.

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Earlier, scientists believed that sleep is necessary for the "rest" of brain neurons, and therefore sleep should be characterized by a decrease in the activity of brain neurons during this period. However, studies of the electrical activity of individual brain neurons during sleep have shown that during sleep, overall, there is no decrease in the average frequency of neuronal activity compared to the state of restful wakefulness. Currently, sleep research and diagnosis of its pathologies are performed using polysomnography, a system of recording brain activity (EEG), eye movements, muscle activity or skeletal muscle activation (EMG), and heart rate (ECG). During sleep, the metabolic processes in the cerebral cortex do not fall (slow sleep phase); as one would expect, but instead they grow (in the fast-sleep phase), resulting in the sleeping person's brain consuming more oxygen than the human being in a state of alertness. In general, a person's dream has a proper cyclic organization. Electroencephalographic analysis of night sleep allows distinguishing five stages. The first four refer to the slow phase of sleep, the fifth to the fast. The peculiarity of the interpretation of dreams is the first science known by Z. Freud. His theory has a reverse temporal direction toward childhood experiences and childhood suppressed desires. In the Jungian approach, the overall function of dreams is to try to restore our mental balance through the production of dream material, which restores - in a very delicate way - a wholesome mental balance. In the framework of Gestalt Therapy by F. Perls he believed that in order to understand the meaning of dreams, it was better not to interpret it. Given that dreaming is a projection where all the actors and objects that appear in it are the dreamer, it is more appropriate to find feelings about the objects and subjects of sleep. Therefore, it can be argued that the human brain is active during sleep, although this activity is qualitatively different than during the state, and in different stages of sleep has its specificity. Since the formation and development of Freud's views, dreams have been recognized by psychotherapists as an essential key on the path from unconscious material to the achievement of human integrity.

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Simeoni, Ricardo. "A New Approach to High-Order Electroencephalogram Phase Analysis Details the Mathematical Mechanisms of Central Nervous System Impulse Encoding." UNET JOSS: Journal of Science and Society 1, no. 1 (March 1, 2021): 1–34. http://dx.doi.org/10.52042/unetjoss010101.

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This paper presents a new electroencephalogram (EEG) analysis technique which is applied to example EEGs pertaining to nine human subjects and a broad spectrum of clinical scenarios. While focusing on technique physical efficacy, the paper also paves the way for future clinically-focused studies with revelations of several quantified and detailed findings in relation to high-order central nervous system communicative impulse encoding akin to a sophisticated form of phase-shift keying. The fact that fine encoding details are extracted with confidence from a seemingly modest EEG set supports the paper’s position that vast amounts of accessible information currently goes unrecognised by conventional EEG analysis. The technique commences with high resolution Fourier analysis being twice applied to an EEG, providing newly-identified harmonics. Except for deep sleep where harmonic phase, φ, behaviour becomes highly linear, φ transitional values, ∆φ, measured between harmonics of progressively increasing order are found to cluster rather than follow a normal distribution (e.g., χ2 = 303, df = 12, p < 0.001). Clustering is categorised into ten Families for which many separations between ∆φ values are writable in terms of k = j/4 or j/3 (j = 1, 2, 3 ...), with a preference for k = j/2 (χ2 = 77, df = 1, p < 0.001), amounts of a Family-specific quantum increment value, α∆φ. A parabolic relationship (r > 0.9999, p < 0.001) exists between α∆φ (and the parabola minimum associates with an additional inter-Family or universal quantum increment value, αmin). Ratios of α∆φ typically align within ± 0.5% of simple common fractions (95% CI).

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Chapotot, Florian, Claude Gronfier, Christophe Jouny, Alain Muzet, and Gabrielle Brandenberger. "Cortisol Secretion Is Related to Electroencephalographic Alertness in Human Subjects during Daytime Wakefulness1." Journal of Clinical Endocrinology & Metabolism 83, no. 12 (December 1, 1998): 4263–68. http://dx.doi.org/10.1210/jcem.83.12.5326.

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To determine whether human hypothalamo-pituitary-adrenal axis activity is related to the alertness level during wakefulness, 10 healthy young men were studied under resting conditions in the daytime (0900–1800 h) after an 8-h nighttime sleep (2300–0700 h). A serial 70-sec gaze fixation task was required every 10 min throughout the daytime experimental session. The corresponding waking electroencephalographic (EEG) segments were submitted to quantitative spectral analysis, from which EEG β activity (absolute power density in the 13–35 Hz frequency band), an index of central alertness, was computed. Blood was collected continuously through an indwelling venous catheter and sampled at 10-min intervals. Plasma cortisol concentrations were measured by RIA, and the corresponding secretory rates were determined by a deconvolution procedure. Analysis of individual profiles demonstrated a declining tendency for EEG β activity and cortisol secretory rate, with an overall temporal relationship indicated by positive and significant cross-correlation coefficients between the two variables in all subjects (average r= 0.565, P < 0.001). Changes in cortisol secretion lagged behind fluctuations in EEG β activity, with an average delay of 10 min for all the subjects. On the average, 4.6 ± 0.4 cortisol secretory pulses and 4.9 ± 0.5 peaks in EEG β activity were identified by a detection algorithm. A significant, although not systematic, association between the episodes in the two variables was found: 44% of the peaks in EEG β activity (relative amplitude, near 125%; P < 0.001) occurred during an ascending phase of cortisol secretion, cortisol secretory rates increasing by 40% (P < 0.01) 10-min after peaks in EEG β activity. However, no significant change in EEG β activity was observed during the period from 50 min before to 50 min after pulses in cortisol secretion. In conclusion, the present study describes a temporal coupling between cortisol release and central alertness, as reflected in the waking EEGβ activity. These findings suggest the existence of connections between the mechanisms involved in the control of hypothalamo-pituitary-adrenal activity and the activation processes of the brain, which undergoes varying degrees of alertness throughout daytime wakefulness.

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Pace, Marta, Ilaria Colombi, Matteo Falappa, Andrea Freschi, Mojtaba Bandarabadi, Andrea Armirotti, Blanco María Encarnación, et al. "Loss of Snord116 alters cortical neuronal activity in mice: a preclinical investigation of Prader–Willi syndrome." Human Molecular Genetics 29, no. 12 (May 18, 2020): 2051–64. http://dx.doi.org/10.1093/hmg/ddaa084.

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Abstract Prader–Willi syndrome (PWS) is a neurodevelopmental disorder that is characterized by metabolic alteration and sleep abnormalities mostly related to rapid eye movement (REM) sleep disturbances. The disease is caused by genomic imprinting defects that are inherited through the paternal line. Among the genes located in the PWS region on chromosome 15 (15q11-q13), small nucleolar RNA 116 (Snord116) has been previously associated with intrusions of REM sleep into wakefulness in humans and mice. Here, we further explore sleep regulation of PWS by reporting a study with PWScrm+/p− mouse line, which carries a paternal deletion of Snord116. We focused our study on both macrostructural electrophysiological components of sleep, distributed among REMs and nonrapid eye movements. Of note, here, we study a novel electroencephalography (EEG) graphoelements of sleep for mouse studies, the well-known spindles. EEG biomarkers are often linked to the functional properties of cortical neurons and can be instrumental in translational studies. Thus, to better understand specific properties, we isolated and characterized the intrinsic activity of cortical neurons using in vitro microelectrode array. Our results confirm that the loss of Snord116 gene in mice influences specific properties of REM sleep, such as theta rhythms and, for the first time, the organization of REM episodes throughout sleep–wake cycles. Moreover, the analysis of sleep spindles present novel specific phenotype in PWS mice, indicating that a new catalog of sleep biomarkers can be informative in preclinical studies of PWS.

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Witte, H., P. Putsche, M. Eiselt, K. Hoffmann, B. Schack, M. Arnold, and H. Jäger. "Analysis of the interrelations between a low-frequency and a high-frequency signal component in human neonatal EEG during quiet sleep." Neuroscience Letters 236, no. 3 (November 1997): 175–79. http://dx.doi.org/10.1016/s0304-3940(97)00751-9.

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Wu, Dan, Chaoyi Li, Yu Yin, Changzheng Zhou, and Dezhong Yao. "Music Composition from the Brain Signal: Representing the Mental State by Music." Computational Intelligence and Neuroscience 2010 (2010): 1–6. http://dx.doi.org/10.1155/2010/267671.

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This paper proposes a method to translate human EEG into music, so as to represent mental state by music. The arousal levels of the brain mental state and music emotion are implicitly used as the bridge between the mind world and the music. The arousal level of the brain is based on the EEG features extracted mainly by wavelet analysis, and the music arousal level is related to the musical parameters such as pitch, tempo, rhythm, and tonality. While composing, some music principles (harmonics and structure) were taken into consideration. With EEGs during various sleep stages as an example, the music generated from them had different patterns of pitch, rhythm, and tonality. 35 volunteers listened to the music pieces, and significant difference in music arousal levels was found. It implied that different mental states may be identified by the corresponding music, and so the music from EEG may be a potential tool for EEG monitoring, biofeedback therapy, and so forth.

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Jeong, A.-Hyun, Jisu Hwang, Kyungae Jo, Singeun Kim, Yejin Ahn, Hyung Joo Suh, and Hyeon-Son Choi. "Fermented Gamma Aminobutyric Acid Improves Sleep Behaviors in Fruit Flies and Rodent Models." International Journal of Molecular Sciences 22, no. 7 (March 29, 2021): 3537. http://dx.doi.org/10.3390/ijms22073537.

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The aim of this study was to investigate the effect of Lactobacillus brevis-fermented γ-aminobutyric acid (LB-GABA) on sleep behaviors in invertebrate and vertebrate models. In Drosophila melanogaster, LB-GABA-treated group showed an 8–9%-longer sleep duration than normal group did. LB-GABA-treated group also showed a 46.7% lower level of nighttime activity with a longer (11%) sleep duration under caffeine-induced arousal conditions. The LB-GABA-mediated inhibition of activity was confirmed as a reduction of total movement of flies using a video tracking system. In the pentobarbital-induced sleep test in mice, LB-GABA (100 mg/kg) shortened the time of onset of sleep by 32.2% and extended sleeping time by 59%. In addition, mRNA and protein level of GABAergic/Serotonergic neurotransmitters were upregulated following treatment with LB-GABA (2.0%). In particular, intestine- and brain-derived GABAA protein levels were increased by sevenfold and fivefold, respectively. The electroencephalography (EEG) analysis in rats showed that LB-GABA significantly increased non-rapid eye movement (NREM) (53%) with the increase in theta (θ, 59%) and delta (δ, 63%) waves, leading to longer sleep time (35%), under caffeine-induced insomnia conditions. LB-GABA showed a dose-dependent agonist activity on human GABAA receptor with a half-maximal effective concentration (EC50) of 3.44 µg/mL in human embryonic kidney 293 (HEK293) cells.

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Journal articles on the topic 'Human sleep EEG analysis'

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