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Volume 14 No. 07
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Accepted Papers

Scientific Investigations

Changes in Sleep Characteristics and Breathing Parameters During Sleep in Early and Late Pregnancy

Bilgay Izci-Balserak, PhD1; Brendan T. Keenan, MS2; Charles Corbitt, MS2; Beth Staley, BA2; Michael Perlis, PhD2; Grace W. Pien, MD, MSCE3
1Department of Biobehavioral Health Sciences, College of Nursing, University of Illinois, Chicago, Illinois; 2Center for Sleep and Circadian Neurobiology, University of Pennsylvania, Philadelphia, Pennsylvania; 3School of Medicine, Johns Hopkins University, Baltimore, Maryland


Study Objectives:

Few studies have objectively evaluated sleep characteristics during pregnancy or investigated the relationship between altered spectral electroencephalogram (EEG) bands and sleep-disordered breathing (SDB). The study aimed to describe changes in sleep as measured by polysomnography (PSG) and spectral EEG bands during pregnancy and to examine the relationship between delta power in non-rapid eye movement (NREM) sleep and SDB.


This is a secondary analysis of a prospective study. One hundred twenty-three women underwent full PSG in early pregnancy, and 97 repeated PSG in late pregnancy. Spectral analysis of the EEG in NREM sleep was performed. We used linear and logistic mixed-model regression to analyze the sleep measures and linear regression to explore the association between delta power and apnea-hypopnea index (AHI) changes during pregnancy.


In late pregnancy, women had shorter sleep duration, poorer sleep efficiency, more awakenings, more stage N2 sleep, less slow wave sleep, less REM sleep, higher AHI, and higher periodic limb movement index compared to early pregnancy. The percentage of stage N1 sleep, sleep latency, REM sleep latency, and arousal index frequency did not change. Regarding EEG-spectra, delta and theta powers decreased, but beta-2 power increased during pregnancy. In multivariable analyses, greater reduction of delta power was associated with larger increases in AHI (β [95% confidence interval] = −0.038 [−0.073, −0.002], P = .040). Estimates suggest that each one-unit increase in AHI reduces delta power by 4% in late pregnancy.


PSG-measured sleep characteristics change during pregnancy. Delta power decreases when the severity of SDB increases during pregnancy.


A commentary on this article appears in this issue on page 1095.


Izci-Balserak B, Keenan BT, Corbitt C, Staley B, Perlis M, Pien GW. Changes in sleep characteristics and breathing parameters during sleep in early and late pregnancy. J Clin Sleep Med. 2018;14(7):1161–1168.


Current Knowledge/Study Rationale: Sleep patterns differ during pregnancy. Few studies have objectively evaluated sleep characteristics during pregnancy or investigated the relationship between changes in EEG bands and SDB.

Study Impact: This study measured changes in sleep characteristics that included EEG frequency bands from early to late pregnancy. This study also indicated that the level of delta power from early to late pregnancy may decrease with increased severity of SDB. Alterations in sleep architecture have been shown to increase the risk of obesity, diabetes, and cardiovascular complications. Thus, decreased sleep duration and changes in sleep architecture during pregnancy may adversely affect maternal and infant outcomes. The current study increases knowledge and awareness about changes in sleep characteristics related to pregnancy and sleep disturbances among antenatal care providers. This knowledge may contribute to better health outcomes for mothers and their children.


Maternal sleep can affect a number of endocrine, metabolic, and neurological functions that are critical for maintaining a healthy pregnancy and fetal growth.1 Emerging evidence indicates that sleep disturbances including sleep apnea and short sleep duration are associated with adverse fetal-maternal outcomes (eg, gestational diabetes and preeclampsia) in the pregnant population, analogous to increased risks for obesity, diabetes, hypertension, and mortality in the nonpregnant population.2 Most pregnant women (66% to 100%) experience changes in sleep architecture, sleep quality, and sleep duration312 attributed to the physical and hormonal adaptations of pregnancy (eg, frequent urination and snoring). Nocturnal awakenings become more frequent in late pregnancy.11,1316 Studies using questionnaires and actigraphy have reported that sleep efficiency (SE) progressively decreases because of more frequent and longer nocturnal awakenings after sleep onset.46,10,14,17,18 Although the sleep pattern may undergo consistent changes in most women by the third trimester, only a small number of studies has comprehensively evaluated electroencephalographic (EEG) sleep architecture during pregnancy at different time points during gestation. In fact, those that exist are limited by small sample sizes (n < 30 subjects).49,19 Furthermore, the changes reported in EEG sleep parameters including rapid eye movement (REM) sleep and slow wave sleep (SWS) during pregnancy are inconsistent. For example, existing studies have noted that SWS did not change,4,9 decreased,57 or increased8 during pregnancy.

Conventional methods for assessing sleep architecture involve visual scoring of the EEG.20 Unlike conventional methods, EEG power spectral analysis is a technique analyzing the statistical significance of cycles in time series. It is a more refined measure of EEG than the categorical typology used for visual sleep-stage scoring. Slow wave activity of non-rapid eye movement (NREM) sleep has proved to be a reliable marker of sleep homeostasis.21 Delta spectral power is the dominant electroencephalographic (EEG) frequency (< 4 Hz) during NREM sleep. Delta EEG power is generally highest in the first cycle of NREM and then decreases exponentially across the night.22 Delta EEG activity has been observed to be increased in women with preeclampsia,23 and is decreased overnight in the nonpregnant population after disruption of SWS24 or a daytime nap.25 Beta EEG activity is associated with high levels of perceived stress and increased at or around sleep onset and during NREM sleep in patients with insomnia.2628 Only one small study (n = 9) using spectral analysis4 in healthy pregnant women reported that delta, theta, and alpha bands were reduced, but high frequencies such as beta band did not change across pregnancy. Pregnant women are susceptible to sleep disturbances caused by sleep apnea, periodic limb movements, or difficulty maintaining sleep1,29,30 that result in reduction in the percentage of SWS. Despite evidence suggesting that alterations in sleep and EEG abnormalities—such as reduced SWS—negatively affect well-being in nongravid individuals and fetal-maternal health in the pregnant population, studies using spectral analysis in pregnancy are lacking. This is likely because of the high cost of performing polysomnography (PSG), difficulty of recruiting participants, and need for specialized methodology.

Using data from a larger longitudinal study, we aimed to investigate whether (1) PSG-measured sleep characteristics and spectral EEG bands (delta, theta, alpha, sigma, beta-1, and beta-2) during NREM sleep change from early to late pregnancy, and (2) increased severity of sleep-disordered breathing (SDB) is associated with changes in delta EEG power during pregnancy.


Study Participants and Design

This study is a secondary analysis of data from a prospective, observational study designed to evaluate risk factors for SDB in pregnant women.3032 The study was approved by the institutional review board of the University of Pennsylvania, Philadelphia, Pennsylvania. Patients were recruited from women who received care in the outpatient obstetrics practices at the Hospital of the University of Pennsylvania. Exclusion criteria were communication, cognitive or behavioral impairments interfering with informed participation, no telephone, self-reported illicit drug use or alcoholism, serious preexisting medical conditions, sedative/hypnotic use ≥ 3×/wk, or current obstructive sleep apnea (OSA) treatment. Women who were eligible and agreed to participate provided written informed consent. In early pregnancy 134 women were enrolled and completed overnight PSG; 4 women were subsequently excluded from the study because they failed to meet inclusion or exclusion criteria. Seven women were excluded from the analysis due to having multiple pregnancies. In late pregnancy, 102 of the remaining 123 women completed PSG. Data from 5 of these women were excluded from the analysis because of nocturnal sleep duration less than 3 hours (n = 2) or technical error (n = 3). Baseline characteristics of women with and without follow-up PSG are presented in Table S1 in the supplemental material.


Details of the study protocol have been described previously.30 Briefly, participants completed a demographic questionnaire and underwent overnight laboratory PSG in early pregnancy (8–14 weeks) and again in late pregnancy (27–39 weeks). The demographic questionnaire included maternal age, race/ethnicity, employment status, and work schedule.

PSG tests (Sandman, Nellcor Puritan Bennett (Melville) Ltd. Kanata, Ontario, Canada) included EEG (central, occipital leads), electrooculogram, submental and tibialis electromyograms, single bipolar electrocardiogram, finger pulse oximetry (Nellcor), chest and abdominal excursion (Protech piezo belts), airflow by nasal pressure, and oral thermistor.30 All recordings included EEG (C3-A2, C4-A1), electrooculography (horizontal and vertical), and electromyogram (submental) were scored visually by experienced sleep technologist in 30-second epochs. All sleep stages including stage N1, stage N2, stage N3 (SWS), and REM sleep were scored according to standardized criteria.20 Apneas and hypopneas were scored as previously described30 and the apnea-hypopnea index (AHI, apneas + hypopneas / hours of sleep) was computed using standard criteria.30,32

EEG Power Spectral Analysis

Power spectral analysis is a statistical technique for detecting periodicities within time series data. As employed within EEG, the technique is routinely used to decompose complex wave forms into their constituent frequencies.

Quantification is accomplished by determining the amount of voltage that occurs per Hz for prespecified bandwidths.27 Original signal acquisition was governed by Sandman 8.0 PSG system with MMC amplifiers at the sampling rate of 128 Hz. The high-pass filter setting of the EEG amplifiers was set to 0.3 Hz, the low-pass filter to 100 Hz, and the 60 Hz notch filter was activated. The EEG signals were low-pass filtered prior to A/D conversion by an anti-aliasing filter (70 Hz, 24 dB/octave). The base sampling rate was 256 Hz.

All EEGs had been visually inspected and artifacts had been removed as described elsewhere.27 Additionally, a validated postprocessing statistical program was used for artifact rejection. The program included the calculation of alternative amplitude measures, three forms of artifact rejection including the Brunner method,33 and data averaging by stage and/or cycle. Following these procedures, the digital EEGs from early to late pregnancy were subjected to power spectral analysis by applying a Fast Fourier transform using Sandman 8.0 software. The spectral window was set for a 4-second interval. Prior to frequency assessment, the data within the 4-second windows were automatically cosine tapered and detrended (mean detrend) to eliminate nonstationary data. After the frequency assessment, nonoverlapping windows were averaged to yield mean power spectral distributions for each sleep-scored 30-second epoch (0.25 Hz resolution). Absolute power spectra (μV2) for each of the EEG sites were computed for the delta band (1–4 Hz), theta (4–7.5 Hz), alpha (7.5–12 Hz), sigma (12–14 Hz), beta-1 (14–25 Hz), and beta-2 (25–35 Hz).26 Average spectral profiles were created for each NREM cycle. The proportion of EEG bands in NREM cycles changes across the night. For example, individuals usually have the highest delta EEG power in the first cycle of NREM and then delta EEG decreases exponentially throughout the night. For this reason, the analyses of spectral data were limited to the absolute power from the first two NREM cycles in order to ensure that there were comparable spectral data between early and late pregnancy.

Statistical Analysis

Descriptive data were expressed as means and standard deviations for all continuous measures in early or late pregnancy. To stabilize the variance and correct for non-normality, the log transformation was used for the percentage of stage N1 sleep, percentage of wake after sleep onset (WASO), arousal index, and power of EEG frequency of alpha, theta, and beta.

Linear or logistic mixed effects models were performed to analyze the longitudinal data for sleep measures.34 This analysis resembles standard regression but accounts for the inherent correlation of repeated measures within a given individual and accommodates missing data that occurs in longitudinal studies. We used mixed-effects analysis to determine whether sleep parameters, including sleep stages and absolute power in EEG spectral bands, changed between early and late pregnancy. To examine the relationship between delta power changes and changes in AHI from early to late pregnancy, data were analyzed using linear regression restricted to individuals with data at both timepoints. Analyses were performed unadjusted and in a fully adjusted model that included change in delta power as the outcome variable, change in SDB severity as assessed by AHI, and any covariates with a value of P < .20 in unadjusted analyses or that have been previously associated with delta power and SDB (including age, gestational age, body mass index [BMI], and parity).30,35 We also checked for collinearity between potentially related variables using Spearman coefficient (eg, gestational weight gain, baseline BMI, and age) during the model selection process. Values of P < .05 were considered statistically significant.


Sample Characteristics

One hundred twenty-three women who completed PSG in early pregnancy were included in our analysis sample (Table 1). Mean (SD) gestational age was 12.05 (1.80) weeks. Ninety-seven of these women also completed PSG in late pregnancy (Table 2). Mean gestational age was 33.61 (2.56) weeks. Most participants (75%) self-identified as African American, with the remaining 25% of women reporting white or other racial backgrounds. Of the 123 women studied at baseline, 47% were nulliparous, and all women had singleton pregnancies. At enrollment in early pregnancy (Table 1), the mean age of participants was 27.15 (7.20) years and mean BMI was 30.56 (7.22) kg/m2. Mean BMI increased to 33.30 (6.25) kg/m2 in late pregnancy (P < .001). Subject characteristics, including demographic, gynecological, or baseline sleep variables (eg, age, race, parity, baseline AHI) were not different between women completing one or both PSG tests (Table S1).

Demographic characteristics of the study sample in early pregnancy (n = 123).


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Table 1

Demographic characteristics of the study sample in early pregnancy (n = 123).

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Changes in demographic characteristics from early to late pregnancy.


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Table 2

Changes in demographic characteristics from early to late pregnancy.

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Sleep Continuity Data

There were changes in sleep parameters over time during pregnancy (Table 3). Mean total sleep time (TST) decreased from 368.99 (54.53) minutes in early pregnancy to 354.91 (66.65) minutes in late pregnancy (P = .019). We also examined the amount of time spent in each sleep stage and WASO as a percentage of TST. SE decreased from 77% in early to 74% in late pregnancy (P = .007) whereas WASO significantly increased (P = .012). Women spent 12.5% less time in the supine position in late pregnancy compared to early pregnancy (P = .001). There was no significant change in the arousal index.

Changes in sleep parameters from early to late pregnancy.


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Table 3

Changes in sleep parameters from early to late pregnancy.

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Sleep Architecture Data

The percentages of SWS and REM sleep decreased from early to late pregnancy, whereas the percentage of stage N2 sleep increased (Table 3). The percentage of stage N1 sleep showed a borderline but nonsignificant change between early and late pregnancy (P = .074), whereas latency to stage N2 sleep and REM sleep remained similar during pregnancy.

AHI and Limb Movement

The mean AHI and periodic limb movement index (PLMI) both significantly increased from early pregnancy to late pregnancy (Table 3).

Changes in EEG Spectra From Early to Late Pregnancy

Changes in EEG spectral parameters obtained from the first two NREM cycles are summarized in Table 4. Mean delta EEG power (P = .019) and theta power (P = .001) were significantly lower in late compared to early pregnancy. By contrast, mean beta-2 power was significantly higher in late compared to early pregnancy (P = .001). However, alpha, sigma, and beta-1 power during NREM sleep did not change significantly from early to late pregnancy.

Changes in power or log power of EEG frequency spectra in NREM sleep from early to late pregnancy.


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Table 4

Changes in power or log power of EEG frequency spectra in NREM sleep from early to late pregnancy.

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Changes in Delta Power From Early to Late Pregnancy

We examined the relationship between changes in delta power and severity of SDB using change in AHI as a continuous variable as well as relationships with other sleep and demographic characteristics (Table 5). In unadjusted analyses, a trend between increases in AHI and decreases in delta power was observed (P = .19). Increases in TST were significantly associated with increases in delta power from early to late pregnancy (P = .002) in unadjusted analyses, and there was trending evidence for more decrease in delta among African Americans compared to white/other (P = .060) groups. No significant relationships between changes in delta power and gestational weight gain (P = .987), PLMI (P = .763), parity (P = .254), education (P = .517), marital status (P = .421), shift work (P = .533), changes in napping frequency (P = .729), or an interaction between changes in TST and nap frequencies (P = .864) exist. Using covariates associated with changes in delta power at P < .20 in unadjusted analyses, as well as a priori covariates known to associate with delta power or SDB severity, we created a multivariable model including AHI change, TST change, BMI change, age, race, parity, and gestational age. In this multivariable analysis, we observed a significant relationship between delta power changes and changes in AHI severity during pregnancy (β [95% CI] = −0.038 [−0.073, −0.002], P = .040) (Table 5). Specifically, for each one-unit increase in AHI between early and late pregnancy, the mean delta power decreased by approximately 4%. Change in TST (P = .001) and race (P = .018) also remained or became statistically significant in this multivariate model.

Unadjusted and adjusted relationships of delta power changes between early and late pregnancy with AHI changes.


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Table 5

Unadjusted and adjusted relationships of delta power changes between early and late pregnancy with AHI changes.

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Our study demonstrates that sleep continuity and architecture change significantly from early to late pregnancy using objective measurements of sleep. Our findings from spectral analysis of the EEG in NREM sleep also revealed a significant reduction in the delta and theta power, but an increase in beta-2 power from early to late pregnancy. Additionally, this is the first study to our knowledge to report that there is a significant relationship between changes in delta power and changes in severity of SDB during pregnancy. Furthermore, this is one of few studies that used full PSG and power spectral analysis in a large longitudinal cohort to evaluate sleep characterisitics comprehensively during pregnancy. The changes in sleep continuity and architecture included decreased amounts of total sleep and percentages of SWS and REM sleep, increased percentage of stage N2 sleep, and more wake after sleep onset, resulting in lower SE from early to late pregnancy. Women slept for less time in the supine position in late pregnancy compared to early pregnancy. We did not observe significant changes in arousal index, stage N1 sleep, sleep latency, or REM sleep latency, and frequency of napping.

In the current study, we observed significant decreases in SE and TST, and an increase in WASO with reductions of SWS and REM sleep in late pregnancy. Conversely, women spent more time in stage N2 sleep. In this study, women even in early pregnancy have disturbed sleep represented by high WASO (32%), low TST (∼6 hours) and low SE (77%). This could be explained by the large proportion of African Americans (75% of our subjects) our study, as our finding is in agreement with similar results from pregnant and nonpregnant cohorts, showing that African-American women had the shortest sleep duration, the worst sleep continuity, and latest sleep midpoint.36,37 Decreased mean nocturnal sleep time in late pregnancy is not an unexpected finding, considering that sleep disturbances increase in late pregnancy.1,11 In this study, we did not find a significant change over time in latency to sleep onset or latency to REM sleep (though first-night effect could hypothetically have prolonged latency to sleep onset during the early pregnancy PSG). Our findings indicate that women in late pregnancy do not have difficulty initiating sleep, but have more difficulty maintaining sleep. Multiple awakenings and difficulty returning back to sleep may be caused by frequent urination, physical discomfort and aches, leg cramps, spontaneous awakenings, and increased AHI and PLMI during pregnancy which have been reported in pregnancy1,5,6,10 Additionally, we found that women spent considerably less time sleeping in the supine position in the late pregnancy, a finding that may be reinforced by advice from the obstetrical community to pregnant women against sleeping supine.38 Women may also experience difficulty maintaining sleep due to having difficulty finding a comfortable position, which can contribute to changes in their sleep pattern during pregnancy, especially in late pregnancy.1,11 Furthermore, a low percentage of REM sleep and difficulty maintaining sleep combined with poor SE and more WASO in late pregnancy may be a result of sleep fragmentation associated with high levels of circulating progesterone during late pregnancy. Wilson et al. reported that higher progesterone levels in late pregnancy was associated with increased awakenings and more time awake after sleep onset.6 Sharkey et al. also observed a similar association between increased level of progesterone from the follicular phase through the mid-luteal phase and increased WASO in nonpregnant women.39

In our study, SWS decreased statistically from early to late pregnancy. Lee et al.5 recruited 29 women before pregnancy and followed them until 3 months postpartum; they reported that TST and the percentage of SWS decreased and did not return to prepregnancy values by the third month postpartum.5 In a comprehensive EEG analysis done for the current study, EEG delta power, which constitutes the major part of SWS during NREM, significantly decreased during pregnancy. An earlier report from Brunner et al. also noted a reduction in delta power from early to late pregnancy, but no significant change in SWS (minutes) during pregnancy.4 Delta EEG activity during NREM sleep reflects the intensity of a restorative process40 that reverses the effects of wakefulness on the brain. In our study, daytime napping is unlikely to have contributed to decreased delta power in late pregnancy because the frequency of naps did not change during pregnancy, nor did we find an interaction between nap frequency and TST.

Another significant finding of this study is the reduction of REM sleep from early to late pregnancy. This finding concurs with those of previous studies.4,6,8,9 However, other studies did not find a significant change in the percentage of REM sleep.5,7 Evidence related to changes in stage N1 sleep has also been inconsistent. In the current study, we observed no statistically significant change in stage N1 sleep, although there was a trend toward reduced stage N1 in late pregnancy. This is similar to the findings of earlier studies.4,5,7,8 However, other studies found an increase in stage N1 sleep.6,9 This inconsistent result might be explained by the small sample sizes in many of these studies, differences in study designs such as cross-sectional versus longitudinal design, laboratory versus home-based PSG, and variations in how much time the women slept during the PSG night.

We observed that AHI and PLMI increased during pregnancy, which can adversely affect sleep continuity and quality, and more importantly can contribute to adverse pregnancy outcomes. Furthermore, we found that the reduction of delta power from early to late pregnancy was associated with an increased severity of AHI. Specifically, we showed that with every one-point increase in AHI from early to late pregnancy, delta power decreased by approximately 4%. This is the first study, to our knowledge, that has evaluated the changes in delta power in conjunction with changes in AHI severity and other established factors that affect delta power, and the only study with a large sample size to have two assessments of delta power in early and late pregnancy. These data provide new evidence in support of our premise that increased severity of SDB decreases delta power during pregnancy, which can be a potential risk factor for adverse pregnancy outcomes because of SWS, particularly EEG delta power on important hormones and metabolic functions. For example, the secretion of growth hormone has been closely associated with SWS and delta power of the sleep EEG.41 Growth hormone stimulates growth, cell reproduction, cell regeneration, reduced insulin sensitivity, and increased plasma glucose levels.41

We also found that beta-2 power (25–32 Hz) in NREM sleep significantly increased from early to late pregnancy. The beta EEG band in NREM is reported to be elevated particularly in women with primary insomnia.25 A number of studies have noted that the highest prevalence of insomnia and the most wake episodes occur in late pregnancy.1,11,17 Cortical electro-physiological signals in the beta band have been hypothetically assumed to reflect coherent cortical processing of sensory information. Thus, an EEG power increase in beta-2 frequency band during sleep can be interpreted as a marker of cortical hyperarousal.26,27 The hyperarousal during sleep in late pregnancy may be related to women's concerns about labor, the health of the fetus, pregnancy complications, and balancing new life with a new baby and job, resulting in failure to disengage cognitive processes during sleep.27 In this study we did not ask a specific question of women about reasons for difficulty in sleeping.

The current study had a number of significant strengths and a few limitations of note. One of the strengths is that it is a longitudinal study with intrasubject comparisons from early to late pregnancy. This is one of the largest existing studies in pregnant women to measure objective sleep parameters using PSG and is the largest study employing comprehensive analysis of EEG via power spectral analysis. These features of the study allowed us to assess the temporal relation of important sleep parameters to progression through pregnancy. Our study provides new insights into sleep patterns and EEG frequency bands in pregnancy. Our subjects were also mostly urban, African-American women, who are traditionally underrepresented in research studies but have high rates of sleep disturbances. Thus, our results may not be generalizable to other populations. Our findings are limited to the early and late pregnancy period, as we did not perform assessments of the participants prior to conception or postpartum. Analyses performed here should be expanded to these groups (eg, suburban, white women or patients before and after pregnancy) to extent of generalizability. Similarly, a comparison group of nonpregnant women would be helpful to rule out threats to internal validity and assess whether the changes in sleep pattern were unique to pregnancy. Furthermore, there is a possibility of bias resulting from the missing PSG data in late pregnancy; however, we did not find significant differences in demographic, gynecological, or baseline sleep variables (eg, age, race, parity, baseline AHI) between those who did or did not complete both PSG tests. Moreover, we relied on mixed-effects models for analysis, which provide accurate estimates with missing data under fairly unrestrictive missing data assumptions. Future studies should work to replicate the associations found here, particularly for novel associations or any outcomes where there remains inconsistency in the literature.

In conclusion, this study showed that sleep characteristics change from early to late pregnancy, including decreased REM sleep and delta EEG power, and increased stage N2 sleep and WASO. Novel findings of this study are that the level of delta power during pregnancy may decrease with increased severity of SDB. Thus, our findings suggest links between decreased delta power level and increased severity of SDB. Given observations of an association between sleep disturbances, short sleep duration, sleep apnea, and adverse pregnancy outcomes, our findings add to our understanding of the changes in sleep characteristics during pregnancy. Increased awareness is needed regarding sleep changes and their implications in adverse pregnancy outcomes among health care providers, and our results help to aid in this awareness. These results also provide the foundation for future longitudinal studies investigating the association of adverse pregnancy outcomes and changes in EEG frequency band and the effect of interventions on sleep during pregnancy.


All authors have seen and approved the manuscript. The authors report no conflicts of interest.



apnea-hypopnea index


body mass index




non-rapid eye movement


obstructive sleep apnea


periodic limb movements index




rapid eye movement


standard deviation


sleep-disordered breathing


sleep efficiency


slow wave sleep


total sleep time


wake after sleep onset


The study was funded by the National Institute of Nursing Research (K99NR013187), Eunice Kennedy Shriver National Institute of Child Health & Human Development (K23HD041465), and National Heart, Lung, and Blood Institute (T32 HL07953). The authors thank Professor Allan Pack from the University of Pennsylvania for his advice and support with the research protocol.



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Supplemental Material

Supplemental Material

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