Neonatal Sleep Research Articles

Effect of Nesting and Swaddling on Sleep Duration of Preterm Neonate Hospitalized at Tertiary Care Centre of Eastern Nepal

Background: All preterm neonates’ sleep is disturbed during hospitalization period due to various stressful environmental factors and human error and without focusing in individual developmental care. This impacts neonatal neurological development as well as normal growth resulting lifelong disabilities. Thus, modification of the environment by intervening developmental care could minimize the iatrogenic effect and neuro-developmental delay by decreasing stressful environment. This study was conducted to assess the effect of nesting and swaddling on sleep duration of preterm neonates during hospitalization at B.P.Koirala Instituted of Health sciences (BPKIHS). Methods: True experimental (post-test control design) study conducted among 36 preterm neonates, admitted in Nursery and Neonatal Unit of BPKIHS. The objective of this study was to assess the effect of nesting and swaddling on sleep duration. Consecutive sampling technique was used for total sample size then after random allocation is applied for creates the samples into two groups: experimental and control. Performa was used to collect socio-demographic characteristics. Sleep duration was recorded in self-administer observation form base on the observational behavior characteristics scoring of neonatal sleep by AASM (American Academy of sleep Medicine). As intervention, nesting and swaddling were performed in experimental group, then the sleep was video-recorded for 2 hours for each neonate. Finally, data were analyzed using descriptive and inferential statistics. Results: Among the enrolled neonates, there were 23 male (63.9%) and 13 female (36.1%) preterm. There was significant difference in sleep duration between experimental and control group. The mean ± SD of sleep duration was 59.22 ± 22.61 minute in experimental group and 39.50 ± 8.06 minute in control with p-value 0.001. The mean sleep duration was not associated with other selected study variables except the performed intervention. Conclusions: Nesting and Swaddling can be used as convenient and effective method to increase neonatal sleep duration or rest period.

LANMAO sleep recorder versus polysomnography in neonatal EEG recording and sleep analysis

BackgroundThe field of neonatal sleep analysis is burgeoning with devices that purport to offer alternatives to polysomnography (PSG) for monitoring sleep patterns. However, the majority of these devices are limited in their capacity, typically only distinguishing between sleep and wakefulness. This study aims to assess the efficacy of a novel wearable electroencephalographic (EEG) device, the LANMAO Sleep Recorder, in capturing EEG data and analyzing sleep stages, and to compare its performance against the established PSG standard. MethodsThe study involved concurrent sleep monitoring of 34 neonates using both PSG and the LANMAO device. Initially, the study verified the consistency of raw EEG signals captured by the LANMAO device, employing relative spectral power analysis and Pearson correlation coefficients (PCC) for validation. Subsequently, the LANMAO device’s integrated automated sleep staging algorithm was evaluated by comparing its output with expert-generated sleep stage classifications. ResultsAnalysis revealed that the PCC between the relative spectral powers of various frequency bands during different sleep stages ranged from 0.28 to 0.48. Specifically, the correlation for delta waves was recorded at 0.28. The automated sleep staging algorithm of the LANMAO device demonstrated an overall accuracy of 79.60 %, Cohen kappa of 0.65, and F1 Score of 76.93 %. Individual accuracy for Wake at 87.20 %, NREM at 85.70 %, and REM Sleep at 81.30 %. ConclusionWhile the LANMAO Sleep Recorder’s automated sleep staging algorithm necessitates further refinement, the device shows promise in accurately recording neonatal EEG during sleep. Its potential for minimal invasiveness makes it an appealing option for monitoring sleep conditions in newborns, suggesting a novel approach in the field of neonatal sleep analysis.

Sleep as a driver of pre- and postnatal brain development.

In 1966, Howard Roffwarg proposed the ontogenic sleep hypothesis, relating neural plasticity and development to rapid eye movement (REM) sleep, a hypothesis that current fetal and neonatal sleep research is still exploring. Recently, technological advances have enabled researchers to automatically quantify neonatal sleep architecture, which has caused a resurgence of research in this field as attempts are made to further elucidate the important role of sleep in pre- and postnatal brain development. This article will review our current understanding of the role of sleep as a driver of brain development and identify possible areas for future research. IMPACT: The evidence to date suggests that Roffwarg's ontogenesis hypothesis of sleep and brain development is correct. A better understanding of the relationship between sleep and the development of functional connectivity is needed. Reliable, non-invasive tools to assess sleep in the NICU and at home need to be tested in a real-world environment and the best way to promote healthy sleep needs to be understood before clinical trials promoting and optimizing sleep quality in neonates could be undertaken.

REM sleep breathing: Insights beyond conventional respiratory metrics.

Breathing and sleep state are tightly linked. The traditional approach to evaluation of breathing in rapid eye movement sleep has been to focus on apneas and hypopneas, and associated hypoxia or hypercapnia. However, rapid eye movement sleep breathing offers novel insights into sleep physiology and pathology, secondary to complex interactions of rapid eye movement state and cardiorespiratory biology. In this review, morphological analysis of clinical polysomnogram data to assess respiratory patterns and associations across a range of health and disease is presented. There are several relatively unique insights that may be evident by assessment of breathing during rapid eye movement sleep. These include the original discovery of rapid eye movement sleep and scoring of neonatal sleep, control of breathing in rapid eye movement sleep, rapid eye movement sleep homeostasis, sleep apnea endotyping and pharmacotherapy, rapid eye movement sleep stability, non-electroencephalogram sleep staging, influences on cataplexy, mimics of rapid eye movement behaviour disorder, a reflection of autonomic health, and insights into cardiac arrhythmogenesis. In summary, there is rich clinically actionable information beyond sleep apnea encoded in the respiratory patterns of rapid eye movement sleep.

Quantitative Electroencephalography in Term Neonates During the Early Postnatal Period Across Various Sleep States.

Neonatal sleep is pivotal for their growth and development, yet manual interpretation of raw images is time-consuming and labor-intensive. Quantitative Electroencephalography (QEEG) presents significant advantages in terms of objectivity and convenience for investigating neonatal sleep patterns. However, research on the sleep patterns of healthy neonates remains scarce. This study aims to identify QEEG markers that distinguish between different neonatal sleep cycles and analyze QEEG alterations across various sleep stages in relation to postmenstrual age. From September 2023 to February 2024, full-term neonates admitted to the neonatology department at the Obstetrics and Gynecology Hospital of Fudan University were enrolled in this study. Electroencephalographic (EEG) recordings were obtained from neonates aged 37-42 weeks, within 1-7 days post-birth. The ROC curve was employed to evaluate QEEG features related to amplitude, range EEG (rEEG), spectral density, and connectivity across different sleep stages. Furthermore, regression analyses were performed to investigate the association between these QEEG characteristics and postmenstrual age. The alpha frequency band's spectral_diff_F3 emerged as the most potent discriminator between active sleep (AS) and quiet sleep (QS). In distinguishing AS from wakefulness (W), the theta frequency's spectral_diff_C4 was the most effective, whereas the delta frequency's spectral_diff_P4 excelled in differentiating QS from W. During AS and QS phases, there was a notable increase in entropy within the delta frequency band across all monitored brain regions and in the spectral relative power within the theta frequency band, correlating with postmenstrual age (PMA). Spectral difference showcases the highest discriminative capability across awake and various sleep states. The observed patterns of neonatal QEEG alterations in relation to PMA are consistent with the maturation of neonatal sleep, offering insights into the prediction and evaluation of brain development outcomes.

Sex differences in cardiac transcriptomic response to neonatal sleep apnea.

Pediatric obstructive sleep apnea poses a significant health risk, with potential long-term consequences on cardiovascular health. This study explores the dichotomous nature of neonatal cardiac response to chronic intermittent hypoxia (CIH) between males and females, aiming to fill a critical knowledge gap in the understanding of sex-specific cardiovascular consequences of sleep apnea in early life. Neonates were exposed to CIH until p28 and underwent comprehensive invivo physiological assessments, including whole-body plethysmography, treadmill stress-tests, and echocardiography. Results indicated that male CIH rats weighed 13.7% less than age-matched control males (p = 0.0365), while females exhibited a mild yet significant increased respiratory drive during sleep (93.94 ± 0.84 vs. 95.31 ± 0.81;p = 0.02). Transcriptomic analysis of left ventricular tissue revealed a substantial sex-based difference in the cardiac response to CIH, with males demonstrating a more pronounced alteration in gene expression compared to females (5986 vs. 3174 genes). The dysregulated miRNAs in males target metabolic genes, potentially predisposing the heart to altered metabolism and substrate utilization. Furthermore, CIH in males was associated with thinner left ventricular walls and dysregulation of genes involved in the cardiac action potential, possibly predisposing males to CIH-related arrhythmia. These findings emphasize the importance of considering sex-specific responses in understanding the cardiovascular implications of pediatric sleep apnea.

Failure of an electric rocking device to improve neonatal sleep.

We wanted to investigate whether an electric rocking device (Swing2Sleep, Neumünster, Germany), sold with the claim to promote infant sleep, would increase total sleep time or reduce sleep latency once infants are put therein. In a randomised crossover design, 20 infants (median gestational age at birth 31.7 weeks, range 25-39) were placed to sleep either first with the device rocking, then not rocking (or vice versa) for 5-7 h each. The device consisted of a hammock with three spiral springs that performed vertical swings at a rate of 100/min and an amplitude of 2.5 cm. There was no significant difference in %time spent asleep (83 (22-97) vs. 85% (49-96)), sleep latency (7.7 (2-45) vs. 12.3 (4-42) min), sleep fragmentation (1.3 (0.5-2.3) vs. 1.1 (0.2-5.5)) or efficiency (0.8 (0.2-1.0) vs. 0.9 (0.5-1.0)) between both conditions. At its recommended settings, the device did not achieve its intended effect in these infants.

The effect of facilitated tucking and white noise on stress and sleep of newborns receiving nasal continuous positive airway pressure

ObjectiveTo investigate the effects of facilitated tucking and white noise on stress and sleep in neonates receiving nasal continuous positive airway pressure (CPAP). MethodThis study was conducted as a randomised controlled experimental study of neonates receiving nasal CPAP in neonatal intensive care units. The study sample consisted of 108 newborns (facilitated tucking (n = 36), white noise (n = 36) and control (n = 36)) receiving nasal CPAP support in the NICU. The neonates' sleep parameters were recorded by actigraphy for 24 h. Data were collected using the Neonatal Descriptive Information Form, the Sleep Tracking Form and the Neonatal Stress Scale. Percentage, mean, chi-squared and one-way ANOVA were used for data analysis. ResultsIt was found that 50.9% of the newborns were female, their mean gestational age was 33.54 ± 3.38, their mean height was 43.56 ± 5.12, and their mean weight was 2139.23 ± 827.82. The total sleep time of the neonates in the facilitated tucking and white noise group increased by 3 h, their sleep efficiency increased by 20% and their mean stress scores decreased (p < 0.05). ConclusionFacilitated tucking and white noise each showed a similar improvement in sleep duration and sleep efficiency and a reduction in stress scores in neonates receiving nasal CPAP. Close monitoring of sleep in this population and supportive care practices are recommended. Practice implicationsThe findings of this study may help to reduce sleep problems and stress levels in the clinical care of neonates in the NICU through developmental nursing practices.

Web Integrated Continuous Positive Airway Pressure System

Keywords: Continuous Positive Airway Pressure(CPAP) 1, Sleep Apnea 2, Neonatal 3, Respiratory Distress Syndrome (RDS) 4, Premature baby 5, Transpiration medical device 6, Internet of Things 7 CPAP, a non-invasive ventilation method crucial in neonatal care, has significant implications for the survival of very low birth weight preterm infants. However, its widespread adoption is hindered by cost constraints and the need for additional features like Apnea Hypopnea Index (AHI) monitoring. This study seeks to overcome these challenges by developing an affordable CPAP device for Neonatal Intensive Care Units (NICUs). By incorporating sensors for data collection and a microcontroller-based control system, this device aims to optimize CPAP function while minimizing energy consumption. Furthermore, the collected data will be securely stored on a cloud platform, potentially benefiting both neonatal care and sleep apnea management. Recognizing the vital role of sleep in human health, this innovation underscores the importance of restorative rest for physical and mental well-being. Highlights: · Web-integrated CPAP devices often come with remote monitoring capabilities. This allows healthcare providers to remotely monitor patients' CPAP usage data, including compliance and efficacy metrics, without requiring in-person visits. This feature facilitates better patient management and allows for timely intervention if issues arise. · These devices typically track and record various parameters such as usage hours, mask leaks, apnea-hypopnea index (AHI), and pressure settings. The data can be accessed by both patients and healthcare providers, providing insights into treatment progress and effectiveness over time. · Many web-integrated CPAP devices offer cloud connectivity, allowing users to sync their data to secure online platforms. This feature enables seamless access to CPAP usage data from multiple devices, such as smartphones, tablets, or computers, enhancing convenience and flexibility for users. · Some modern CPAP machines offer wireless connectivity options, such as Bluetooth or Wi-Fi, enabling users to connect their devices to compatible accessories or smartphone apps. This connectivity allows for features like remote control of device settings, automatic software updates, and integration with health tracking apps for a more comprehensive health management experience. · The integration of web-based CPAP platforms with telemedicine services allows for virtual consultations between patients and healthcare providers. This feature enhances access to care, particularly for patients in remote or underserved areas, and enables real-time adjustments to CPAP settings based on patient feedback and data analysis.

An IoT and Machine Learning-based Neonatal Sleep Stage Classification

Sleep, in neonates, is used to access the quality of brain and physical development. Typically, neonatal sleep has been divided into three stages: active sleep (AS), quiet sleep (QS), and intermediate sleep (IS). Polysomnography (PSG) is considered a gold standard to classify sleep. To address this issue, over the past two decades, researchers proposed multiple algorithms for automatic sleep stage classification. These algorithms work achieved outstanding results i.e. quiet sleep detection still, lacks in many aspects. One major drawback of the existing research is amalgamation of awake and active sleep into low voltage irregular (LVI) state. This amalgamation corrupts 40% of the overall EEG signal. For this reason, we proposed an algorithm for neonatal sleep-wake classification using machine learning. The proposed research is divided into three steps. Firstly, the EEG signal was pre-processed using finite impulse response filter to remove the noise and artifacts. Clean EEG signal is then divided into 4560 30-sec segments. Then, twenty prominent EEG features were extracted from time, frequency, and spatial domain. After feature extraction, support vector machine was used for sleep stage classification. The propounded study outperformed all the existing algorithms for sleep-wake classification with a mean accuracy of 83.7%. Four-fold cross-validation was used to validate the overall dataset. Multiple other performance matrices i.e. sensitivity, specificity, Kappa were calculated to prove the efficacy of the proposed study. Statistical results show that the proposed study can be used as a real-time neonatal sleep and Awake classification algorithm, as this did not use prior post-processing techniques.

A Sequential End-to-End Neonatal Sleep Staging Model with Squeeze and Excitation Blocks and Sequential Multi-Scale Convolution Neural Networks.

Automatic sleep staging offers a quick and objective assessment for quantitatively interpreting sleep stages in neonates. However, most of the existing studies either do not encompass any temporal information, or simply apply neural networks to exploit temporal information at the expense of high computational overhead and modeling ambiguity. This limits the application of these methods to multiple scenarios. In this paper, a sequential end-to-end sleep staging model, SeqEESleepNet, which is competent for parallelly processing sequential epochs and has a fast training rate to adapt to different scenarios, is proposed. SeqEESleepNet consists of a sequence epoch generation (SEG) module, a sequential multi-scale convolution neural network (SMSCNN) and squeeze and excitation (SE) blocks. The SEG module expands independent epochs into sequential signals, enabling the model to learn the temporal information between sleep stages. SMSCNN is a multi-scale convolution neural network that can extract both multi-scale features and temporal information from the signal. Subsequently, the followed SE block can reassign the weights of features through mapping and pooling. Experimental results exhibit that in a clinical dataset, the proposed method outperforms the state-of-the-art approaches, achieving an overall accuracy, F1-score, and Kappa coefficient of 71.8%, 71.8%, and 0.684 on a three-class classification task with a single channel EEG signal. Based on our overall results, we believe the proposed method could pave the way for convenient multi-scenario neonatal sleep staging methods.

An Ensemble Voting Approach With Innovative Multi-Domain Feature Fusion for Neonatal Sleep Stratification

An Ensemble Voting Approach With Innovative Multi-Domain Feature Fusion for Neonatal Sleep Stratification

The effect of a neonatal sleep intervention on maternal postpartum hypertension: a randomized trial

In nonpregnant adults, poor sleep is associated with higher blood pressure. Poor sleep is common in the postpartum period and is often attributed to infant caretaking needs. However, its effects on cardiovascular health in individuals with a hypertensive disorder of pregnancy are unknown. This study aimed to determine the effect of a neonatal sleep intervention on maternal postpartum blood pressure in individuals with a hypertensive disorder of pregnancy. In this single-institution pilot randomized controlled trial from July 2021 to March 2022, 110 individuals with a hypertensive disorder of pregnancy were randomized to receive a neonatal sleep intervention (SNOO responsive bassinet) plus usual care of safe sleep education (n=54) or usual care alone (n=56). Remote follow-up visits were conducted at 1 week, 6 weeks, and 4 months after delivery and involved blood pressure and weights, sleep and mood questionnaires, and self-reported infant and maternal sleep logs. Based on institutional data, the sample size had 80% power to detect a 4.5-mm Hg difference in the primary outcome of mean arterial pressure at 6 weeks after delivery. Baseline characteristics were similar between the arms. At 1 week after delivery, the intervention arm had lower mean arterial pressure and less antihypertensive medication use than the control arm (99±10 vs 103±7 mm Hg [P=.04] and 23% vs 35% [P=.15], respectively). At 6 weeks after delivery, mean arterial pressure was similar between arms (93±8 vs 94±8 mm Hg; P=.54), but there was a lower rate of antihypertensive use in the intervention arm (15% vs 26%; P=.19). Scores from maternal sleep and mood questionnaires at 6 weeks after delivery and self-reported infant and maternal sleep duration at 6 weeks and 4 months after delivery were similar between arms (P>.05). The SNOO responsive bassinet as a neonatal sleep intervention did not result in improved mean arterial pressure at 6 weeks after delivery after hypertensive disorders of pregnancy.

The Derivation of Epigastric Motion to Assess Neonatal Breathing and Sleep: An Exploratory Study.

New non-medical monitors are offered for respiration monitoring of neonates. Epigastric motion during sleep was investigated by means of a wearable tracker in parallel to clinical monitoring. 23 hospitalised neonates ready for discharge. A 3-axes-accelerometer and -gyroscope was placed in a standard epigastric position. Between two routine care rounds signals were recorded in parallel to monitoring of impedance pneumography (IP), ECG and pulse oximetry. Motion signals vs. time charts were evaluated using 10-min episodes and semiquantitatively assigned to breathing signal quality, regular breathing, periodic breathing and confounding artefacts. The results were compared with the impedance pneumographic data. 26 recordings (mean duration: 210 min/infant) were conducted without bradycardia or apnea alarm. The gestational age at birth ranged 28.9 to 41.1 and at recording from 35.6 to 42.3 postmenstrual weeks. Motion patterns of quiet sleep with regular breathing, periodic breathing and active sleep with confounding body movements were found. The longitudinal and transversal gyroscope axes resulted in best signal quality. Periodic breathing was found in up to 80% of episodes and decreased inversely with gestational age showing significantly more periodic breathing in preterm infants. Respiration signals of the gyroscope vs. IP showed a low bias and highly variating frequencies. Standardized motion trackers may detect typical neonatal breathing and body-motion-patterns, that could help to classify neonatal sleep. Respiratory rates can only be determined during quiet sleep.

Cumulative Diversity Pattern Entropy (CDEn): A High-Performance, Almost-Parameter-Free Complexity Estimator for Nonstationary Time Series

Tedious parameter settings and poor performances seriously affect the entropy estimation's effectiveness in time series analysis. To solve these limits, we propose a conceptually novel definition, cumulative diversity pattern entropy (CDEn), focusing on eliminating parameter selections and improving quantization accuracy, stability, and robustness. The CDEn algorithm consists of three steps: (1) improved phase-space reconstruction (IPSR) with constant embedding dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\bm{m\ }} = {\bm{\ }}2$</tex-math></inline-formula> and time delay <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\bm{\tau \ }} = {\bm{\ }}1$</tex-math></inline-formula> ; (2) diversity pattern partition generated by the cosine similarity between adjacent vectors; and (3) entropy calculation based on the normalized cumulative probability distribution. Numerical experiments are performed using 7 synthetic datasets and 15 baseline entropy methods for comparative validation. The results confirm CDEn's best description of chaotic/stochastic dynamics with the highest quantization accuracy and the lowest error rate of 2.04%. The coefficient of variation (CV) results also verify CDEn's excellent quantization stability with CV lower than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> . The relative change rate (RCR) results demonstrate that CDEn achieves the best robustness to data length and noise. Finally, the entropy algorithms are applied to a real-world dataset, i.e., neonatal sleep EEG analysis. The results further confirm that suggested CDEn outperforms the state-of-the-art entropy methods, with the minimum outliers and best statistical significance (highest mean of effect size, 1.22) in characterizing the neurodynamics of different sleep stages.

Evaluation of Sleep Practices and Knowledge in Neonatal Healthcare.

Developmental care is designed to optimize early brain maturation by integrating procedures that support a healing environment. Protecting preterm sleep is important in developmental care. However, it is unclear to what extent healthcare professionals are aware of the importance of sleep and how sleep is currently implemented in the day-to-day care in the neonatal intensive care unit (NICU). Identifying the current state of knowledge among healthcare professionals regarding neonatal sleep and how this is transferred to practice. A survey was distributed among Dutch healthcare professionals. Three categories of data were sought, including (1) demographics of respondents; (2) questions relating to sleep practices; and (3) objective knowledge questions relating to sleep physiology and importance of sleep. Data were analyzed using Spearman's rho test and Cramer's V test. Furthermore, frequency tables and qualitative analyses were employed. The survey was completed by 427 participants from 34 hospitals in 25 Dutch cities. While healthcare professionals reported sleep to be especially important for neonates admitted in the NICU, low scores were achieved in the area of knowledge of sleep physiology. Most healthcare professionals (91.8%) adapted the timing of elective care procedures to sleep. However, sleep assessments were not based on scientific knowledge. Therefore, the difference between active sleep and wakefulness may often be wrongly assessed. Finally, sleep is rarely discussed between colleagues (27.4% regularly/always) and during rounds (7.5%-14.3% often/always). Knowledge about sleep physiology should be increased through education among neonatal healthcare professionals. Furthermore, sleep should be considered more often during rounds and handovers.

Incubator-based active noise control device: comparison to ear covers and noise reduction zone quantification

BackgroundNoise exposure in the neonatal intensive care unit (NICU) is consistently higher than current recommendations. This may adversely affect neonatal sleep, weight gain, and overall health. We sought to evaluate the effect of a novel active noise control (ANC) system.MethodsAn ANC device’s noise reduction performance was compared to that of adhesively affixed foam ear covers in response to alarm and voice sounds in a simulated NICU environment. The zone of noise reduction of the ANC device was quantified with the same set of alarm and voice sounds.ResultsThe ANC device provided greater noise reduction than the ear covers in seven of the eight sound sequences tested in which a noise reduction greater than the just noticeable difference was achieved. For noise in the 500 Hz octave band, the ANC device exhibited consistent noise reduction throughout expected patient positions. It provided better performance for noise below 1000 Hz than above 1000 Hz.ConclusionsThe ANC device provided generally superior noise reduction to the ear covers and provided a zone of noise reduction throughout the range where an infant would be placed within an incubator. Implications for patient sleep and weight gain are discussed.ImpactActive noise control device can effectively reduce noise inside an infant incubator due to bedside device alarms.This is the first analysis of an incubator-based active noise control device and comparison to adhesively affixed silicone ear covers.A non-contact noise reduction device may be an appropriate means of reducing noise exposure of the hospitalized preterm infant.

Does sleep correlate with neurodevelopmental outcomes in preterm and term infants in early-preschool children?

Does sleep correlate with neurodevelopmental outcomes in preterm and term infants in early-preschool children?

A convolutional neural network-based decision support system for neonatal quiet sleep detection.

Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stage development. In this study, we propose an automatic and computationally efficient algorithm to detect neonatal quiet sleep (QS) using a convolutional neural network (CNN). Our study used 38-hours of electroencephalography (EEG) recordings, collected from 19 neonates at Fudan Children's Hospital in Shanghai, China (Approval No. (2020) 22). To train and test the CNN, we extracted 12 prominent time and frequency domain features from 9 bipolar EEG channels. The CNN architecture comprised two convolutional layers with pooling and rectified linear unit (ReLU) activation. Additionally, a smoothing filter was applied to hold the sleep stage for 3 minutes. Through performance testing, our proposed method achieved impressive results, with 94.07% accuracy, 89.70% sensitivity, 94.40% specificity, 79.82% F1-score and a 0.74 kappa coefficient when compared to human expert annotations. A notable advantage of our approach is its computational efficiency, with the entire training and testing process requiring only 7.97 seconds. The proposed algorithm has been validated using leave one subject out (LOSO) validation, which demonstrates its consistent performance across a diverse range of neonates. Our findings highlight the potential of our algorithm for real-time neonatal sleep stage classification, offering a fast and cost-effective solution. This research opens avenues for further investigations in early-stage development monitoring and the assessment of neonatal health.

The impact of a neonatal sleep intervention on sleep quality in the postpartum period

To determine the impact of a novel neonatal sleep intervention on maternal sleep quality in the postpartum period in individuals with a hypertensive disorder of pregnancy (HDP) and assess the relationship between maternal sleep quality and blood pressure. In this single institution pilot randomized controlled trial, 110 individuals with a HDP were randomized to receive a neonatal sleep intervention, a SNOO smart bassinet, plus usual care of safe sleep education (n=54) or usual care alone (n=56). Individuals were included if they were 18+ years old, had a singleton term live birth of a non-anomalous infant without a prolonged neonatal intensive care unit stay, and had a new HDP without prior chronic hypertension. Remote follow-up visits conducted at 6 weeks postpartum included vital signs and subjective sleep quality questionnaires including the Pittsburgh Sleep Quality Index (PSQI), Patient-Reported Outcomes Measurement Information System Sleep Disturbance (PROMIS-SD) questionnaire, and the Epworth Sleepiness Scale (ESS). Maternal sleep quality was a prespecified secondary outcome of this trial. Clinical and demographic characteristics were similar between arms (Table 1). Follow-up sleep data was available for 82 (75%) participants at 6 weeks (mean of 41.8±4.8 days) postpartum, with a higher follow rate in the SNOO arm (89% vs 61%) (Table 2). PSQI, PROMIS-SD, and ESS scores were similar between the two groups (p>0.05). The majority (88%) noted poor subjective sleep quality with PSQI score >5. There was no significant correlation between blood pressure and sleep quality (p>0.05) at 6 weeks postpartum. Use of the SNOO bassinet as a neonatal sleep intervention did not result in improved maternal subjective sleep quality at 6 weeks postpartum in our cohort of individuals with HDP. Self-reported sleep quality was not associated with blood pressure in this cohort of individuals with HDP.View Large Image Figure ViewerDownload Hi-res image Download (PPT)