Effects of signal artefacts on electroencephalography spectral power during sleep: quantifying the effectiveness of automated artefact-rejection algorithms.

Electroencephalography (EEG) recordings during sleep are often contaminated by muscle and ocular artefacts, which can affect the results of spectral power analyses significantly. However, the extent to which these artefacts affect EEG spectral power across different sleep states has not been quantified explicitly. Consequently, the effectiveness of automated artefact-rejection algorithms in minimizing these effects has not been characterized fully. To address these issues, we analysed standard 10-channel EEG recordings from 20 subjects during one night of sleep. We compared their spectral power when the recordings were contaminated by artefacts and after we removed them by visual inspection or by using automated artefact-rejection algorithms. During both rapid eye movement (REM) and non-REM (NREM) sleep, muscle artefacts contaminated no more than 5% of the EEG data across all channels. However, they corrupted delta, beta and gamma power levels substantially by up to 126, 171 and 938%, respectively, relative to the power level computed from artefact-free data. Although ocular artefacts were infrequent during NREM sleep, they affected up to 16% of the frontal and temporal EEG channels during REM sleep, primarily corrupting delta power by up to 33%. For both REM and NREM sleep, the automated artefact-rejection algorithms matched power levels to within ~10% of the artefact-free power level for each EEG channel and frequency band. In summary, although muscle and ocular artefacts affect only a small fraction of EEG data, they affect EEG spectral power significantly. This suggests the importance of using artefact-rejection algorithms before analysing EEG data.

Quantitative electroencephalography during rapid eye movement (REM) and non-REM sleep in combat-exposed veterans with and without post-traumatic stress disorder.

Sleep disturbances are a hallmark feature of post-traumatic stress disorder (PTSD), and associated with poor clinical outcomes. Few studies have examined sleep quantitative electroencephalography (qEEG), a technique able to detect subtle differences that polysomnography does not capture. We hypothesized that greater high-frequency qEEG would reflect 'hyperarousal' in combat veterans with PTSD (n = 16) compared to veterans without PTSD (n = 13). EEG power in traditional EEG frequency bands was computed for artifact-free sleep epochs across an entire night. Correlations were performed between qEEG and ratings of PTSD symptoms and combat exposure. The groups did not differ significantly in whole-night qEEG measures for either rapid eye movement (REM) or non-REM (NREM) sleep. Non-significant medium effect sizes suggest less REM beta (opposite to our hypothesis), less REM and NREM sigma and more NREM gamma in combat veterans with PTSD. Positive correlations were found between combat exposure and NREM beta (PTSD group only), and REM and NREM sigma (non-PTSD group only). Results did not support global hyperarousal in PTSD as indexed by increased beta qEEG activity. The correlation of sigma activity with combat exposure in those without PTSD and the non-significant trend towards less sigma activity during both REM and NREM sleep in combat veterans with PTSD suggests that differential information processing during sleep may characterize combat-exposed military veterans with and without PTSD.

The association between sleep-disordered breathing and maternal endothelial and metabolic markers in pregnancies complicated by obesity.

STUDY OBJECTIVES: To evaluate the impact of sleep-disordered breathing (SDB) on vascular, angiogenic and metabolic analytes in pregnancy. METHODS: Participants with a body mass index ≥30 kg/m2 underwent polysomnography at 14-20 weeks gestation (visit 1). Participants with SDB (defined as an apnea-hypopnea index ≥5 events/h) were then enrolled in a separate trial. SDB-negative participants returned for a polysomnogram at 28-31 weeks (visit 2) and were recategorized as persistent-negative SDB or new-onset SDB. Mean arterial blood pressure, mean uterine artery Doppler pulsatility index, endoglin, soluble Feline McDonough Sarcoma-like tyrosine kinase 1, placental growth factor, and the homeostatic model assessment for insulin resistance were measured after each visit. Our primary outcome was a composite of uterine artery Doppler pulsatility index, soluble FMS-like tyrosine kinase 1/placental growth factor ratio, and homeostatic model assessment for insulin resistance. For secondary analyses, each outcome variable was analyzed independently. RESULTS: A total of 242 and 130 participants completed visit 1 and visit 2, respectively. Newly diagnosed SDB was present in 37% of individuals at visit 1 and 31% of individuals at visit 2. No significant differences in our composite outcome vector were observed in individuals with and without SDB at either visit. In our secondary analysis, mean arterial blood pressure (88.7 ± 7.3 mm Hg vs 85.4 ± 7.1 mm Hg, P = .04) and fasting glucose (92.4 ± 15.2 mg/dL vs 86.6 ± 11.5 mg/dL, P = .05) were higher in participants with early pregnancy SDB. These associations were not observed for new-onset SDB. No associations were observed between uterine artery Doppler pulsatility index and angiogenic markers and SDB in pregnancy. CONCLUSIONS: SDB in early pregnancy was not associated with our composite primary outcome but was associated with higher mean arterial blood pressure and fasting glucose. The pathophysiologic changes that occur in pregnant individuals with SDB and how they lead to an increased risk of preeclampsia and gestational diabetes remain poorly understood. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: Sleep Disordered Breathing, Obesity and Pregnancy Study (SOAP); URL: https://clinicaltrials.gov/ct2/show/NCT02086448; Identifier: NCT02086448. CITATION: Onslow ML, Wolsk J, Wisniewski S, et al. The association between sleep-disordered breathing and maternal endothelial and metabolic markers in pregnancies complicated by obesity. J Clin Sleep Med. 2023;19(1):97-109.

Decreased coherence in higher frequency ranges (beta and gamma) between central and frontal EEG in patients with schizophrenia: A preliminary report.

Schizophrenia is associated with a dysfunction of cognitive integration that may be due to abnormalities in inhibitory neural circuitry. A previous study found a failure of gamma band (25-45 Hz) synchronization in patients with schizophrenia compared to controls. Another recent study also stressed the importance of investigating high frequencies in the scalp-recorded sleep electroencephalogram (EEG). In this study, we compared coherence between first episode drug-naïve patients with schizophrenia (n=8) and age- and sex-matched normal controls (n=8) using two 32-s epochs of C4 and F4 EEG. The coherence was obtained using 4096 data points (128 Hz signal) using cross-spectral analysis with Blackman-Tukey window in beta (15.25-24.75 Hz) and gamma (25-44.75 Hz) frequency bands. We used wake, non-rapid eye movement (NREM) and rapid eye movement (REM) sleep periods for the analyses. Our results show a significant decrease in coherence in both beta and gamma frequency bands in patients. Post-hoc 't' tests revealed a significantly lower coherence only during the wake stage in patients with schizophrenia in beta as well as gamma frequency bands. These results further support the importance of the analyses of high-frequency bands in the EEG and support previous findings of abnormal neural synchrony in patients with schizophrenia. These results have been discussed further in relation to wake and sleep stages.