Journal articles: 'Spectral analysis of human sleep EEG'

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Lina, Jean-Marc, Emma O’Callaghan, and Valérie Mongrain. "Scale-Free Dynamics of the Mouse Wakefulness and Sleep Electroencephalogram Quantified Using Wavelet-Leaders." Clocks & Sleep 1, no. 1 (October 20, 2018): 50–64. http://dx.doi.org/10.3390/clockssleep1010006.

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Scale-free analysis of brain activity reveals a complexity of synchronous neuronal firing which is different from that assessed using classic rhythmic quantifications such as spectral analysis of the electroencephalogram (EEG). In humans, scale-free activity of the EEG depends on the behavioral state and reflects cognitive processes. We aimed to verify if fractal patterns of the mouse EEG also show variations with behavioral states and topography, and to identify molecular determinants of brain scale-free activity using the ‘multifractal formalism’ (Wavelet-Leaders). We found that scale-free activity was more anti-persistent (i.e., more different between time scales) during wakefulness, less anti-persistent (i.e., less different between time scales) during non-rapid eye movement sleep, and generally intermediate during rapid eye movement sleep. The scale-invariance of the frontal/motor cerebral cortex was generally more anti-persistent than that of the posterior cortex, and scale-invariance during wakefulness was strongly modulated by time of day and the absence of the synaptic protein Neuroligin-1. Our results expose that the complexity of the scale-free pattern of organized neuronal firing depends on behavioral state in mice, and that patterns expressed during wakefulness are modulated by one synaptic component.

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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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Posada-Quintero, Hugo F., Jeffrey B. Bolkhovsky, Michael Qin, and Ki H. Chon. "Human Performance Deterioration Due to Prolonged Wakefulness Can Be Accurately Detected Using Time-Varying Spectral Analysis of Electrodermal Activity." Human Factors: The Journal of the Human Factors and Ergonomics Society 60, no. 7 (June 15, 2018): 1035–47. http://dx.doi.org/10.1177/0018720818781196.

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Objective: The aim was to determine if indices of the autonomic nervous system (ANS), derived from the electrodermal activity (EDA) and electrocardiogram (ECG), could be used to detect deterioration in human cognitive performance on healthy participants during 24-hour sleep deprivation. Background: The ANS is highly sensitive to sleep deprivation. Methods: Twenty-five participants performed a desktop-computer-based version of the psychomotor vigilance task (PVT) every 2 hours. Simultaneously with reaction time (RT) and false starts from PVT, we measured EDA and ECG. We derived heart rate variability (HRV) measures from ECG recordings to assess dynamics of the ANS. Based on RT values, average reaction time (avRT), minor lapses (RT > 500 ms), and major lapses (RT > 1 s) were computed as indices of performance, along with the total number of false starts. Results: Performance measurement results were consistent with the literature. The skin conductance level, the power spectral index, and the high-frequency components of HRV were not significantly correlated to the indices of performance. The nonspecific skin conductance responses, the time-varying index of EDA (TVSymp), and normalized low-frequency components of HRV were significantly correlated to indices of performance ( p < 0.05). TVSymp exhibited the highest correlation to avRT (–0.92), major lapses (–0.85), and minor lapses (–0.83). Conclusion: We conclude that indices that account for high-frequency dynamics in the EDA, specifically the time-varying approach, constitute a valuable tool for understanding the changes in the autonomic nervous system. Application: This can be used to detect the adverse effects of prolonged wakefulness on human performance.

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Dube, J., J. Lina, S. Soltani, S. Chauvette, O. Bukhtiyarova, J. Carrier, and I. Timofeev. "0354 Age-Related Spectral Changes in NREM And REM Sleep in Mice are Global and Not Local." Sleep 43, Supplement_1 (April 2020): A134. http://dx.doi.org/10.1093/sleep/zsaa056.351.

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Abstract Introduction Brain topography modulates age-related changes in the human sleep electroencephalogram, which are linked with differences in integrity of specific cortical areas and may reflect local changes in sleep homeostasis. In mice, there is conflicting evidence regarding the topography of age-related changes for NREM and REM sleep. To disambiguate this issue, we investigated in mice the topography of age-related spectral differences for REM and NREM sleep. Methods LFP electrodes were implanted in 5 cortical areas and in the hippocampus of 17 C57/BL6 mice (8 young and 9 old, mean age = 7.5 and 16 months). Mice LFPs were recorded for a week and states of vigilance were semi-automatically detected in light and dark periods (12h-12h). Spectral analysis was run on 4s windows. Values were averaged for each electrode and in each period of the light/dark cycle in REM/NREM sleep for slow delta (0.25-2Hz), delta (2-4Hz), theta (4-8Hz), sigma (10-16Hz) and ripples (150-200Hz). Mixed models were computed separately for REM and NREM in dark and light period, with age as group factor and electrode and frequency as repeated factors. Results Two-way interactions were found between age and frequency and between electrode and frequency, for NREM and REM in dark and light periods. Each frequency band, except ripples, showed a topographical signature in NREM and REM (e.g. higher power in anterior compared to posterior areas for delta band in NREM sleep). These relative patterns did not change in older mice, but global changes occurred on all electrodes: in older mice, delta power was globally higher in NREM and REM sleep whereas sigma power was lower in REM sleep. Conclusion Age-related changes in spectral power of sleeping mice do not vary according to brain topography as in humans. Sleep deprivation studies are needed to investigate whether age is associated with global changes in sleep homeostasis in mice. Support This work has been supported by the Quebec Fonds de Recherche Nature et Technologies (FQRNT).

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Staba, Richard J., Charles L. Wilson, Anatol Bragin, Itzhak Fried, and Jerome Engel. "Quantitative Analysis of High-Frequency Oscillations (80–500 Hz) Recorded in Human Epileptic Hippocampus and Entorhinal Cortex." Journal of Neurophysiology 88, no. 4 (October 1, 2002): 1743–52. http://dx.doi.org/10.1152/jn.2002.88.4.1743.

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High-frequency oscillations (100–200 Hz), termed ripples, have been identified in hippocampal (Hip) and entorhinal cortical (EC) areas of rodents and humans. In contrast, higher-frequency oscillations (250–500 Hz), termed fast ripples (FR), have been described in seizure-generating limbic areas of rodents made epileptic by intrahippocampal injection of kainic acid and observed in humans ipsilateral to areas of seizure initiation. However, quantitative studies supporting the existence of two spectrally distinct oscillatory events have not been carried out in humans nor has the preferential appearance of FR within seizure generating areas received statistical evaluation based on analysis of a large sample of oscillatory events. Interictal oscillations within the bandwidth of 80–500 Hz were detected in Hip and EC areas of patients with mesial temporal lobe epilepsy using wideband EEG recorded during non-rapid eye-movement sleep from chronically implanted depth electrodes. Power spectral analysis showed that oscillations detected from Hip and EC areas were composed of two spectrally distinct groups. The lower-frequency ripple group was defined by a frequency of 96 ± 14 Hz (median ± width), while the higher-frequency FR group had a frequency of 262 ± 59 Hz. FR oscillations were significantly shorter in duration compared with ripple oscillations ( P < 0.0001). In regard to the occurrence of FR and ripples in epileptic Hip and EC, the mean ratio of the number of FR to ripples generated in areas ipsilateral to seizure onset was significantly higher compared with the mean ratio of FR to ripple generation from contralateral areas ( P = 0.008). Furthermore, sites ipsilateral to seizure onset with hippocampal atrophy had significantly higher ratios compared with sites contralateral to both seizure onset and hippocampal atrophy ( P = 0.001). These data provide compelling quantitative and statistical evidence for the existence of two spectrally distinct groups of limbic oscillations that have frequency and duration characteristics similar to those previously described in epileptic rat and human Hip and EC. The strong association between FR and regions of seizure initiation supports the view that FR reflects pathological hypersynchronous events crucially associated with seizure genesis.

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Chandrasekaran, R., R. J. Hemalath, E. Anand Kumar, S. Murali, T. R. Thamizhvani, and Soumya Y.K. "Spectral analysis of polysomnography." International Journal of Engineering & Technology 7, no. 2.25 (May 3, 2018): 86. http://dx.doi.org/10.14419/ijet.v7i2.25.16565.

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The Polysomnography (PSG) is the most commonly used test in the diagnosis of OSAS – Obstructive Sleep Apnea Syndrome. PSG signals consist of simultaneous recording of multiple physiological parameters related to sleep and wakefulness. PSG is used to evaluate abnormalities of sleep and or wakefulness and other physiological disorders that have an impact on or related to sleep and or wakefulness. In this paper, we propped an idea of detection of insomnia based on frequency spectral analysis of PSG signals. The PSG signals consist of EMG of the chin, EEG taken from various lobes, respiratory signal, EOG signals, Temporary rectal signal and ECG signal. From all these physiological parameters, the Spectral analysis of EOG (horizontal), EEG FPZ-CZ and PZ-OZ [EEG 10-20 electrodes paced on midline FPZ,CZ,OZ channels]signals are analyzed and the mean, variance, standard deviation, RMS value and SNR features of the signal are extracted. The proposed methodology is applied to the male as well as female subjects at the age group of 30-40 years. The difference of the frequency range taken at respective intervals of time is noted and compared.

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Prerau, Michael J., Ritchie E. Brown, Matt T. Bianchi, Jeffrey M. Ellenbogen, and Patrick L. Purdon. "Sleep Neurophysiological Dynamics Through the Lens of Multitaper Spectral Analysis." Physiology 32, no. 1 (January 2017): 60–92. http://dx.doi.org/10.1152/physiol.00062.2015.

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During sleep, cortical and subcortical structures within the brain engage in highly structured oscillatory dynamics that can be observed in the electroencephalogram (EEG). The ability to accurately describe changes in sleep state from these oscillations has thus been a major goal of sleep medicine. While numerous studies over the past 50 years have shown sleep to be a continuous, multifocal, dynamic process, long-standing clinical practice categorizes sleep EEG into discrete stages through visual inspection of 30-s epochs. By representing sleep as a coarsely discretized progression of stages, vital neurophysiological information on the dynamic interplay between sleep and arousal is lost. However, by using principled time-frequency spectral analysis methods, the rich dynamics of the sleep EEG are immediately visible—elegantly depicted and quantified at time scales ranging from a full night down to individual microevents. In this paper, we review the neurophysiology of sleep through this lens of dynamic spectral analysis. We begin by reviewing spectral estimation techniques traditionally used in sleep EEG analysis and introduce multitaper spectral analysis, a method that makes EEG spectral estimates clearer and more accurate than traditional approaches. Through the lens of the multitaper spectrogram, we review the oscillations and mechanisms underlying the traditional sleep stages. In doing so, we will demonstrate how multitaper spectral analysis makes the oscillatory structure of traditional sleep states instantaneously visible, closely paralleling the traditional hypnogram, but with a richness of information that suggests novel insights into the neural mechanisms of sleep, as well as novel clinical and research applications.

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Dijk, Derk Jan, and Serge Daan. "Sleep EEG spectral analysis in a diurnal rodent:Eutamias sibiricus." Journal of Comparative Physiology A 165, no. 2 (March 1989): 205–15. http://dx.doi.org/10.1007/bf00619195.

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Shen, Y., E. Olbrich, P. Achermann, and P. F. Meier. "Dimensional complexity and spectral properties of the human sleep EEG." Clinical Neurophysiology 114, no. 2 (February 2003): 199–209. http://dx.doi.org/10.1016/s1388-2457(02)00338-3.

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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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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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Huh, K., K. J. Meador, D. W. Loring, H. S. Taylor, D. W. King, B. B. Gallagher, J. R. Smith, and H. F. Flanigin. "Spectral analysis of human hippocampal EEG: Behavioral activation." Journal of Epilepsy 1, no. 3 (January 1988): 151–55. http://dx.doi.org/10.1016/s0896-6974(88)80120-8.

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Morisson, F., G. Lavigne, D. Petit, T. Nielsen, J. Malo, and J. Montplaisir. "Spectral analysis of wakefulness and REM sleep EEG in patients with sleep apnoea syndrome." European Respiratory Journal 11, no. 5 (May 1, 1998): 1135–40. http://dx.doi.org/10.1183/09031936.98.11051135.

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Poulin, Julie, Emmanuel Stip, and Roger Godbout. "REM sleep EEG spectral analysis in patients with first-episode schizophrenia." Journal of Psychiatric Research 42, no. 13 (October 2008): 1086–93. http://dx.doi.org/10.1016/j.jpsychires.2008.01.003.

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Zhang, Lin, Jonathan Samet, Brian Caffo, Isaac Bankman, and Naresh M. Punjabi. "Power Spectral Analysis of EEG Activity During Sleep in Cigarette Smokers." Chest 133, no. 2 (February 2008): 427–32. http://dx.doi.org/10.1378/chest.07-1190.

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Müller, Caroline, Peter Achermann, Matthias Bischof, Arto C. Nirkko, Corinne Roth, and Claudio L. Bassetti. "Visual and Spectral Analysis of Sleep EEG in Acute Hemispheric Stroke." European Neurology 48, no. 3 (2002): 164–71. http://dx.doi.org/10.1159/000065509.

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Hornyak, Magdolna, Bernd Feige, Ulrich Voderholzer, and Dieter Riemann. "Spectral analysis of sleep EEG in patients with restless legs syndrome." Clinical Neurophysiology 116, no. 6 (June 2005): 1265–72. http://dx.doi.org/10.1016/j.clinph.2005.02.004.

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Hasan, Md Junayed, Dongkoo Shon, Kichang Im, Hyun-Kyun Choi, Dae-Seung Yoo, and Jong-Myon Kim. "Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals." Applied Sciences 10, no. 21 (October 29, 2020): 7639. http://dx.doi.org/10.3390/app10217639.

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This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively.

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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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Buysse, Daniel J., Anne Germain, Martica L. Hall, Douglas E. Moul, Eric A. Nofzinger, Amy Begley, Cindy L. Ehlers, Wesley Thompson, and David J. Kupfer. "EEG Spectral Analysis in Primary Insomnia: NREM Period Effects and Sex Differences." Sleep 31, no. 12 (December 2008): 1673–82. http://dx.doi.org/10.1093/sleep/31.12.1673.

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Hiremath, Basavaraj, Natarajan Sriraam, B. R. Purnima, Nithin N. S., Suresh Babu Venkatasamy, and Megha Narayanan. "EEG-Based Demarcation of Yogic and Non-Yogic Sleep Patterns Using Power Spectral Analysis." International Journal of E-Health and Medical Communications 12, no. 6 (November 2021): 1–18. http://dx.doi.org/10.4018/ijehmc.20211101.oa2.

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Electroencephalogram (EEG) signals resulting from recordings of polysomnography play a significant role in determining the changes in physiology and behavior during sleep. This study aims at demarcating the sleep patterns of yogic and non-yogic subjects. Frequency domain features based on power spectral density methods were explored in this study. The EEG recordings were segmented into 1s and 0.5s. EEG patterns with four windowing scheme overlaps (0%, 50%, 60% and 75%) to ensure stationarity of the signal in order to investigate the effect of the pre-processing stage. In order to recognize the yoga and non-yoga group through N3 sleep stage, non-linear KNN classifier was introduced and performance was evaluated in terms of sensitivity and specificity. The experimental results show that modified covariance PSD estimate is the best method in classifying the sleep stage N3 of yogic and non-yogic subjects with 95% confidence interval, sensitivity, specificity and accuracy of 97.3%, 98% and 97%, respectively.

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Beersma, D. G. M., and R. H. van den Hoofdakker. "Spectral analysis of the sleep EEG of depressed patients before and after total sleep deprivation." Acta Neuropsychiatrica 7, no. 2 (June 1995): 38–40. http://dx.doi.org/10.1017/s0924270800037510.

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Sleep electroencephalography in depressed patients reveals many signs of disrupted sleep, like long sleep latency, frequent awakenings, reduced amounts of time spent in the sleep stages 3 and 4, and early morning wakefulness. Upon total deprivation of sleep for one night, many patients experience an unexpected alleviation of their depression, which usually lasts until the subsequent sleep period. Attempts have been made to explain these changes of mood to result from induced changes in sleep physiological mechanisms. Such attempts can roughly be categorized in two classes. One class of hypotheses concerns proposed disturbances in circadian sleep control (i.e. the timing of sleep is inappropriately controlled), the other class concerns postulated disrupted homeostatic sleep control (i.e. the intensity of sleep is inappropriately controlled). For both types of theoretical approaches data have been published which are consistent with the hypotheses as well as data which are not.

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Kim, H., M. Prerau, and S. Redline. "0350 Characterizing Continuous Changes in Spectral Dynamics of Sleep EEG as a Function of Age." Sleep 43, Supplement_1 (April 2020): A133. http://dx.doi.org/10.1093/sleep/zsaa056.347.

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Abstract Introduction Sleep is a continuous and dynamic physiological process. Current research practice, however, limits our ability to observe electroencephalography (EEG) oscillation dynamics by breaking sleep into discrete stages. In this study, we propose a novel quantitative framework that represents population-level changes in sleep EEG spectral dynamics as a function of age, preserving the information-rich spectral dynamics of sleep data. Rather than relying on sleep stages, our approach uses slow-oscillation power (SO-power) as an objective, continuous-valued correlate of sleep depth. Methods We analyzed the EEG signal (Fz-Cz, 256 Hz sampling rate) from a subset of the Multi-Ethnic Study of Atherosclerosis (MESA) study participants (n = 2056, 53.6% female, age: mean 69.37 ± 9.12, range 54 - 94) who underwent polysomnography. For each subject, we computed the sleep EEG multitaper spectrogram and extracted the total baseline-normalized SO-power (0.1 - 1.5 Hz). We next computed mean EEG spectral power as a function of SO-power, which we then tracked across all subjects as a function of age in sliding windows. Results The population analysis shows apparent, continuous changes in time-frequency domain features of the EEG as a function of a sleep depth along with age, that would be otherwise lost in traditional analyses. Moreover, by analyzing the directionality of the SO-power, we show that there is no apparent difference in neural activity during deepening sleep and lightening sleep; thus EEG sleep state is likely non-directional. Conclusion Our results show that state-based sleep dynamics of the EEG power spectrum can comprehensively be represented using SO-power as a surrogate of sleep depth. This representation identifies state-based activity independent of the temporal evolution of sleep architecture. As such, it is a powerful tool for analysis and phenotyping of EEG activity in large cohorts. Support The Biomedical Global Talent Nurturing Program through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (HI19C1065) to HK, National Institute of Neurological Disorders and Stroke (NINDS, R01 NS-096177) to MP.

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Zhang, Cheng, Kun Chen, Guangfa Wang, Jue Zhang, and Jing Ma. "Effects of Continuous Positive Airway Pressure on Sleep EEG Characteristics in Patients with Primary Central Sleep Apnea Syndrome." Canadian Respiratory Journal 2021 (April 22, 2021): 1–6. http://dx.doi.org/10.1155/2021/6657724.

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This study aimed to investigate the effects of continuous positive airway pressure (CPAP) on the electroencephalographic (EEG) characteristics of patients with primary central sleep apnea syndrome (CSAS). Nine patients with primary CSAS were enrolled in this study. The raw sleep EEG data were analyzed based on two main factors: fractal dimension (FD) and zero-crossing rate of detrended FD. Additionally, conventional EEG spectral analysis in the delta, theta, alpha, and beta bands was conducted using a fast Fourier transform. The FD in patients with primary CSAS who underwent CPAP treatment was significantly decreased during nonrapid eye movement (NREM) sleep but increased during rapid eye movement (REM) sleep ( p < 0.05 ). Regarding the EEG spectral analysis, the alpha power increased, while the delta/alpha ratio decreased during REM sleep in patients with CSAS ( p < 0.05 ). In conclusion, CPAP treatment can reduce FD in NREM sleep and increase FD during REM sleep in patients with primary CSAS. FD may be used as a new biomarker of EEG stability and improvement in brain function after CPAP treatment for primary CSAS.

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Kloepfer, C., B. Feige, C. Nissen, and D. Riemann. "067 MEMORY CONSOLIDATION DURING SLEEP AND EEG SPECTRAL ANALYSIS IN PRIMARY INSOMNIA." Sleep Medicine 10 (December 2009): S18—S19. http://dx.doi.org/10.1016/s1389-9457(09)70069-3.

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Alloway, Christi E. D., Robert D. Ogilvie, and Colin M. Shapiro. "EEG Spectral Analysis of the Sleep-onset Period in Narcoleptics and Normal Sleepers." Sleep 22, no. 2 (March 1999): 191–203. http://dx.doi.org/10.1093/sleep/22.2.191.

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Yang, Joel S. C., Christian L. Nicholas, Gillian M. Nixon, Margot J. Davey, Vicki Anderson, Adrian M. Walker, John A. Trinder, and Rosemary S. C. Horne. "Determining Sleep Quality in Children with Sleep Disordered Breathing: EEG Spectral Analysis Compared with Conventional Polysomnography." Sleep 33, no. 9 (September 2010): 1165–72. http://dx.doi.org/10.1093/sleep/33.9.1165.

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Ventouras, Erricos M., Periklis Y. Ktonas, Hara Tsekou, Thomas Paparrigopoulos, Ioannis Kalatzis, and Constantin R. Soldatos. "Independent Component Analysis for Source Localization of EEG Sleep Spindle Components." Computational Intelligence and Neuroscience 2010 (2010): 1–12. http://dx.doi.org/10.1155/2010/329436.

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Sleep spindles are bursts of sleep electroencephalogram (EEG) quasirhythmic activity within the frequency band of 11–16 Hz, characterized by progressively increasing, then gradually decreasing amplitude. The purpose of the present study was to process sleep spindles with Independent Component Analysis (ICA) in order to investigate the possibility of extracting, through visual analysis of the spindle EEG and visual selection of Independent Components (ICs), spindle “components” (SCs) corresponding to separate EEG activity patterns during a spindle, and to investigate the intracranial current sources underlying these SCs. Current source analysis using Low-Resolution Brain Electromagnetic Tomography (LORETA) was applied to the original and the ICA-reconstructed EEGs. Results indicated that SCs can be extracted by reconstructing the EEG through back-projection of separate groups of ICs, based on a temporal and spectral analysis of ICs. The intracranial current sources related to the SCs were found to be spatially stable during the time evolution of the sleep spindles.

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Dijk, Derk Jan, Domien G. M. Beersma, and Gerda M. Bloem. "Sex Differences in the Sleep EEG of Young Adults: Visual Scoring and Spectral Analysis." Sleep 12, no. 6 (November 1989): 500–507. http://dx.doi.org/10.1093/sleep/12.6.500.

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Farag, Amr F., Shereen M. El-Metwally, and Ahmed A. Morsy. "A Sleep Scoring System Using EEG Combined Spectral and Detrended Fluctuation Analysis Features." Journal of Biomedical Science and Engineering 07, no. 08 (2014): 584–92. http://dx.doi.org/10.4236/jbise.2014.78059.

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Armitage, Roseanne, Carol Landis, Robert Hoffmann, Martha Lentz, Nathaniel Watson, Jack Goldberg, and Dedra Buchwald. "Power spectral analysis of sleep EEG in twins discordant for chronic fatigue syndrome." Journal of Psychosomatic Research 66, no. 1 (January 2009): 51–57. http://dx.doi.org/10.1016/j.jpsychores.2008.08.004.

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VASKOJR, R., D. BRUNNER, J. MONAHAN, JACKDOMAN, J. BOSTON, A. ELJAROUDI, J. MIEWALD, D. BUYSSE, C. REYNOLDSIII, and D. KUPFER. "Power spectral analysis of EEG in a multiple-bedroom, multiple-polygraph sleep laboratory." International Journal of Medical Informatics 46, no. 3 (October 1997): 175–84. http://dx.doi.org/10.1016/s1386-5056(97)00064-6.

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PARSONS, L. C., L. J. CROSBY, M. PERLIS, T. BRITT, and P. JONES. "Longitudinal Sleep EEG Power Spectral Analysis Studies in Adolescents with Minor Head Injury." Journal of Neurotrauma 14, no. 8 (August 1997): 549–59. http://dx.doi.org/10.1089/neu.1997.14.549.

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Xiromeritis, Athanasios G., Apostolia A. Hatziefthimiou, Georgios M. Hadjigeorgiou, Konstantinos I. Gourgoulianis, Dimitra N. Anagnostopoulou, and Nikiforos V. Angelopoulos. "Quantitative spectral analysis of vigilance EEG in patients with obstructive sleep apnoea syndrome." Sleep and Breathing 15, no. 1 (February 20, 2010): 121–28. http://dx.doi.org/10.1007/s11325-010-0335-6.

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Klimes, Petr, Juliano J. Duque, Ben Brinkmann, Jamie Van Gompel, Matt Stead, Erik K. St. Louis, Josef Halamek, Pavel Jurak, and Gregory Worrell. "The functional organization of human epileptic hippocampus." Journal of Neurophysiology 115, no. 6 (June 1, 2016): 3140–45. http://dx.doi.org/10.1152/jn.00089.2016.

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The function and connectivity of human brain is disrupted in epilepsy. We previously reported that the region of epileptic brain generating focal seizures, i.e., the seizure onset zone (SOZ), is functionally isolated from surrounding brain regions in focal neocortical epilepsy. The modulatory effect of behavioral state on the spatial and spectral scales over which the reduced functional connectivity occurs, however, is unclear. Here we use simultaneous sleep staging from scalp EEG with intracranial EEG recordings from medial temporal lobe to investigate how behavioral state modulates the spatial and spectral scales of local field potential synchrony in focal epileptic hippocampus. The local field spectral power and linear correlation between adjacent electrodes provide measures of neuronal population synchrony at different spatial scales, ∼1 and 10 mm, respectively. Our results show increased connectivity inside the SOZ and low connectivity between electrodes in SOZ and outside the SOZ. During slow-wave sleep, we observed decreased connectivity for ripple and fast ripple frequency bands within the SOZ at the 10 mm spatial scale, while the local synchrony remained high at the 1 mm spatial scale. Further study of these phenomena may prove useful for SOZ localization and help understand seizure generation, and the functional deficits seen in epileptic eloquent cortex.

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Journal articles on the topic 'Spectral analysis of human sleep EEG'

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