Spectral Analysis Of Sleep EEG Research Articles

Spectral Power Analysis of Sleep Electroencephalography in Subjects with Different Severities of Obstructive Sleep Apnea and Healthy Controls.

PurposePrevious spectral analysis studies on obstructive sleep apnea (OSA) involved small samples, and the results were inconsistent. We performed a spectral analysis of sleep EEG based on different severities of OSA using the Sleep Heart Health Study data. This study aimed to determine the difference in EEG spectral power during sleep in the non-OSA group and with different severities of OSA in the general population.Patients and MethodsThe participants (n = 5,804) underwent polysomnography, and they were classified into non-OSA, mild OSA, moderate OSA, and severe OSA groups. The fast Fourier transformation was used to compute the EEG power spectrum for total sleep duration within contiguous 30-second epochs of sleep. The EEG spectral powers of the groups were compared using 4,493 participants after adjusting potential confounding factors that could affect sleep EEG.ResultsThe power spectra differed significantly among the groups for all frequency bands (p corr < 0.001). We found that the quantitative EEG spectral powers in the beta and sigma bands of total sleep differed (p corr < 0.001) among the participants in the non-OSA group and with different severities of OSA, controlling for covariates. The beta power was higher and the sigma power was lower in the OSA groups than in the non-OSA group. The beta power decreased in the order of severe OSA, moderate OSA, mild OSA, and non-OSA.ConclusionThis study suggests that there are differences between the microstructures of PSG-derived sleep EEG of non-OSA participants and those with different severities of OSA.

Impaired memory consolidation during sleep in patients with functional memory disorder

Functional memory disorder (FMD) is characterized by mnestic and attentional deficits without symptoms of mild cognitive impairment or dementia. FMD usually develops in subjects with high psychosocial stress level and is classified to the somatoform disorders. We assessed memory performance (procedural mirror tracing task, declarative visual and verbal memory task) and other cognitive functions before and after one night of sleep in 12 FMD patients (mean age: 51.7 yrs, 7 females) and 12 healthy subjects matched for age, gender and IQ. Memory performance and other neurocognitive tasks did not differ between the groups at baseline. After one night of sleep, FMD patients showed an impairment of declarative memory consolidation compared to healthy subjects (visual task: p = 0.004; verbal task: p = 0.039). Spectral analysis of sleep-EEG indicated an increased cortical excitation in FMD. We hypothesize that a hyperarousal state in FMD might contribute to sleep disturbance implicating negative effects on declarative memory consolidation.

Spectral analysis of sleep EEG in patients with restless legs syndrome

Objective Conventional analyses of sleep EEG recordings according to standard criteria indicate severe sleep disturbances in patients with restless legs syndrome (RLS). Spectral analysis of sleep EEG may be a sensitive tool to detect functional alterations of sleep mechanisms beyond the visual scoring of polysomnographic records. We analysed sleep EEG spectral power differences between RLS patients and healthy subjects. Furthermore, we studied the relationship of sleep EEG spectral power to the occurrence of periodic leg movements in sleep (PLMS) and arousal events. Methods Sleep EEGs from 20 patients with idiopathic RLS and of 20 age and sex matched healthy subjects were investigated. The spectral analysis was carried out on the same 30 s epochs for which sleep stages had been determined. As a first step, whole-night spectral power excluding epochs with an arousal or with a PLMS was compared separately for REM and NREM sleep between RLS and healthy subjects. In a second step, we evaluated the spectral effects of PLMS, PLMS with associated arousals and isolated arousals relative to epochs of sleep without such events in both groups. In this part of the analysis, we only included the epochs of sleep stage 2 (the main and most stable non-REM sleep stage) and of REM sleep. Results Spectral power of all sleep epochs (excluding arousals and PLMS) did not differ between patients with RLS and healthy subjects. As expected, arousals and PLMS-associated arousals resulted in a significant increase in higher-frequency activity (alpha, beta1, beta2 and gamma bands) in both groups. Spectral power in epochs with PLMS alone did not significantly differ from spectral power in epochs without PLMS and without arousal in any of the groups. Conclusions We found no evidence for an altered cortical activity in sleep stage 2 and REM sleep epochs in RLS patients compared to that in healthy controls if epochs with arousals were not considered. Furthermore, while PLMS associated with an arousal have a high impact on EEG spectra, the effect of a PLMS without arousal seems to be minor and transient. Significance Our data suggest that RLS related symptoms may intermittently disrupt sleep but do not appear to involve a persistent disturbance of the basic sleep generating patterns.

Visual and Spectral Analysis of Sleep EEG in Acute Hemispheric Stroke

Background: Reports on the effects of focal hemispheric damage on sleep EEG are rare and contradictory. Patients and Methods: Twenty patients (mean age ± SD 53 ± 14 years) with a first acute hemispheric stroke and no sleep apnea were studied. Stroke severity [National Institute of Health Stroke Scale (NIHSS)], volume (diffusion-weighted brain MRI), and short-term outcome (Rankin score) were assessed. Within the first 8 days after stroke onset, 1–3 sleep EEG recordings per patient were performed. Sleep scoring and spectral analysis were based on the central derivation of the healthy hemisphere. Data were compared with those of 10 age-matched and gender-matched hospitalized controls with no brain damage and no sleep apnea. Results: Stroke patients had higher amounts of wakefulness after sleep onset (112 ± 53 min vs. 60 ± 38 min, p < 0.05) and a lower sleep efficiency (76 ± 10% vs. 86 ± 8%, p < 0.05) than controls. Time spent in slow-wave sleep (SWS) and rapid eye movement (REM) sleep and total sleep time were lower in stroke patients, but differences were not significant. A positive correlation was found between the amount of SWS and stroke volume (r = 0.79). The slow-wave activity (SWA) ratio NREM sleep/wakefulness was lower in patients than in controls (p < 0.05), and correlated with NIHSS (r = –0.47). Conclusion: Acute hemispheric stroke is accompanied by alterations of sleep EEG over the healthy hemisphere that correlate with stroke volume and outcome. The increased SWA during wakefulness and SWS over the healthy hemisphere contralaterally to large strokes may reflect neuronal hypometabolism induced transhemispherically (diaschisis).

The time course of high-frequency bands (15–45 Hz) in all-night spectral analysis of sleep EEG

Objective: The EEG spectral content of all-night sleep recordings obtained in 7 healthy young subjects, aged 18–20 years, including frequencies up to 45 Hz, was studied in order to detect eventual changes in the high-frequency range similar to those reported by magnetic field recording during REM sleep at 40 Hz. Methods: For this purpose, power spectra were calculated with a fast Fourier transform and the power of the bands ranging 0.75–4.50 Hz (Delta), 4.75–7.75 (Theta), 8.00–12.25 (Alpha), 12.50–15.00 (Sigma), 15.25–24.75 (Beta), 25.00–34.75 (Gamma1), and 35.00–44.75 (Gamma 2) was calculated for-the whole period of analysis (7 h). Also two additional time series: the ratio between Beta and Gamma2, and between Gamma1 and Gamma2 were calculated (Beta and Gamma ratios). Results: Beta and Gamma1 showed small changes with a tendency to increase during REM sleep; Gamma2, on the contrary, showed small changes with a tendency to decrease during REM sleep. Beta and Gamma ratio peaks were clearly correlated with the occurrence of REM sleep. The small changes shown by Beta, Gamma1 and Gamma2 were not statistically significant; on the contrary, Beta ratio and Gamma ratio showed the most important statistical significance values being highest during REM sleep and lowest during slow-wave sleep. Finally, the calculation of the linear correlation coefficient and of the cross-correlation between the different bands showed a clear reciprocity between Delta and Beta and Gamma ratios. Conclusions: Our study shows a new method for the analysis of high frequencies (up to 45 Hz) in the scalp-recorded sleep EEG which allowed us to better define, as compared to previous studies on the same topic, the changes in power characteristically associated with REM sleep and correlated with the REM/non-REM ultradian rhythm, and to propose it as a tool for future studies.

Temporal EEG dynamics of non-REM sleep episodes in humans

The process of the human non-rapid eye movement (non-REM) sleep period has not been clarified. Time-based analysis on sleep EEG may provide an explanation. We focused on chronological aspects of initiation and termination of non-REM episodes, using spectral analysis of sleep EEG. The subjects were healthy male volunteers ( n=32, mean age±S.D.: 25.5±3.5 years). The rise latencies from non-REM sleep onset to the maximal power value and the decay latencies from the maximal power value to non-REM sleep offset were determined in the initial and final 21-min windows of individual non-REM episodes in each EEG band ranges. Low (12.1–13.7 Hz) and high (14.1–16.0 Hz) sigma ranges were analyzed separately. The rise and decay latencies were shorter in higher frequency ranges (>14 Hz) and longer in lower frequency ranges (<14 Hz). There were significant differences in the rise and decay latencies between low and high sigma ranges, indicating that the whole frequency ranges were clearly separated at the middle of the sigma range (14 Hz). The rise and decay latencies were significantly different in lower frequency ranges. The clock time of the night significantly affected only the rise latencies of the delta (0.78–3.9 Hz), alpha (8.2–11.7 Hz) and low sigma (12.1–13.7 Hz) ranges. In conclusion, initiation and termination of non-REM sleep was represented by higher frequency ranges, whereas further evolution and devolution of non-REM sleep was represented by lower frequency ranges, and only the evolution process was affected by the clock time of the night.

A comparison of period amplitude and power spectral analysis of sleep EEG in normal adults and depressed outpatients

Three experiments were carried out to evaluate the relationship between two techniques for quantifying electroencephalographic (EEG) data during sleep: period amplitude analysis (PAA) and power spectral analysis (PSA). In Experiment 1, canonical correlations and regression analyses were computed on PSA and PAA data from 40 undergraduate volunteers. The results yielded an average canonical correlation of 0.98. Further, multiple regression analyses demonstrated that the PSA variables accounted for approximately 66% of the variance in the PAA data, whereas PAA variables captured 88% of the variance in the PSA data. Epoch-to-epoch correlations were higher for PAA measures than for PSA data, perhaps indicating greater stability of PSA measures across epochs of sleep. In Experiment 2, PSA and PAA data were compared in 17 unmedicated outpatients with unipolar depression. Canonical correlations and regression analyses indicated that the overlap in variance between PSA and PAA did not exceed 50%, regardless of whether PSA or PAA variables were used as predictors. Epoch-to-epoch correlations between PAA measures were significantly higher than correlations among PSA variables, again suggesting greater stability of PAA data across epochs of sleep. The range of correlations for either data set was, however, substantially lower in the depressed than in the normal group. Experiment 3 evaluated the possibility that filter settings and artifact-rejection procedures had contributed to reduced overlap in PSA-PAA variance and reduced stability in depressed patients. An additional group of eight healthy volunteers served as subjects. Findings in Experiment 3 indicated that methodological differences between Experiments 1 and 2 did not account for the reduced correlations in the depressed group. It was concluded that PSA and PAA data should be comparable in normal subjects but are relatively independent in depressed patients. Epoch-to-epoch correlations were higher for PAA data than those found between PSA measures in both normal subjects and depressed patients, suggesting that PAA may be more stable across sleep epochs. Reduced stability may be a reflection of nonstationarity in the EEG of depressed patients.