Sleep Events Research Articles

Expiratory muscle strength training reduces oxidative stress and systemic inflammation in men with obstructive sleep apnea syndrome: a double-blinded, randomized parallel trial

Abstract Study Objectives This study aimed to evaluate and compare the effects of high and low-intensity expiratory muscle strength training (EMST) on disease severity, systemic inflammation, oxidative stress, respiratory muscle strength, exercise capacity, symptoms, daytime sleepiness, fatigue severity, and sleep quality in male patients with obstructive sleep apnea syndrome (OSAS). Methods Thirty-one male patients diagnosed with moderate OSAS were included in this double-blind, randomized, parallel study. Patients were randomized into two groups: High-EMST and Low-EMST groups. EMST was used at home 7 days/week, once a day, for 25 breaths, 12 weeks. Respiratory muscle strength was measured using a mouth pressure device. Disease severity (Apnea–Hypopnea Index [AHI]) and, respiratory sleep events by polysomnography, total oxidant level(TOS), total antioxidant level(TAS), oxidative stress index (OSI), C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-10 (IL-10) levels by blood serum were evaluated. Results The percentage of AHI change in the high-EMST group(50.8%) was significantly higher than in the low-EMST group(6.3%; p = .002, d = 1.31). In general, as MEP increased by one unit, AHI decreased by 0.149 points (b = −0.149; CR = −3.065; p = .002), and as AHI increased by one unit, ODI increased by 0.746 points (b = 0.746; CR = 10.604; p < .001). TOS, OSI, TNF-α and IL-6 levels decreased at similar rates in both groups. Conclusions EMST significantly reduces systemic inflammation and oxidative stress while improving expiratory muscle strength in male patients with moderate OSAS. High-EMST is more effective in enhancing the severity of disease than low-EMST. EMST is a practical, effective, and promising treatment for pulmonary rehabilitation in patients with moderate OSAS. Clinical Trials Effect of EMST systemic inflammation and oxidative stress in patients with moderate OSAS, https://clinicaltrials.gov/study/NCT05242406, with the number NCT05242406.

Assessing Response Rates and Sleep Disorder Prevalence: Insights from a Propranolol Treatment Study for Infantile Haemangiomas.

Infantile haemangiomas (IHs) sometimes require treatment with propranolol. Sleep disturbances are the most frequently reported side effects. Monitoring adverse drug events necessitates repeated hospital visits, which can be challenging during a pandemic. To explore the effectiveness of a new electronic questionnaire in identifying sleep disturbances related to treatment with propranolol and potential confounding factors. To evaluate the response rate to the questionnaire. To report the proportion of patients on propranolol with sleep disturbances. In an observational, prospective cohort study, caregivers provided clinical information during ambulatory visits and via an electronic questionnaire after an 8-week treatment course with propranolol and at the time of treatment interruption. Adverse drug reaction reporting forms were assessed for causality. The questionnaire response rate was 91%, and the completion rate was 100%. A total of 59% of patients experienced sleep disturbances during propranolol treatment, which were considered adverse reactions. Sleep disorders were frequent during sleep regression phases and in subjects who fell asleep during physical contact with caregivers or bed-sharing with parents. The application of this questionnaire allows for identifying adverse sleep events associated with propranolol in IHs and potential confounders. Counselling on sleep hygiene is recommended before treatment onset.

Construction and evaluation of a predictive model for the types of sleep respiratory events in patients with OSA based on hypoxic parameters.

To explore the differences and associations of hypoxic parameters among distinct types of respiratory events in patients with obstructive sleep apnea (OSA) and to construct prediction models for the types of respiratory events based on hypoxic parameters. A retrospective analysis was conducted on a cohort of 67 patients with polysomnography (PSG). All overnight recorded respiratory events with pulse oxygen saturation (SpO2) desaturation were categorized into four categories: hypopnea (Hyp, 3409 events), obstructive apnea (OA, 5561 events), central apnea (CA, 1110 events) and mixed apnea (MA, 1372 events). All event recordings were exported separately from the PSG software as comma-separated variable (.csv) files, which were imported into custom-built MATLAB software for analysis. Based on 13 hypoxic parameters, artificial neural network (ANN) and binary logistic regression (BLR) were separately used for construction of Hyp, OA, CA and MA models. Receiver operating characteristic (ROC) curves were employed to compare the various predictive indicators of the two models for different respiratory event types, respectively. Both ANN and BLR models suggested that 13 hypoxic parameters significantly influenced the classification of respiratory event types; The area under the ROC curves of the ANN models surpassed those of traditional BLR models respiratory event types. The ANN models constructed based on the 13 hypoxic parameters exhibited superior predictive capabilities for distinct types of respiratory events, providing a feasible new tool for automatic identification of respiratory event types using sleep SpO2.

Leveraging Attention-reinforced UWB Signals to Monitor Respiration during Sleep

The respiration state during overnight sleep is an important indicator of human health. However, existing contactless solutions for sleep respiration monitoring either perform in controlled environments and have low usability in practical scenarios or only provide coarse-grained respiration rates, being unable to accurately detect abnormal events in patients. In this article, we propose Respnea, a non-intrusive sleep respiration monitoring system using an ultra-wideband device. Particularly, we propose a profiling algorithm, which can locate the sleep positions in non-controlled environments and identify different subject states. Further, we construct a deep learning model that adopts a multi-head self-attention mechanism and learns the patterns implicit in the respiration signals to distinguish sleep respiration events at a granularity of seconds. To improve the generalization of the model, we propose a contrastive learning strategy to learn a robust representation of the respiration signals. We deploy our system in hospital and home scenarios and conduct experiments on data from healthy subjects and patients with sleep disorders. The experimental results show that Respnea achieves high temporal coverage and low errors (a median error of 0.27 bpm) in respiration rate estimation and reaches an accuracy of 94.44% on diagnosing the severity of sleep apnea-hypopnea syndrome.

Sleeping Stage Classification Using CNN

Abstract: Maintaining proper health and mental stability is critical for overall health and well-being. Despite a good deal of research investment, sleep quality continues to be a crucial public challenge. Nowadays, people of all age groups are affected by improper sleep quality. Poor sleep can lead to a variety of neurological disorders. Sleep disorders are common in all subsets of the population, independently of gender. This public health challenge greatly affects quality of life in terms of both physical and mental health. Insomnia, parasomnias, sleep- related breathing difficulties, hypersomnia, bruxism, narcolepsy, and circadian rhythm disorders are some common examples of sleep-related disorders. Some of these disorders can be treated with proper analysis of early symptoms; in such cases, adequate sleep quality is essential for the patient’s recovery. Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleepdisorders, and population health. While still in its nascent stage, there are several challenges which preclude AI’s generalizability and wide-reaching clinical applications. Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms and deep learning algorithm prior to its inclusion in the sleep clinic. In this project, we can develop the framework for sleeping stage classification for both subjective data and ECG data and classify the data using Convolutional neural network algorithm to analyse the multiple sleeping stages.

EarSleep: In-ear Acoustic-based Physical and Physiological Activity Recognition for Sleep Stage Detection

Since sleep plays an important role in people's daily lives, sleep monitoring has attracted the attention of many researchers. Physical and physiological activities occurring in sleep exhibit unique patterns in different sleep stages. It indicates that recognizing a wide range of sleep activities (events) can provide more fine-grained information for sleep stage detection. However, most of the prior works are designed to capture limited sleep events and coarse-grained information, which cannot meet the needs of fine-grained sleep monitoring. In our work, we leverage ubiquitous in-ear microphones on sleep earbuds to design a sleep monitoring system, named EarSleep1, which interprets in-ear body sounds induced by various representative sleep events into sleep stages. Based on differences among physical occurrence mechanisms of sleep activities, EarSleep extracts unique acoustic response patterns from in-ear body sounds to recognize a wide range of sleep events, including body movements, sound activities, heartbeat, and respiration. With the help of sleep medicine knowledge, interpretable acoustic features are derived from these representative sleep activities. EarSleep leverages a carefully designed deep learning model to establish the complex correlation between acoustic features and sleep stages. We conduct extensive experiments with 48 nights of 18 participants over three months to validate the performance of our system. The experimental results show that our system can accurately detect a rich set of sleep activities. Furthermore, in terms of sleep stage detection, EarSleep outperforms state-of-the-art solutions by 7.12% and 9.32% in average precision and average recall, respectively.

“Counting sheep PSG”: EEGLAB-compatible open-source matlab software for signal processing, visualization, event marking and staging of polysomnographic data

BackgroundProgress in advancing sleep research employing polysomnography (PSG) has been negatively impacted by the limited availability of widely available, open-source sleep-specific analysis tools. New methodHere, we introduce Counting Sheep PSG, an EEGLAB-compatible software for signal processing, visualization, event marking and manual sleep stage scoring of PSG data for MATLAB. ResultsKey features include: (1) signal processing tools including bad channel interpolation, down-sampling, re-referencing, filtering, independent component analysis, artifact subspace reconstruction, and power spectral analysis, (2) customizable display of polysomnographic data and hypnogram, (3) event marking mode including manual sleep stage scoring, (4) automatic event detections including movement artifact, sleep spindles, slow waves and eye movements, and (5) export of main descriptive sleep architecture statistics, event statistics and publication-ready hypnogram. Comparison with existing methodsCounting Sheep PSG was built on the foundation created by sleepSMG (https://sleepsmg.sourceforge.net/). The scope and functionalities of the current software have made significant advancements in terms of EEGLAB integration/compatibility, preprocessing, artifact correction, event detection, functionality and ease of use. By comparison, commercial software can be costly and utilize proprietary data formats and algorithms, thereby restricting the ability to distribute and share data and analysis results. ConclusionsThe field of sleep research remains shackled by an industry that resists standardization, prevents interoperability, builds-in planned obsolescence, maintains proprietary black-box data formats and analysis approaches. This presents a major challenge for the field of sleep research. The need for free, open-source software that can read open-format data is essential for scientific advancement to be made in the field.

Differences in Polysomnographic and Craniofacial Characteristics of Catathrenia Phenotypes: A Cluster Analysis.

Catathrenia is a rare sleeping disorder characterized by repetitive nocturnal groaning during prolonged expirations. Patients with catathrenia had heterogeneous polysomnographic, comorbidity, craniofacial characteristics, and responses to treatment. Identifying phenotypes of catathrenia might benefit the exploration of etiology and personalized therapy. Sixty-six patients diagnosed with catathrenia by full-night audio/video polysomnography seeking treatment with mandibular advancement devices (MAD) or continuous positive airway pressure (CPAP) were included in the cohort. Polysomnographic characteristics including sleep architecture, respiratory, groaning, and arousal events were analyzed. Three-dimensional (3D) and 2D craniofacial hard tissue and upper airway structures were evaluated with cone-beam computed tomography and lateral cephalometry. Phenotypes of catathrenia were identified by K-mean cluster analysis, and inter-group comparisons were assessed. Two distinct clusters of catathrenia were identified: cluster 1 (n=17) was characterized to have more males (71%), a longer average duration of groaning events (18.5±4.8 and 12.8±5.7s, p=0.005), and broader upper airway (volume 41,386±10,543 and 26,661±6700 mm3, p<0.001); cluster 2 (n=49) was characterized to have more females (73%), higher respiratory disturbance index (RDI) (median 1.0 [0.3, 2.0] and 5.2 [1.2, 13.3]/h, p=0.009), more respiratory effort-related arousals (RERA)(1 [1, 109] and 32 [13, 57)], p=0.005), smaller upper airway (cross-sectional area of velopharynx 512±87 and 339±84 mm2, p<0.001) and better response to treatment (41.2% and 82.6%, p=0.004). Two distinct phenotypes were identified in patients with catathrenia, primary catathrenia, and catathrenia associated with upper airway obstruction, suggesting respiratory events and upper airway structures might be related to the etiology of catathrenia, with implications for its treatment.

Nocturnal Heart Rate Response to Spontaneous Arousals Following Sleep Restriction

Short sleep duration is associated with future cardiovascular disease including hypertension, especially in females, but mechanistic evidence is largely lacking. Experimental sleep restriction has been shown to cause short sleep-mediated tachycardia and reduced heart rate variability, indicative of autonomic cardiac instability, in some but not all studies. Heart rate (HR) response to spontaneous cortical arousals can be regarded as an index of autonomic cardiac control given its characteristic temporal pattern. Whether the HR response to cortical arousals is altered during experimental sleep restriction remains unknown. We hypothesized that sleep restriction would result in augmented HR reactivity to spontaneous arousals, and that this response would be more marked in females. We studied 19 young, healthy subjects (eight females, age: 23.5 ± 5 years, body mass index [BMI]: 24.5 ± 3.6 kg/m2). In a randomized, crossover design, participants were tested during a normal sleep (NS, 9 hr sleep opportunity) or sleep restriction (SR, 4 hr sleep opportunity) study arm. Each 16-day study visit consisted of a 4-day study acclimation phase, 9-day experimental sleep perturbation segment, followed by a 3-day recovery period. An overnight polysomnography (PSG) study was conducted on study days 2, 5 and 11 (at baseline and after 1 and 7 days of restricted versus control sleep). A registered PSG technician provided sleep staging and spontaneous arousal scoring, which was confirmed by an investigator with sleep expertise (IMG). For each identified spontaneous arousal, the HR change was quantified for 30 cardiac cycles post-arousal onset and compared to a pre-arousal baseline (average HR 5 – 10 cardiac cycles prior to arousal). An arousal was considered valid for analysis if at least one minute of stage N2 or rapid eye movement (REM) sleep occurred following sleep stage transition and was at least 30 seconds apart from other sleep disturbance-related events (e.g., limb movement, apneic event, etc.). Given low prevalence of slow wave sleep (SWS) arousals, arousals occurring in that sleep stage were excluded from present analysis. Statistical analysis, including repeated measures ANOVA (RMANOVA, p &lt; 0.05) with condition and time (cardiac cycles) as within-subjects factors and sex as the between-subjects factor, was conducted to compare the beat-to-beat HR response to arousal between study conditions. Additionally, average and peak HR responses to spontaneous arousals were compared via RMANOVA with condition and time (study days) as within-subjects and sex as between-subjects factors. HR response to spontaneous arousal in stage two and REM sleep was similar between NS and SR on study day 5 or day 11 (condition × time: p = 0.943, 0.865). Secondary analysis of average and peak HR responsiveness to spontaneous arousals in stage two and REM sleep between conditions and study days was also not statistically different (condition × time: p = 0.193, 0.185). Last, no significant sex differences were detected across comparisons (condition × time × sex: p &gt; 0.05 for all). These findings suggest that a 9-day sleep restriction paradigm may not alter cardiac autonomic function in otherwise healthy adults. Future work is warranted to determine whether these results extend to individuals with established cardiovascular risk factors or cardiovascular disease. Support: NIH T32-DK007013, R01-114676, and CTSA grant UL1-TR002377. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Novel Electrophysiological Signatures of Learning and Forgetting in Human Rapid Eye Movement Sleep.

Despite the known behavioral benefits of rapid eye movement (REM) sleep, discrete neural oscillatory events in human scalp electroencephalography (EEG) linked with behavior have not been discovered. This knowledge gap hinders mechanistic understanding of the function of sleep, as well as the development of biophysical models and REM-based causal interventions. We designed a detection algorithm to identify bursts of activity in high-density, scalp EEG within theta (4-8 Hz) and alpha (8-13 Hz) bands during REM sleep. Across 38 nights of sleep, we characterized the burst events (i.e., count, duration, density, peak frequency, amplitude) in healthy, young male and female human participants (38; 21F) and investigated burst activity in relation to sleep-dependent memory tasks: hippocampal-dependent episodic verbal memory and nonhippocampal visual perceptual learning. We found greater burst count during the more REM-intensive second half of the night (p < 0.05), longer burst duration during the first half of the night (p < 0.05), but no differences across the night in density or power (p > 0.05). Moreover, increased alpha burst power was associated with increased overnight forgetting for episodic memory (p < 0.05). Furthermore, we show that increased REM theta burst activity in retinotopically specific regions was associated with better visual perceptual performance. Our work provides a critical bridge between discrete REM sleep events in human scalp EEG that support cognitive processes and the identification of similar activity patterns in animal models that allow for further mechanistic characterization.

Personalised Exercise-Rehabilitation FOR people with Multiple long-term conditions (PERFORM): protocol for a randomised feasibility trial

IntroductionPersonalised Exercise-Rehabilitation FOR people with Multiple long-term conditions (PERFORM) is a research programme that seeks to develop and evaluate a comprehensive exercise-based rehabilitation intervention designed for people with multimorbidity, the...

On the use of transformer-based detection models for accurate sleep event annotation and analysis

On the use of transformer-based detection models for accurate sleep event annotation and analysis

Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity

Background/Objective: Automatic apnea/hypopnea events classification, crucial for clinical applications, often faces challenges, particularly in hypopnea detection. This study aimed to evaluate the efficiency of a combined approach using nasal respiration flow (RF), peripheral oxygen saturation (SpO2), and ECG signals during polysomnography (PSG) for improved sleep apnea/hypopnea detection and obstructive sleep apnea (OSA) severity screening. MethodsAn Xception network was trained using main features from RF, SpO2, and ECG signals obtained during PSG. In addition, we incorporated demographic data for enhanced performance. The detection of apnea/hypopnea events was based on RF and SpO2 feature sets, while the screening and severity categorization of OSA utilized predicted apnea/hypopnea events in conjunction with demographic data. ResultsUsing RF and SpO2 feature sets, our model achieved an accuracy of 94 % in detecting apnea/hypopnea events. For OSA screening, an exceptional accuracy of 99 % and an AUC of 0.99 were achieved. OSA severity categorization yielded an accuracy of 93 % and an AUC of 0.91, with no misclassification between normal and mild OSA versus moderate and severe OSA. However, classification errors predominantly arose in cases with hypopnea-prevalent participants. ConclusionsThe proposed method offers a robust automatic detection system for apnea/hypopnea events, requiring fewer sensors than traditional PSG, and demonstrates exceptional performance. Additionally, the classification algorithms for OSA screening and severity categorization exhibit significant discriminatory capacity.

A robust deep learning detector for sleep spindles and K-complexes: towards population norms

Sleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive functions that appear during sleep. Large-scale sleep studies would benefit from precise and robust automatic sleep event detectors, capable of adapting the variability in both electroencephalography (EEG) signals and expert annotation rules. We introduce the Sleep EEG Event Detector (SEED), a deep learning system that outperforms existing approaches in SS and KC detection, reaching an F1-score of 80.5% and 83.7%, respectively, on the MASS2 dataset. SEED transfers well and requires minimal fine-tuning for new datasets and annotation styles. Remarkably, SEED substantially reduces the required amount of annotated data by using a novel pretraining approach that leverages the rule-based detector A7. An analysis of 11,224 subjects revealed that SEED's detections provide better estimates of SS population statistics than existing approaches. SEED is a powerful resource for obtaining sleep-event statistics that could be useful for establishing population norms.

Video-PSG: An Intelligent Contactless Monitoring System for Sleep Staging.

Polysomnography (PSG) is the gold standard for sleep staging in clinics, but its skin-contact nature makes it uncomfortable and inconvenient to use for long-term sleep monitoring. As a complementary part of PSG, the video cameras are not utilized to their full potential, only for manual check of simple sleep events, thereby ignoring the potential for physiological and semantic measurement. This leads to a pivotal research question: Can camera be used for sleep staging, and to what extent? We developed a camera-based contactless sleep staging system in the Institute of Respiratory Diseases and created a clinical video dataset of 20 adults. The camera-based feature set, derived from both physiological signals (pulse and breath) and motions all measured from a video, was evaluated for 4-class sleep staging (Wake-REM-Light-Deep). Three optimization strategies were proposed to enhance the sleep staging accuracy: using motion metrics to prune measurement outliers, creating a more personalized model based on the baseline calibration of waking-stage physiological signals, and deriving a specialized feature for REM detection. It achieved the best accuracy of 73.1% (kappa = 0.62, F1-score = 0.75) in the benchmark of five sleep-staging classifiers. Notably, the system exhibited high accuracy in predicting the overall sleep structure and subtle changes between different sleep stages. The study demonstrates that camera-based contactless sleep staging is a new value stream for sleep medicine, which also provides clinical and technical insights for future optimization and implementation.

Sleep-Disordered Breathing: Statistical Characteristics Of Joint Recurrent Indicators In ЕЕG Activity

The purpose of this study was to identify promising candidates for the role of biomarkers associated with different degrees of the apnea-hypapnea index in patients using polysomnographic recordings. Material — The study used polysomnography data recorded in 30 patients with nocturnal respiratory dysfunction in the form of obstructive sleep apnea syndrome. Methods — Analysis of polysomnographic recordings was carried out using a joint recurrent indicator, for which further statistical characteristics were assessed: average value, geometric mean, cubic mean, median, dispersion, standard deviation, the coefficient of variation, asymmetry indicator, kurtosis indicator. Results — For all polysomnographic recordings, joint recurrence diagrams were calculated to identify time points corresponding to specific sleep events in patients with high and low apnea-hypnea index. Based on the statistical characteristics of such events, possible candidates for the role of biomarkers to diagnose apnea syndrome are introduced. Conclusion — The article presents clustering parameters and the efficiency of dividing into clusters of statistical characteristics for two groups of patients - with high and low apnea-hypnea index. Characteristics have been identified that are promising candidates for the role of biomarkers associated with the apnea-hypnea index value.

Contributions of the light environment and co-sleeping to sleep consolidation into nighttime in early infants: A pilot study

BackgroundSleep consolidation into nighttime is considered the primary goal of sleep development in early infants. However, factors contributing to sleep consolidation into nighttime remain unclear. AimTo clarify the influences of the light environment and nighttime co-sleeping on sleep consolidation into nighttime in early infants. Study designCross-sectional study. Subjects and methodsSleep–wake time and light stimulation were measured in infants for 4 consecutive days using actigraphy. The infants’ mothers were asked to complete a sleep events diary and a questionnaire about childcare, including “co-sleeping”, defined as when the infant and mother slept on the same surface throughout the night. Outcome measuresThe data were analyzed with a focus on daytime and nighttime sleep parameters. ResultsDaytime light stimulation reduced daytime “active sleep”, tended to reduce daytime sleep, and increased daytime waking. Nighttime light stimulation reduced nighttime “quiet sleep” and nighttime sleep and increased nighttime waking. Co-sleeping reduced nighttime waking, and, as a result, nighttime sleep time and sleep efficiency increased. Co-sleeping reduced daytime sleep and tended to increase daytime waking. Consequently, co-sleeping tended to increase the ratio of nighttime sleep to daytime sleep. ConclusionsThe present findings suggest that an appropriate light environment promotes daytime waking and nighttime sleep in early infants, but it does not contribute to sleep consolidation into nighttime by itself. On the other hand, co-sleeping may promote sleep consolidation into nighttime. Therefore, further methods for safe co-sleeping need to be established while avoiding risk factors for sudden unexpected death in infancy/sudden infant death syndrome.

DAILY STRESS PROCESSES, PHYSICAL ACTIVITY, AND SLEEP IN OLDER ADULTHOOD

Abstract Older adults who engage in healthy behaviors such as being physically active and sleeping well report lower levels of stress. Less is known about the links between exposure and reactivity to stressful events in daily life and their relationship with physical activity and sleep. The current study examined within-person associations between daily physical activity, sleep, and exposure and affective reactivity to naturally occurring interpersonal stressors. Older adults (N = 180) from the Daily Experiences and Well-being Study completed ecological momentary assessments (EMAs) every 3 hours for 5-6 days where they reported negative affect throughout the day and interpersonal tensions at the end of the day. They also reported their nightly sleep quality and wore Actical accelerometers to capture physical activity. Older adults reported greater numbers of stressors on days when they spent less time being sedentary and engaged in more light physical activity. On days when older adults experienced more stressors, they reported higher levels of negative affect, but this association was attenuated when they were more physically active that day. Additionally, older adults reported lower sleep quality the night after they reported higher numbers of stressors. These findings underscore the importance of assessing physical activity, sleep and stressful events in daily life and have implications for both physical and psychological well-being.

Accelerometry reveals nocturnal biphasic sleep behavior in wild giraffe

Most research on mammalian resting and sleep behavior is conducted under controlled laboratory or zoo settings, with limited studies taking place in their natural habitats. To analyze these behaviors within a natural setting, actigraphy has been identified as a valuable method. This study sought to, firstly, measure inactivity and sleep in free-roaming Angolan giraffe (Giraffa giraffa angolensis) and, secondly, present their nocturnal behavioral rhythmicity. Three giraffe were fitted with an accelerometer-GPS device attached to an ossicone in November 2018 and behavioral data were collected daily between December 2018 and April 2019 to record their cranial movement. As giraffe show behavior specific head movements, sleep events could be detected as well as active and inactive behavior patterns. In this study, we analyzed how long giraffe were inactive during the night, how many sleep events they showed and for how long they were in the sleep position during the night. Giraffe were inactive for 490.8 ± 43.7 minutes during the night (from 19:00 to 7:00 local time), and a total of 857 sleep events were recorded with an average of 2.0 ± 1.0 events per night and total length of sleep per night averaged 8.6 ± 7.9 minutes. Further, results show a clear biphasic sleep profile during the night with peaks of inactivity and sleep events during the hours after sunset (21:00–23:00) and during the early morning hours before sunrise (3:00–5:00). We found individual differences for the number or sleep events, the total time sleeping as well as for inactivity. Interestingly, differences between months only were detected for inactivity. No differences were found for the number of sleep events nor for the total sleep time per night between months. For the first time, this study sheds light on the nocturnal sleep behavior and biphasic inactivity rhythm of giraffe in their natural habitat, providing additional support for previous findings indicating brief sleep episodes in giraffe.

The role of experience in prioritizing hippocampal replay

During sleep, recent memories are replayed by the hippocampus, leading to their consolidation, with a higher priority given to salient experiences. To examine the role of replay in the selective strengthening of memories, we recorded large ensembles of hippocampal place cells while male rats ran repeated spatial trajectories on two linear tracks, differing in either their familiarity or number of laps run. We observed that during sleep, the rate of replay events for a given track increased proportionally with the number of spatial trajectories run by the animal. In contrast, the rate of sleep replay events decreased if the animal was more familiar with the track. Furthermore, we find that the cumulative number of awake replay events occurring during behavior, influenced by both the novelty and duration of an experience, predicts which memories are prioritized for sleep replay, providing a more parsimonious neural correlate for the selective strengthening of memories.