Toward a better noninvasive assessment of preejection period: A novel automatic algorithm for B‐point detection and correction on thoracic impedance cardiogram

Impedance cardiography is the most common clinically validated, noninvasive method for determining the timing of the opening of the aortic valve, an important event used for measuring preejection period, which reflects sympathetic beta‐adrenergic influences on the heart. Automatic detection of the exact time of the opening of the aortic valve (B point on the impedance cardiogram) has proven to be challenging as its appearance varies between and within individuals and may manifest as a reversal, inflection, or rapid slope change of the thoracic impedance derivative's (dZ/dt) rapid rise. Here, a novel automatic algorithm is proposed for the detection of the B point by finding the main rapid rise of the dZ/dt signal, which is due to blood ejection. Several conditions based on zero crossings, minima, and maxima of the dZ/dt signal and its derivatives are considered to reject any unwanted noise and artifacts and select the true B‐point location. The detected B‐point locations are then corrected by modeling the B‐point time data using forward and reverse autoregressive models. The proposed algorithm is validated against expert‐detected B points and is compared with different conventional methods; it significantly outperforms them by at least 54% in mean error, 30% in mean absolute error, and 27% in standard deviation of error. This algorithm can be adopted in ambulatory studies requiring beat‐to‐beat evaluation of cardiac hemodynamic parameters over extended time periods where expert scoring is not feasible.     

Relations between daytime pre‐ejection period reactivity and sleep in late childhood

The sympathetic nervous system and children's sleep serve critical arousal regulation functions. Shortened pre‐ejection period, a reliable indirect index of greater sympathetic nervous system activity, has been associated with reduced sleep duration and quality in adults, but limited evidence exists in children regarding associations between pre‐ejection period and sleep. We examined relations between pre‐ejection period reactivity in response to a laboratory‐based stressor and multiple parameters of actigraphy‐based sleep duration and quality in children. The sample included 123 boys and 112 girls [mean age = 11.31 years, standard deviation (SD) = 0.63 years]. Controlling for body mass index, sex and pre‐ejection period baseline, increased sympathetic nervous system reactivity, indexed by a lower level of pre‐ejection period during the challenge than the baseline, was associated with worse sleep quality indicated by lower sleep efficiency, greater sleep activity and greater long wake episodes. The findings add to a small literature on relations between sympathetic nervous system functioning and children's sleep, suggesting that poor sleep quality is related to dysregulation of this stress response system.     

Trajectories of sleep and cardiac sympathetic activity indexed by pre‐ejection period in childhood

Fragmented and insufficient sleep has been implicated in disrupted autonomic nervous system activity during resting state conditions in typically developing children. Towards explication of these relations over development, the current study tested reciprocal relations between the development of sleep parameters (efficiency, duration, latency) and cardiac sympathetic nervous system (SNS) activity indexed by pre‐ejection period (PEP) during waking–resting state conditions throughout middle and late childhood. Whether sleep derives changes in PEP or vice versa was examined. A longitudinal design was employed and latent growth modelling was used to examine the research questions. During the first assessment, 282 children aged 9.44 years (65% European American, 35% African American) participated. Two more assessments followed, with a 1‐year lag between consecutive study waves. Sleep was examined with 7 nights of actigraphy in the child's home. Controlling for many potential confounds (sex, race/ethnicity, body mass index and family socioeconomic status), higher sleep efficiency and more sleep minutes predicted increases in PEP (less SNS activity) over 3 years. PEP did not predict changes in sleep efficiency or duration over time and there were no significant effects for sleep latency. Findings highlight the probable direction of effects between these two key bioregulatory systems. High levels of cardiac SNS activity are associated with many negative health outcomes, and thus these findings may have important implications.     

Quantifying rapid changes in cardiovascular state with a moving ensemble average

MEAP, the moving ensemble analysis pipeline, is a new open‐source tool designed to perform multisubject preprocessing and analysis of cardiovascular data, including electrocardiogram (ECG), impedance cardiogram (ICG), and continuous blood pressure (BP). In addition to traditional ensemble averaging, MEAP implements a moving ensemble averaging method that allows for the continuous estimation of indices related to cardiovascular state, including cardiac output, preejection period, heart rate variability, and total peripheral resistance, among others. Here, we define the moving ensemble technique mathematically, highlighting its differences from fixed‐window ensemble averaging. We describe MEAP's interface and features for signal processing, artifact correction, and cardiovascular‐based fMRI analysis. We demonstrate the accuracy of MEAP's novel B point detection algorithm on a large collection of hand‐labeled ICG waveforms. As a proof of concept, two subjects completed a series of four physical and cognitive tasks (cold pressor, Valsalva maneuver, video game, random dot kinetogram) on 3 separate days while ECG, ICG, and BP were recorded. Critically, the moving ensemble method reliably captures the rapid cyclical cardiovascular changes related to the baroreflex during the Valsalva maneuver and the classic cold pressor response. Cardiovascular measures were seen to vary considerably within repetitions of the same cognitive task for each individual, suggesting that a carefully designed paradigm could be used to capture fast‐acting event‐related changes in cardiovascular state.     

The detection of impedance cardiogram characteristic points using wavelet transform

A novel impedance cardiograph event detection method using wavelet transform is proposed. When compared to the C and E points in the pressure-volume loop, the wavelet method performs significantly better than the traditional method (P < 0.05) in the B and X points detection even after the addition of 20% artificial noise into the test signal. Nevertheless, the SVs estimated by ICG are poorly correlated with values measured by the conductance catheter.     

Origin of the impedance cardiogram investigated in the dog by exchange transfusion with a stroma-free haemoglobin solution

Summary In an anaesthetized dog an exchange transfusion was carried out with stroma-free haemoglobin solution. The total circulating blood volume was kept constant. The heart-synchronous changes in the thoracic electrical impedance (Z _o ) were measured before and after the exchange transfusion.Z _o consists of a parallel connection of a tissue impedance (Z _t ) and a blood impedance (Z _b ). With the aid of this model forZ _o the relative variations inZ _b (Z _b /Z _b ) were calculated from the relative variations inZ _o (Z _o /Z _o ). The marked decrease of Z _b /Z _b during the experiment can only be explained by the fact that apart from the heart-synchronous changes in vascular volume the impedance changes are also caused by flow-dependent changes in the electrical conductivity of blood caused by variations in orientation of the erythrocytes.     

Insomnia‐related interpretational bias is associated with pre‐sleep worry

Cognitive models of insomnia highlight the role of biased cognition in sleep‐related information, which is proposed to underlie pre‐sleep worry, which in turn results in both subjective and objective sleep deficits. To test this hypothesis, the current study investigated interpretational bias, which is a tendency to interpret ambiguous stimuli in a threat‐related (here: insomnia‐related) manner. We specifically hypothesized that interpretational bias would be associated with (a) pre‐sleep worry and (b) poor subjective and objective sleep. Interpretational bias was measured using the ambiguous scenario task, in which participants (n = 76, community sample) were presented with two types of scenarios (insomnia and anxiety related) that could be alternatively interpreted in a neutral manner. Participants additionally completed questionnaires to assess global sleep quality and pre‐sleep worry, which were followed by 1‐week sleep assessments (via diaries and actigraphy) to estimate specific, daily subjective and objective sleep parameters. The results showed that insomnia‐related (but not anxiety‐related) interpretational bias was positively associated with pre‐sleep worry as well as overall sleep quality. However, these associations could be explained by general trait anxiety. We also found no connection to specific subjective or objective parameters of daily sleep, such as sleep onset latency. These findings support the cognitive‐hyperarousal mechanism, where biased cognition (together with trait anxiety) underlies pre‐sleep worry. The association with overall sleep quality, but not with specific, daily subjective or objective sleep parameters, may suggest that interpretational bias is specifically relevant for how individuals judge and describe their sleep quality.     

Automatic artefact detection in single‐channel sleep EEG recordings

Quantitative electroencephalogram analysis (e.g. spectral analysis) has become an important tool in sleep research and sleep medicine. However, reliable results are only obtained if artefacts are removed or excluded. Artefact detection is often performed manually during sleep stage scoring, which is time consuming and prevents application to large datasets. We aimed to test the performance of mostly simple algorithms of artefact detection in polysomnographic recordings, derive optimal parameters and test their generalization capacity. We implemented 14 different artefact detection methods, optimized parameters for derivation C3A2 using receiver operator characteristic curves of 32 recordings, and validated them on 21 recordings of healthy participants and 10 recordings of patients (different laboratory) and considered the methods as generalizable. We also compared average power density spectra with artefacts excluded based on algorithms and expert scoring. Analyses were performed retrospectively. We could reliably identify artefact contaminated epochs in sleep electroencephalogram recordings of two laboratories (healthy participants and patients) reaching good sensitivity (specificity 0.9) with most algorithms. The best performance was obtained using fixed thresholds of the electroencephalogram slope, high‐frequency power (25–90 Hz or 45–90 Hz) and residuals of adaptive autoregressive models. Artefacts in electroencephalogram data can be reliably excluded by simple algorithms with good performance, and average electroencephalogram power density spectra with artefact exclusion based on algorithms and manual scoring are very similar in the frequency range relevant for most applications in sleep research and sleep medicine, allowing application to large datasets as needed to address questions related to genetics, epidemiology or precision medicine.     

EEG predictors of dreaming outside of REM sleep

The stream of human consciousness persists during sleep, albeit in altered form. Disconnected from external input, the mind and brain remain active, at times creating the bizarre sequences of thought and imagery that comprise “dreaming.” Yet despite substantial effort toward understanding this unique state of consciousness, no reliable neurophysiological indicator of dreaming has been discovered. Here, we identified electroencephalographic (EEG) correlates of dreaming using a within‐subjects design to characterize the EEG preceding awakenings from sleep onset, REM (rapid eye movement) sleep, and N2 (NREM Stage 2) sleep from which participants were asked to report their mental experience. During the transition into sleep, compared to periods during which participants reported thinking, emergence of dream imagery was associated with increased absolute power below 7 Hz. During later N2, dreaming conversely occurred during periods of decreased relative power below 1 Hz, accompanied by an increase in relative power above 4 Hz. No EEG predictors of dreaming were identified during REM. These observations suggest an inverted‐U relationship between dreaming and the prevalence of low‐frequency EEG rhythms, such that dreaming first emerges in concert with EEG slowing during the sleep‐wake transition, but then disappears as high‐amplitude slow oscillations come to dominate the recording during later N2 sleep.     

应用阻抗心动图测量脉搏波传导时间

为了研究射血前时间对用脉搏波传导时间进行连续血压测量的影响,本文设计并实现一种用阻抗心动图确定脉搏波起始点计算脉搏波传导时间的方法,在深呼使血压产生波动的过程中,采用此方法与心电R波峰值作起始点的方法进行对比实验.实验结果表明,用这种方法测量脉搏波传导时间随呼吸的变化曲线与血压变化的规律相一致.与用心电R波法相比,阻抗心电图法可以有效避免射血前时间变化对PWTT测量结果的影响,是一种准确测量脉搏波传导时间的方法.     

Periodic limb movements during sleep are associated with cardiovascular diseases: A systematic review and meta‐analysis

Periodic limb movements during sleep present with repetitive movements, typically in the lower limbs, during sleep. Periodic limb movements during sleep have been proposed to be associated with increased risk of heart diseases. The aim of this study was to examine the co‐morbidity rates of heart disease, including acute myocardial infarction, coronary artery disease and cardiovascular disease, in subjects with or without periodic limb movements during sleep through a meta‐analysis. An electronic review of PubMed, Embase, ScienceDirect, Cochrane Library, ProQuest, Web of Science, ClinicalKey and ClinicalTrials.gov was performed. Clinical studies, case‐controlled trials and cohort studies were all included in the search. Case reports or series, and non‐clinical studies were excluded. A meta‐analysis of the results of six studies comparing the prevalence of coronary artery disease/acute myocardial infarction/cardiovascular disease in subjects with/without periodic limb movements during sleep was performed. There were significantly higher co‐morbidity rates of coronary artery disease (odds ratio = 1.568, 95% confidence interval: 1.187–2.073, p = 0.002) and cardiovascular disease (odds ratio = 1.279, 95% confidence interval: 1.095–1.494, p = 0.002), but not acute myocardial infarction (odds ratio = 1.272, 95% confidence interval = 0.942–1.718, p = 0.117), in the periodic limb movements during sleep group than in the non‐periodic limb movements during sleep group. This meta‐analysis highlights the importance of a significantly high prevalence of coronary artery disease and cardiovascular disease in subjects with periodic limb movements during sleep. Further studies should be focused on the potential pathophysiology, and whether treatment for periodic limb movements during sleep can improve the outcome of heart disease.     

Using trial‐level data and multilevel modeling to investigate within‐task change in event‐related potentials

EEG data, and specifically the ERP, provide psychologists with the power to examine quickly occurring cognitive processes at the native temporal resolution at which they occur. Despite the advantages conferred by ERPs to examine processes at different points in time, ERP researchers commonly ignore the trial‐to‐trial temporal dimension by collapsing across trials of similar types (i.e., the signal averaging approach) because of constraints imposed by repeated measures ANOVA. Here, we present the advantages of using multilevel modeling (MLM) to examine trial‐level data to investigate change in neurocognitive processes across the course of an experiment. Two examples are presented to illustrate the usefulness of this technique. The first demonstrates decreasing differentiation in N170 amplitude to faces of different races across the course of a race categorization task. The second demonstrates attenuation of the ERN as participants commit more errors within a task designed to measure implicit racial bias. Although the examples presented here are within the realm of social psychology, the use of MLM to analyze trial‐level EEG data has the potential to contribute to a number of different theoretical domains within psychology.     

Infant feeding practices and sleep development in pre‐schoolers from the EDEN mother–child cohort

Sleep problems affect 20%–30% of toddlers and preschoolers. Few longitudinal studies focused on the impact of infant feeding practices on sleep. We aimed to study the associations between feeding practices up to 8 months and trajectories of sleep quantity or quality from 2 to 5–6 years. Analyses included 1,028 children from the EDEN mother–child cohort. Data were collected by self‐administered questionnaires. Associations between feeding practices (breastfeeding, complementary feeding, use of thickened infant formula, night feeding) and sleep trajectories (sleep‐onset difficulties, night waking, nighttime in bed) were analysed by multiple logistic regressions. Predominant breastfeeding for more than 4 months was associated with lower risk for belonging to the persistent sleep‐onset difficulties trajectory. Night feeding at 4 months or at 2 years old was associated with higher risk for belonging to the persistent sleep‐onset difficulties trajectory, and night feeding at 8 months was associated with higher risk for night waking and higher risk for short nighttime in bed. Early introduction (< 4 months) to complementary foods (excluding baby cereals) was related to lower risk for short nighttime in bed. Use of baby cereals or thickened infant formula was related neither to sleep quality nor to sleep quantity. In conclusion, infant feeding practices are associated with sleep trajectories in preschoolers, with notably a potential protective role of breastfeeding. Further researches are needed to clarify the mechanisms of these relationships.     

Association between proteomics and obstructive sleep apnea phenotypes in a community‐based cohort of women

Proteomic‐based technologies offer new opportunities to identify proteins that might reflect the cardiometabolic stress caused by different aspects of sleep‐disordered breathing. We aimed to investigate whether severe obstructive sleep apnea and severe obstructive sleep apnea during rapid eye movement sleep are associated with changed levels of inflammatory and cardiac disease‐related proteins in a population‐based cohort of women. In the community‐based “Sleep and Health in Women” (SHE) cohort study, 400 women underwent polysomnography, anthropometric measurements and blood sampling. Two proteomic assays (Olink Proseek^® Inflammation panel and Olink Proseek^® Cardiovascular II panel), each measuring 92 proteins, were analysed in a subsample of 253 women. p‐Values were adjusted for multiple testing, with false discovery rate set at 10%. In unadjusted models, 57 proteins were associated with apnea?hypopnea index, 56 proteins with oxygen desaturation index and 64 proteins with rapid eye movement?apnea?hypopnea index. After adjustment for age, body mass index and plate, there were no significant associations between apnea?hypopnea index or oxygen desaturation index and any of the proteins. Severe obstructive sleep apnea during rapid eye movement sleep (rapid eye movement?apnea?hypopnea index ≥ 30) was associated with decreased levels of two anti‐inflammatory proteins; Sirt2 (q‐value .016) and LAP‐TGF‐β₁ (q‐value .016). There was also a negative association between rapid eye movement?apnea?hypopnea index of ≥ 30 and Axin1 (q‐value .095), a protein thought to facilitate TGF‐β‐signalling. We conclude that severe obstructive sleep apnea during rapid eye movement sleep is associated with low levels of Sirt2, LAP‐TGF‐β₁ and Axin1, anti‐inflammatory proteins involved in metabolic regulation and in the atherosclerotic process. For obstructive sleep apnea based on a whole night, the associations with cardiac and inflammatory proteins are weaker, and explained to a large extent by age and body mass index.     

Mandible behaviour interpretation during wakefulness,sleep and sleep‐disordered breathing

The mandible movement (MM) signal provides information on mandible activity. It can be read visually to assess sleep–wake state and respiratory events. This study aimed to assess (1) the training of independent scorers to recognize the signal specificities; (2) intrascorer reproducibility and (3) interscorer variability. MM was collected in the mid‐sagittal plane of the face of 40 patients. The typical MM was extracted and classified into seven distinct pattern classes: active wakefulness (AW), quiet wakefulness or quiet sleep (QW/S), sleep snoring (SS), sleep obstructive events (OAH), sleep mixed apnea (MA), respiratory related arousal (RERA) and sleep central events (CAH). Four scorers were trained; their diagnostic capacities were assessed on two reading sessions. The intra‐ and interscorer agreements were assessed using Cohen's κ. Intrascorer reproducibility for the two sessions ranged from 0.68 [95% confidence interval (CI): 0.59–0.77] to 0.88 (95% CI: 0.82–0.94), while the between‐scorer agreement amounted to 0.68 (95% CI: 0.65–0.71) and 0.74 (95% CI: 0.72–0.77), respectively. The overall accuracy of the scorers was 75.2% (range: 72.4–80.7%). CAH MMs were the most difficult to discern (overall accuracy 65.6%). For the two sessions, the recognition rate of abnormal respiratory events (OAH, CAH, MA and RERA) was excellent: the interscorer mean agreement was 90.7% (Cohen's κ: 0.83; 95% CI: 0.79–0.88). The discrimination of OAH, CAH, MA characteristics was good, with an interscorer agreement of 80.8% (Cohen's κ: 0.65; 95% CI: 0.62–0.68). Visual analysis of isolated MMs can successfully diagnose sleep–wake state, normal and abnormal respiration and recognize the presence of respiratory effort.     

Dynamic RSA: Examining parasympathetic regulatory dynamics via vector‐autoregressive modeling of time‐varying RSA and heart period

Expanding on recently published methods, the current study presents an approach to estimating the dynamic, regulatory effect of the parasympathetic nervous system on heart period on a moment‐to‐moment basis. We estimated second‐to‐second variation in respiratory sinus arrhythmia (RSA) in order to estimate the contemporaneous and time‐lagged relationships among RSA, interbeat interval (IBI), and respiration rate via vector autoregression. Moreover, we modeled these relationships at lags of 1 s to 10 s, in order to evaluate the optimal latency for estimating dynamic RSA effects. The IBI (t) on RSA (t‐n) regression parameter was extracted from individual models as an operationalization of the regulatory effect of RSA on IBI—referred to as dynamic RSA (dRSA). Dynamic RSA positively correlated with standard averages of heart rate and negatively correlated with standard averages of RSA. We propose that dRSA reflects the active downregulation of heart period by the parasympathetic nervous system and thus represents a novel metric that provides incremental validity in the measurement of autonomic cardiac control—specifically, a method by which parasympathetic regulatory effects can be measured in process.     

High‐altitude hypoxia and periodic breathing during sleep: gender‐related differences

High‐altitude exposure is characterized by the appearance of periodic breathing during sleep. Only limited evidence is available, however, on the presence of gender‐related differences in this breathing pattern. In 37 healthy subjects, 23 male and 14 female, we performed nocturnal cardio‐respiratory monitoring in the following conditions: (1) sea level; (2) first/second night at an altitude of 3400 m; (3) first/second night at an altitude of 5400 m and after a 10 day sojourn at 5400 m. At sea level, a normal breathing pattern was observed in all subjects throughout the night. At 3400 m the apnea–hypopnea index was 40.3 ± 33.0 in males (central apneas 77.6%, central hypopneas 22.4%) and 2.4 ± 2.8 in females (central apneas 58.2%, central hypopneas 41.8%; P < 0.01). During the first recording at 5400 m, the apnea–hypopnea index was 87.5 ± 35.7 in males (central apneas 60.0%, central hypopneas 40.0%) and 41.1 ± 44.0 in females (central apneas 73.2%, central hypopneas 26.8%; P < 0.01), again with a higher frequency of central events in males as seen at lower altitude. Similar results were observed after 10 days. With increasing altitude, there was also a progressive reduction in respiratory cycle length during central apneas in males (26.9 ± 3.4 s at 3400 m and 22.6 ± 3.7 s at 5400 m). Females, who displayed a significant number of central apneas only at the highest reached altitude, were characterized by longer cycle length than males at similar altitude (30.1 ± 5.8 s at 5400 m). In conclusion, at high altitude, nocturnal periodic breathing affects males more than females. Females started to present a significant number of central sleep apneas only at the highest reached altitude. After 10 days at 5400 m gender differences in the apnea–hypopnea index similar to those observed after acute exposure were still observed, accompanied by differences in respiratory cycle length.     

Effects of a 24‐hr‐shift‐related short‐term sleep deprivation on cardiac function: A cardiac magnetic resonance‐based study

Fatigue and sleep deprivation are common phenomena, especially among medical professionals and shift workers. Studies have proven that short episodes of sleep deprivation can lead to sympathetic hyperactivity with an elevation in blood pressure, heart rate, and an increased secretion of stress hormones (e.g. cortisol, noradrenaline, thyroid hormones). In this study investigating cardiac strain in 20 healthy subjects undergoing short‐term sleep deprivation, it could be shown for the first time that 24‐hr‐shift‐related short‐term sleep deprivation leads to a significant increase in cardiac contractility, blood pressure, heart rate and stress hormone secretion. These findings may help better understand how workload and shift duration affect public health, and lay the foundation for further investigations.     

Prevalence of sleep disturbances in Chinese university students: a comprehensive meta‐analysis

This is a meta‐analysis of the pooled prevalence of sleep disturbances and its associated factors in Chinese university students. English (PubMed, PsycINFO, Embase) and Chinese (SinoMed, Wan Fang Database and Chinese National Knowledge Infrastructure) databases were systematically and independently searched from inception until 16 August 2016. The prevalence of sleep disturbances was pooled using random‐effects model. Altogether 76 studies involving 112 939 university students were included. The overall pooled prevalence of sleep disturbances was 25.7% (95% CI: 22.5–28.9%). When using the screening scales Pittsburgh Sleep Quality Index, Athens Insomnia Scale and Self‐Rating Sleeping State Scale, and the diagnostic criteria of the Chinese Classification of Mental Disorders (Second Edition), the pooled prevalence of sleep disturbances was 24.1% (95% CI: 21.0–27.5%) and 18.1% (95% CI: 16.4–20.0%), respectively. The percentages of students dissatisfied with sleep quality and those suffering from insomnia symptoms were 20.3% (95% CI: 13.0–30.3%) and 23.6% (95% CI: 18.9–29.0%), respectively. Subgroup analyses revealed that medical students were more vulnerable to sleep disturbances than other student groups. There was no significant difference between males and females, and across geographic locations. Sleep disturbances are common in Chinese university students. Appropriate strategies for prevention and treatment of sleep disturbances in this population need greater attention.     

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.