Sleep Stages Research Articles

Sleep spindles and slow oscillations predict cognition and biomarkers of neurodegeneration in mild to moderate Alzheimer's disease.

Changes in sleep physiology can predate cognitive symptoms by decades in persons with Alzheimer's disease (AD), but it remains unclear which sleep characteristics predict cognitive and neurodegenerative changes after AD onset. Using data from a prospective cohort of mild to moderate AD (n=60), we analyzed non-rapid eye movement sleep spindles and slow oscillations (SOs) at baseline and their associations with baseline amyloid beta (Aβ) and tau and with cognition from baseline to 3-year follow-up. Higher spindle and SO activity predicted significant changes in Aβ and tau at baseline, lower Alzheimer's Disease Assessment Scale Cognitive Subscale (better cognitive performance) score, and higher Mini-Mental State Examination score from baseline to 36 months. Spindles and SOs mediated the effect of phosphorylated tau 181 (pTau181)/Aβ42 on cognition, while pTau181/aβ42 moderated the effect of spindles and SOs on cognition. Our findings demonstrate that spindle and SO activity during sleep constitute predictive and non-invasive biomarkers of neurodegeneration and cognition in AD patients. Sleep spindles predict long-term cognitive performance in AD. Sleep spindle and SOs can be predictive, non-invasive biomarkers for AD. Sleep may be one of the most important modifiable risk factors for AD progression. Sleep microarchitecture is a novel therapeutic target for preserving brain heath. Sleep physiology can provide novel therapeutic targets to slow AD progression.

PhysioEx, a new Python library for explainable sleep staging through deep learning.


Sleep staging is a crucial task in clinical and research contexts for diagnosing and understanding sleep disorders. This work introduces PhysioEx, a Python library designed to support the analysis of sleep stages using deep learning and Explainable AI (XAI). 

Approach:
PhysioEx provides an extensible and modular API for standardizing and automating the sleep staging pipeline, covering data preprocessing, model training, testing, fine-tuning, and explainability. It supports both low-resource devices and high-performance computing clusters and includes pretrained models based on the Sleep Heart Health Study (SHHS) dataset. These models support single-channel EEG and multichannel EEG-EOG-EMG configurations and are easily adaptable to custom datasets. PhysioEx also features a command-line interface toolbox allowing users to streamline the model development and deployment. The library offers a range of XAI post-hoc methods to explain model decisions and align them with expert knowledge. 

Main results:
PhysioEx benchmark state-of-the-art sleep staging models in a standard pipeline. Enabling a fair comparison between them both on the training source and out-of-domain sources. Its XAI techniques provide insights into deep learning-based sleep staging by linking model decisions to human-understandable concepts, such as AASM-defined rules. 

Significance:
PhysioEx addresses the need for a standardized and accessible platform for sleep staging analysis, combining deep learning and XAI. By supporting modular workflows and explainable insights, it bridges the gap between machine learning models and clinical expertise. PhysioEx is publicly available and installable via pip, making it a valuable tool for researchers and practitioners in sleep medicine.

Functional connectivity and metabolic brain alterations in sleepwalkers.

Disorders of arousal (DoA) are characterized by an intermediate state between wakefulness and deep sleep, leading to incomplete awakenings from NREM sleep. Multimodal studies have shown subtle neurophysiologic alterations even during wakefulness in DoA. The aim of this study was to explore the brain functional connectivity in DoA and the metabolic profile of the anterior and posterior cingulate cortex, given its pivotal role in cognitive and emotional processing. Fifteen consecutive patients with DoA (9 males, mean age 26.3 ± 7.7) and 15 age- and sex-matched healthy controls (8 males, mean age 25.8 ± 3.6) were enrolled. All participants underwent a protocol including sleep and psychological evaluation scales and multimodal brain MRI with resting-state functional MRI and 1H-MR spectroscopy. The independent component analysis disclosed an altered resting-state functional connectivity (FC) in the patients' sensory motor network, with a higher connectivity strength in opercular cortex, precuneus, occipital pole, and lingual gyrus. The seed-based analysis revealed a decreased FC between posterior cingulate cortex (PCC) and several cerebral areas. Finally, spectroscopic imaging revealed a reduced content of glutamine in the PCC (p < 0.001). The increased connectivity in the sensory-motor network of DoA patients could constitute a "facilitatory medium" enhancing motor circuit activation, while the connectivity and metabolic alterations of PCC might represent a trait functional feature, contributing to a dysfunctional arousal process and the difficulty to reach a complete awareness during DoA episodes. In addition, these alterations at rest might be related to daytime impairment reported by patients, requiring new strategies for DoA management.

Association of rapid eye movement sleep latency with multimodal biomarkers of Alzheimer's disease.

Sleep disturbances are associated with Alzheimer's disease (AD)and Alzheimer's disease and related dementias (ADRD), but the relationship between sleep architecture, particularly rapid eye movement (REM) sleep, and AD/ADRD biomarkers remains unclear. We enrolled 128 adults (64 with Alzheimer's disease, 41 with mild cognitive impairment [MCI], and 23 with normal cognition [NC]), mean age 70.8±9.6 years, 56.9% female, from a tertiary hospital in China. Participants underwent overnight polysomnography (PSG), amyloid β (Aβ) positron emission tomography (PET), and plasma biomarker analysis: phosphorylated tau at threonine 181 (p-tau181), neurofilament light (NfL), and brain-derived neurotrophic factor (BDNF). After adjusting for demographics, apolipoprotein E (APOE) ε4 status, cognition, and comorbidities, the highest tertile of REM latency was associated with higher Aβ burden (β=0.08, 95% confidence interval [CI]: 0.03 to 0.13, p=0.002), elevated p-tau181 (β=0.19, 95% CI: 0.02 to 0.13, p=0.002), and reduced BDNF levels (β=-0.47, 95% CI: -0.68 to -0.13, p=0.013), compared to the lowest tertile. Prolonged REM latency may serve as a novel marker or risk factor for AD/ADRD pathogenesis. Rapid eye movement latency (REML) may be a potential marker for Alzheimer's disease and Alzheimer's disease and related dementias (AD/ADRD) pathogenesis. Prolonged REML was associated with higher amyloid beta (Aβ) burden, phosphorylated tau-181 (p-tau181), and lower brain-derived neurotrophic factor (BDNF) levels. Intervention trial is needed to determine if targeting REML can modify AD/ADRD risk. Slow-wave sleep was not associated with AD/ADRD biomarkers.

Exploring the Psychological and Physiological Insights Through Digital Phenotyping by Analyzing the Discrepancies Between Subjective Insomnia Severity and Activity-Based Objective Sleep Measures: Observational Cohort Study.

Insomnia is a prevalent sleep disorder affecting millions worldwide, with significant impacts on daily functioning and quality of life. While traditionally assessed through subjective measures such as the Insomnia Severity Index (ISI), the advent of wearable technology has enabled continuous, objective sleep monitoring in natural environments. However, the relationship between subjective insomnia severity and objective sleep parameters remains unclear. This study aims to (1) explore the relationship between subjective insomnia severity, as measured by ISI scores, and activity-based objective sleep parameters obtained through wearable devices; (2) determine whether subjective perceptions of insomnia align with objective measures of sleep; and (3) identify key psychological and physiological factors contributing to the severity of subjective insomnia complaints. A total of 250 participants, including both individuals with and without insomnia aged 19-70 years, were recruited from March 2023 to November 2023. Participants were grouped based on ISI scores: no insomnia, mild, moderate, and severe insomnia. Data collection involved subjective assessments through self-reported questionnaires and objective measurements using wearable devices (Fitbit Inspire 3) that monitored sleep parameters, physical activity, and heart rate. The participants also used a smartphone app for ecological momentary assessment, recording daily alcohol consumption, caffeine intake, exercise, and stress. Statistical analyses were used to compare groups on subjective and objective measures. Results indicated no significant differences in general sleep structure (eg, total sleep time, rapid eye movement sleep time, and light sleep time) among the insomnia groups (mild, moderate, and severe) as classified by ISI scores (all P>.05). Interestingly, the no insomnia group had longer total awake times and lower sleep quality compared with the insomnia groups. Among the insomnia groups, no significant differences were observed regarding sleep structure (all P>.05), suggesting similar sleep patterns regardless of subjective insomnia severity. There were significant differences among the insomnia groups in stress levels, dysfunctional beliefs about sleep, and symptoms of restless leg syndrome (all P≤.001), with higher severity associated with higher scores in these factors. Contrary to expectations, no significant differences were observed in caffeine intake (P=.42) and alcohol consumption (P=.07) between the groups. The findings demonstrate a discrepancy between subjective perceptions of insomnia severity and activity-based objective sleep parameters, suggesting that factors beyond sleep duration and quality may contribute to subjective sleep complaints. Psychological factors, such as stress, dysfunctional sleep beliefs, and symptoms of restless legs syndrome, appear to play significant roles in the perception of insomnia severity. These results highlight the importance of considering both subjective and objective assessments in the evaluation and treatment of insomnia and suggest potential avenues for personalized treatment strategies that address both psychological and physiological aspects of sleep disturbances. Clinical Research Information Service KCT0009175; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=26133.

Wearable Neurotechnology for the Treatment of Insomnia: The Study Protocol of a Prospective, Placebo-Controlled, Double-Blind, Crossover Clinical Trial of a Transcranial Electrical Stimulation Device

Sleep disruption and deprivation are epidemic problems in the United States, even among those without a clinically diagnosed sleep disorder. Military service members demonstrate an increased risk of insomnia, which doubles after deployment. This study will investigate the ability of a translational device, Teledyne PeakSleep™ (Teledyne Scientific &amp; Imaging, Durham, NC, USA), to reduce sleep onset latency and the time spent awake after sleep onset, with improvement in the subjective benefits of sleep for patients with insomnia by enhancing the brain rhythms within the frontal lobe implicated in slow wave generation. During this crossover trial, patients will use the wearable neurotechnology prototype headband, which delivers &lt; 14 min of frontal short duration repetitive–transcranial electrical stimulation over a 30 min period immediately before trying to fall asleep. Using active stimulation versus a sham paradigm, we will compare actigraphy data, physiological data, and subjective sleep measures against a pre-treatment baseline in the same patient over the course of the 8-week study. If successful, PeakSleep™ could address the final common pathway in insomnia, namely the onset and maintenance of slow-wave sleep (SWS), and accordingly has the potential to enhance sleep onset in a wide range of individuals, most importantly warfighters in whom efficient sleep onset may be critical for operational success.

"Multimodal Sleep Signal Tensor Decomposition and Hidden Markov Modeling for Temazepam-Induced Anomalies Across Age Groups".

Recent advances in multimodal signal analysis enable the identification of subtle drug-induced anomalies in sleep that traditional methods often miss. We develop and introduce the Dynamic Representation of Multimodal Activity and Markov States (DREAMS) framework, which embeds explainable artificial intelligence (XAI) techniques to model hidden state transitions during sleep using tensorized EEG, EMG, and EOG signals from 22 subjects across three age groups (18-29, 30-49, and 50-66 years). By combining Tucker decomposition with probabilistic Hidden Markov Modeling, we quantified age-specific, temazepam-induced hidden states and significant differences in transition probabilities. Jensen-Shannon Divergence (JSD) was employed to assess variability in hidden state transitions, with older subjects (50-66 years) under temazepam displaying heightened transition variability and network instability as indicated by a 48.57% increase in JSD (from 0.35 to 0.52) and reductions in network density by 12.5% (from 0.48 to 0.42) and modularity by 21.88% (from 0.32 to 0.25). These changes reflect temazepam's disruptive impact on sleep architecture in older adults, aligning with known age-related declines in sleep stability and pharmacological sensitivity. In contrast, younger subjects exhibited lower divergence and retained relatively stable, cyclical transition patterns. Anomaly scores further quantified deviations in state transitions, with older subjects showing increased transition uncertainty and marked deviations in REM-like to NREM state transitions. This XAI-driven framework provides transparent, age-specific insights into temazepam's impact on sleep dynamics, going beyond traditional methods by identifying subtle, pharmacologically induced changes in sleep stage transitions that would otherwise be missed. DREAMS supports the development of personalized interventions based on sleep transition variability across age groups, offering a powerful tool to understand temazepam's age-dependent effects on sleep architecture.

Explainable vision transformer for automatic visual sleep staging on multimodal PSG signals

Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the ‘black-box’ nature. In this study, we present SleepXViT, an automatic sleep staging system using Vision Transformer (ViT) that provides intuitive, consistent explanations by mimicking human ‘visual scoring’. Tested on KISS–a PSG image dataset from 7745 patients across four hospitals–SleepXViT achieved a Macro F1 score of 81.94%, outperforming baseline models and showing robust performances on public datasets SHHS1 and SHHS2. Furthermore, SleepXViT offers well-calibrated confidence scores, enabling expert review for low-confidence predictions, alongside high-resolution heatmaps highlighting essential features and relevance scores for adjacent epochs’ influence on sleep stage predictions. Together, these explanations reinforce the scoring consistency of SleepXViT, making it both reliable and interpretable, thereby facilitating the synergy between the AI model and human scorers in clinical settings.

Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice

We explore an innovative approach to sleep stage analysis by incorporating complexity features into sleep scoring methods for mice. Traditional sleep scoring relies on the power spectral features of electroencephalogram (EEG) and the electromyogram (EMG) amplitude. We introduced a novel methodology for sleep stage classification based on two types of complexity analysis, namely multiscale entropy and detrended fluctuation analysis. Our analysis revealed significant variances in these complexities, not only within the specific theta and delta bands but across a wide frequency spectrum. Based on these findings, we developed a sleep stage scoring model, termed Sleep Analyzer Complex (SAC), a convolutional neural network model that integrates these complexity features with conventional EEG spectrum and EMG amplitude analysis. This integrated model significantly enhances the accuracy of sleep stage identification, achieving an accuracy of 97.4–98.1% for novel wild-type mice, on par with the agreement level among human scorers (97.3–97.8%). The efficacy of SAC was validated through tests conducted on wild-type mice, and it demonstrated remarkable success in identifying sleep architecture abnormalities in narcoleptic mice as well. This approach not only facilitates automated scoring of sleep/wakefulness states but also holds the potential to uncover detailed physiological insights, thereby advancing EEG-based sleep research.

Application of Ear-EEG and Other Wearable Systems in Sleep Assessment

Nowadays, about one-third of adults have the symptoms of insomnia. To assess these issues, one of the traditional methods used is polysomnography (PSG). However, it is expensive and not widely accessible, highlighting the urgent need for advancements in wearable technology to provide a more practical alternative for continuous sleep monitoring at home. This paper aims to explore various sleep assessment methods, including electrocardiography (ECG), photoplethysmography (PPG), and actigraphy, which measure heart activity, pulse wave, and muscle movement, respectively. By evaluating these methods, the paper analyses their advantages and challenges, particularly focusing on their accuracy in sleep stage classification and comfort levels. At the same time, this study compares Ear-EEG systems with other methods to assess their effectiveness in sleep monitoring. The future development directions for Ear-EEG in sleep monitoring are also discussed, encompassing aspects such as age variability, design improvements, and integrating advanced algorithms. Future advancements in at-home sleep assessments are expected to treat sleep disorders more efficiently and conveniently through early detection of disorders and improved user-friendliness compared to PSG.

Calcineurin governs baseline and homeostatic regulations of non–rapid eye movement sleep in mice

Sleep need accumulates during waking and dissipates during sleep to maintain sleep homeostasis (process S). Besides the regulation of daily (baseline) sleep amount, homeostatic sleep regulation commonly refers to the universal phenomenon that sleep deprivation (SD) causes an increase of sleep need, hence, the amount and intensity of subsequent recovery sleep. The central regulators and signaling pathways that govern the baseline and homeostatic sleep regulations in mammals remain unclear. Here, we report that enhanced activity of calcineurin Aα (CNAα)-a catalytic subunit of calcineurin-in the mouse brain neurons sharply increases the amount (to ~17-h/d) and delta power-a measure of intensity-of non-rapid eye movement sleep (NREMS). Knockout of the regulatory (CnB1) or catalytic (CnAα and CnAβ) subunits of calcineurin diminishes the amount (to ~4-h/d) and delta power of baseline NREMS, but also nearly abrogates the homeostatic recovery NREMS following SD. Accordingly, mathematical modeling of process S reveals an inability to accumulate sleep need during spontaneous or forced wakefulness in calcineurin deficient mice. Moreover, calcineurin promotes baseline NREMS by antagonizing wake-promoting protein kinase A and, in part, by activating sleep-promoting kinase SIK3. Together, these results indicate that calcineurin is an important regulator of sleep need and governs both baseline and homeostatic regulations of NREMS in mice.

The relationship between chronic ankle instability and sleep behaviour

ABSTRACT Chronic Ankle Instability (CAI) is a condition characterized by giving-way episodes, instability and recurrent ankle sprains. Poor sleep can increase the risk of musculoskeletal injury and sleep is known to be an important aspect of injury recovery. However, the effect sleep has on those with CAI as well as its risk for recurrent episodes of giving-way remains unclear. The purpose of this study was to examine the relationship between sleep behaviour and giving-way episodes associated with CAI. Twenty-five participants with CAI (11 M/14 F, age = 22.9 ± 2.7 years, height = 171.9 ± 8.9 cm, mass = 76.7 ± 15.9 kg) were included in this study. All participants completed baseline patient-reported outcome measures and wore a fitness tracker that measured sleep for 1 month. Seven participants had a giving-way (GW, no-giving way = NWG) episode. Those with a giving-way episode spent significantly less time asleep (GW = 325.3 ± 63.2 min, NGW = 413.9 ± 49.5 min, p < 0.001, d = 1.659), less time in bed (GW = 384.9 ± 79.0 min, NGW = 473.1 ± 55.0 min, p = 0.002, d = 1.419), less minutes in REM (GW = 59.9 ± 19.9 min, NGW = 93.5 ± 25.4 min, p = 0.002, d = 1.400) and less minutes in light sleep (GW = 197.6 ± 51.5 min, NGW = 250.2 ± 34.4 min, p = 0.003, d = 1.328) compared to those without. In conclusion, this study shows that individuals with CAI who suffered a giving-way episode had poorer sleep behaviour the night before an episode.

REM Refines and Rescues Memory Representations: A New Theory

Abstract Despite extensive evidence on the roles of non-rapid eye movement (NREM) and REM sleep in memory processing, a comprehensive model that integrates their complementary functions remains elusive due to a lack of mechanistic understanding of REM’s role in offline memory processing. We present the REM Refining and Rescuing (RnR) Hypothesis, which posits that the principal function of REM sleep is to increase the signal-to-noise ratio within and across memory representations. As such, REM sleep selectively enhances essential nodes within a memory representation while inhibiting the majority (Refine). Additionally, REM sleep modulates weak and strong memory representations so they fall within a similar range of recallability (Rescue). Across multiple NREM-REM cycles, tuning functions of individual memory traces get sharpened, allowing for integration of shared features across representations. We hypothesize that REM sleep’s unique cellular, neuromodulatory, and electrophysiological milieu, marked by greater inhibition and a mixed autonomic state of both sympathetic and parasympathetic activity, underpins these processes. The RnR Hypothesis offers a unified framework that explains diverse behavioral and neural outcomes associated with REM sleep, paving the way for future research and a more comprehensive model of sleep-dependent cognitive functions.

Breathtaking dreams: reduced REM phenotype in REM-related sleep apnea

PurposeThe expression of the respiratory events in OSA is influenced by different mechanisms. In particular, REM sleep can highly increase the occurrence of events in a subset of OSA patients, a condition dubbed REM-OSA (often defined as an AHI 2 times higher in REM than NREM sleep). However, a proper characterization of REM-OSA and its pathological sequelae is still inadequate, partly because of limitations in the current definitions.MethodsWe propose a new interpretation of the REM-OSA definition, extending it from a AHI-ratio to a two-dimensional space, considering both time and events ratios in REM over NREM separately. Within this space, we analyzed current definitions of REM-OSA in three large clinical dataset and identified the underlying sources of heterogeneity.ResultsWe observed that REM-OSA and REM-independent-OSA subgroups exist. Some subgroups exhibited abnormal REM characteristics (e.g., REM-OSA with reduced time in REM). Others had OSA features that are intermediate between REM-independent-OSA participants and those with a clear disproportion of REM events.ConclusionWe found that a time and events’ ratio of REM and NREM allow a more precise characterization of REM-OSA subgroups. Our new interpretation can be used to bolster new research into REM-OSA pathophysiological mechanisms.

Lateralized differences in power spectra across different frequency bands during NREM sleep in patients with primary insomnia

Lateralized differences in power spectra across different frequency bands during NREM sleep in patients with primary insomnia

Multimodal Neural Network Analysis of Single-Night Sleep Stages for Screening Obstructive Sleep Apnea

Multimodal Neural Network Analysis of Single-Night Sleep Stages for Screening Obstructive Sleep Apnea

Evening Alpha-Lactalbumin Supplementation Alters Sleep Architecture and Reduces Morning Reaction Time in an Athletically Trained Population With Sleep Difficulties.

Evening consumption of a whey protein rich in the amino acid tryptophan, alpha-lactalbumin (ALAC), has previously shown to benefit sleep-particularly among poor sleepers. Given trained populations often experience sleep difficulty, this study investigated whether evening supplementation of ALAC would influence sleep outcomes, mood, and next-day cognitive performance within a trained population with sleep difficulties. Nineteen trained participants (females, n = 11) with sleep difficulties (Athlete Sleep Screening Questionnaire: 8.1 ± 3.1; Pittsburgh Sleep Quality Index: 10.5 ± 4.1) completed this double-blinded, counterbalanced, randomized, crossover trial. Forty grams of ALAC or control were supplemented 2hr presleep for three consecutive nights in a controlled environment, with sleep measured using dry electroencephalography. Blood samples were taken on the first evening of each experimental trial, with mood, sleepiness, and recovery assessed across the evening and morning. A cognitive testing battery was also completed each morning. During the ALAC condition, the primary findings were that participants had raised plasma tryptophan levels (p < .01), increased nonrapid eye movement Stage 2 sleep duration (CON: 205.9 ± 33.3; ALAC: 216.5 ± 33.1min), reduced rapid eye movement duration (CON: 110.8 ± 27.9; ALAC: 99.7 ± 23.1min), and improved reaction time in cognitive tests involving sensory motor speed, spatial orientation, and vigilant attention (p < .05). Data suggestevening supplementation of 40g ALAC alters sleep architecture and improves next-morning reaction time in trained populations with sleep difficulties. Therefore, trained individuals experiencing sleep difficulty may benefit from acute ALAC supplementation to assist next-day performance. Future research should investigate this effect within habitual environments, outside of a tightly controlled setting.

Self-Similarity Analysis of Heartbeat Fluctuations in Sleep Among Female Shift Workers

Cardiovascular signals exhibit self-similarity characteristics, which are influenced by changes in autonomic nervous system (ANS) regulation caused by shift work. This study aims to assess the self-similarity properties of inter-beat interval (IBI) in healthy female shift workers and non-shift workers in different sleep stages to detect alterations in heartbeat fluctuations due to shift work. Short- and long-term self-similarity properties of the IBI signal ([Formula: see text] and [Formula: see text] scaling exponents, respectively) were analyzed using Detrended Fluctuation Analysis. Time and frequency indices were also calculated. In addition, Principal Component Analysis (PCA) was employed to reduce dimensionality and evaluate group separability based on the obtained features. Most indices showed similar values in the different sleep stages for both groups, but [Formula: see text] during light sleep and sympathovagal balance during REM sleep showed a significant decrease in shift workers compared to non-shift workers ([Formula: see text]). In addition, PCA was able to separate shift workers from non-shift workers and differentiate between nighttime and daytime sleep of workers. This analysis aids in identifying cardiovascular impairment associated with shift work and suggests a loss of ANS self-similarity in shift workers, indicating reduced adaptive capacity. Such alterations in ANS behavior could lead to serious health consequences related to cardiovascular disease.

Statistical Complexity Analysis of Sleep Stages.

Studying sleep stages is crucial for understanding sleep architecture, which can help identify various health conditions, including insomnia, sleep apnea, and neurodegenerative diseases, allowing for better diagnosis and treatment interventions. In this paper, we explore the effectiveness of generalized weighted permutation entropy (GWPE) in distinguishing between different sleep stages from EEG signals. Using classification algorithms, we evaluate feature sets derived from both standard permutation entropy (PE) and GWPE to determine which set performs better in classifying sleep stages, demonstrating that GWPE significantly enhances sleep stage differentiation, particularly in identifying the transition between N1 and REM sleep. The results highlight the potential of GWPE as a valuable tool for understanding sleep neurophysiology and improving the diagnosis of sleep disorders.

The UNICA Sleep HRV Analysis tool: an integrated open-source tool for heart rate variability analysis during sleep.

Heart rate variability (HRV) analysis during sleep plays a key role for understanding autonomic nervous system function and assessing cardiovascular health. The UNICA Sleep HRV analysis (UNICA-HRV) tool is a novel, open-source MATLAB tool designed to fill the gap in current HRV analysis tools. In particular, the integration of ECG and HRV data with hypnogram information, which illustrates the progression through the different sleep stages, ease the computation of HRV metrics in polysomnographic recordings. This integration is crucial for accurate phase-specific analysis, as autonomic regulation changes markedly across different sleep stages. The tool supports single- and multiple-subject analyses and is tailored to enhance usability and accessibility for researchers and clinicians without requiring extensive technical expertise. It implements and support a variety of data inputs and configurations, allowing for flexible, detailed HRV analyses across sleep stages, employing classical and advanced metrics, such as time-domain, frequency-domain, non-linear, complexity, and Poincaré plots indexes. Validation of the tool against established tools like Kubios and PhysioZoo indicates its robustness and precision in generating reliable HRV metrics that are essential not only for sleep research, but also for clinical diagnostics. The introduction of UNICA-HRV represents a significant simplification for sleep studies, and its open-source nature (licensed under a Creative Commons Attribution 4.0 International License) allows to easily extend the functionality to other needs.