Search Results (424)

Childhood absence epilepsy (CAE) is one of the most common epilepsy syndromes in childhood and has traditionally been regarded as a condition with a favorable neurological prognosis. However, increasing evidence suggests that CAE is associated with functional disturbances in neuronal networks that extend beyond seizure generation and may involve sleep and wakefulness regulation. Methods: This narrative mini-review summarizes and critically discusses current clinical and neurophysiological evidence regarding alterations in sleep architecture and sleep electroencephalographic (EEG) microstructures in children with CAE, based on a focused analysis of selected clinical and observational studies. Results: The available data suggest that children with CAE, particularly before treatment initiation, may exhibit sleep macrostructure abnormalities, including reduced total sleep time, prolonged rapid eye movement sleep latency, increased arousal frequency, and decreased sleep efficiency. In addition, changes in sleep microstructure have been reported, most notably reduced sleep spindle density during stage-N2 sleep, especially in patients with concomitant cognitive impairment. These findings may reflect alterations in thalamocortical network function, although current evidence remains limited and heterogeneous. Conclusions: Sleep disturbances appear to represent an important component of the clinical phenotype of childhood absence epilepsy. Assessing the sleep architecture and sleep EEG microstructure, particularly sleep spindles, may provide insights into network dysfunction and cognitive vulnerability; however, further studies are needed to clarify their clinical utility. Full article

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO₂, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber–physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022–2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization. Full article

This paper presents a modular system architecture and an automated signal processing pipeline designed to quantify neurophysiological relaxation responses to fragrance using consumer-grade wearable electroencephalography (EEG). By integrating real-time data streaming via Open Sound Control (OSC) with a high-performance backend, the platform enables objective assessment of olfactory stimuli through a reproducible Sleep Readiness Index (SRI) derived from spectral power shifts. To mitigate the signal quality constraints inherent in portable hardware, the framework utilizes a robust suite of engineering controls, including zero-phase filtering and automated artifact rejection, ensuring data integrity across short-window trials. Validation through construct-level analysis of public sleep datasets and synthetic sensitivity testing confirms the index’s directional reliability, while runtime benchmarking demonstrates sub-millisecond compute times suitable for interactive wellness applications. Ultimately, this framework provides a transparent, auditable engineering scaffold that replaces subjective self-reports with a standardized, within-session proxy metric for comparative fragrance evaluation. Full article

With the increasing demand for sleep health monitoring, automatic sleep staging using single-channel electroencephalogram (EEG) signals has become increasingly prominent due to its clinical practicality. Existing methods have achieved notable progress, but they often fail to adequately capture long-term temporal dependencies and struggle to characterize transition phases. We propose SleepLT, an automated sleep staging framework that integrates multi-scale wavelet decomposition (MWD) and multi-head latent Fourier attention (MLFA). The MLFA module incorporates Fourier analysis into self-attention mechanisms and employs a partially weight-sharing bottleneck to optimize Key/Value generation, effectively capturing sleep rhythms. Extensive experiments on SleepEDF-78 and SHHS datasets demonstrate strong and consistent performance, with Macro F1 improvements of 2.1–3.2% over the compared baselines. Visualizations confirm that SleepLT enhances inter-class discriminability between sleep stages, robustly detects salient waveforms, and effectively captures transitions through long-sequence modeling. These results indicate that SleepLT is effective for automatic sleep staging from single-channel EEG, particularly in improving the recognition of ambiguous transitional stages such as N1 and REM. Full article

Introduction: Caffeine is the most widely consumed psychoactive stimulant worldwide and acts primarily through antagonism of adenosine A1 and A2A receptors, thereby reducing sleep pressure and promoting wakefulness. Although its alerting and performance-enhancing effects are well established, its influence on sleep-related electroencephalography (EEG) has been investigated across diverse paradigms with substantial methodological heterogeneity. This systematic and mechanistic review aimed to synthesize human evidence on how caffeine affects sleep architecture, quantitative sleep EEG, and neurophysiological markers of sleep homeostasis, and to interpret these findings within current models of adenosine-mediated sleep–wake regulation. Materials and Methods: A systematic search of PubMed/MEDLINE, Web of Science, Scopus, Embase, PsycINFO, ResearchGate, and Google Scholar was conducted for studies published between January 1980 and January 2026, with the final search performed on 10 January 2026. Eligible studies were original human investigations examining caffeine exposure or administration and reporting sleep-related EEG outcomes, including polysomnographic sleep staging, spectral EEG analyses, or other EEG-derived sleep metrics. Two reviewers independently screened records and assessed eligibility, with disagreements resolved by consensus. Data on study design, participant characteristics, caffeine interventions, EEG methodology, and outcomes were extracted using a predefined form. Risk of bias was evaluated using the RoB 2 and ROBINS-I tools. Owing to marked heterogeneity across studies, findings were synthesized narratively within a mechanistic interpretive framework. Results: Thirty-two studies were included. Across highly heterogeneous paradigms—including acute bedtime or evening dosing, daytime or repeated caffeine use before nocturnal sleep, administration during prolonged wakefulness followed by recovery sleep, withdrawal protocols, and ambulatory/home EEG monitoring—the most consistent finding was suppression of low-frequency NREM EEG activity, particularly slow-wave activity and the lowest delta frequencies. Caffeine frequently increased faster EEG activity, including sigma/spindle and beta ranges, producing a lighter, more aroused, and more wake-like sleep EEG profile. These effects were especially prominent during early-night NREM sleep and in recovery sleep after sleep deprivation, where caffeine attenuated the expected homeostatic rebound in low-frequency power. REM-related effects were less consistent, but some studies reported delayed REM timing and subtler alterations in REM EEG. Emerging evidence further suggests that caffeine increases EEG complexity and shifts sleep dynamics toward a more excitation-dominant state. Several studies indicated that quantitative EEG measures were more sensitive than conventional sleep-stage variables in detecting caffeine-related sleep disruption. Dose, timing, habitual caffeine use, withdrawal state, age, circadian context, and adenosinergic genetic variation, particularly involving ADORA2A, moderated the magnitude of effects. We also highlighted the connection between current results and sports and sports science. Conclusions: Caffeine reliably alters the neurophysiological architecture of human sleep in a direction consistent with reduced sleep depth and weakened homeostatic recovery. The overall evidence supports a mechanistic model centered on adenosine receptor antagonism, attenuation of sleep-pressure build-up and expression, and a shift toward greater cortical arousal during sleep. Sleep EEG appears to be a sensitive marker of these effects, often revealing physiological disruption even when conventional sleep architecture changes are modest. Future research should prioritize larger and more diverse samples, pharmacokinetic and pharmacogenetic characterization, and ecologically valid high-resolution sleep monitoring to clarify the real-world and functional consequences of caffeine-induced EEG changes. Full article

Circadian rhythms maintain healthy neural function, and their disruption links to pathological brain states including epilepsy. Current diagnostic approaches for epilepsy, which predominantly focus on transient ictal events or static spectral features in intracranial EEG, suffer from a temporal myopia that neglects the rich spatiotemporal dynamics of long-term neural activity. To address this limitation, this study aims to establish multi-band circadian biomarkers as diagnostic signatures for epileptogenic tissue identification and patient subtyping. In this article, we developed a comprehensive biomarker extraction pipeline that analyzes long-term intracranial EEG recordings (72+ h) from 38 drug-resistant epilepsy patients, quantifying multi-band rhythm features from delta to gamma frequencies (1–100 Hz). The pipeline captures three circadian signatures: rhythm amplitude, temporal stability, and cross-frequency coupling. Epileptogenic tissue showed systematic circadian dysregulation: 43.2% reduction in delta band circadian amplitude (p < 0.001), 31.5% impairment in delta–gamma coupling (quantified as a power–envelope correlation proxy for phase–amplitude coupling), and progressive temporal instability across sleep–wake transitions. Using unsupervised clustering, we identified three chronobiological subtypes—Circadian-Preserved (36.8%), Coupling-Deficient (39.5%), and Pan-Dysrhythmic (23.7%)—each with distinct pathophysiological mechanisms and surgical outcomes. Our machine learning classification achieved clinically significant discrimination (AUC = 0.865), with circadian amplitude and coupling strength as the most informative features. These multi-band circadian biomarkers provide interpretable, physiologically grounded signatures for epilepsy diagnosis and subtype stratification, offering a temporal framework for personalized surgical planning and chronotherapy interventions. Full article

Background: Gaucher disease (GD) is a rare lysosomal storage disorder caused by mutations in the GBA1 gene. Traditionally, GD is classified into three subtypes based on the severity of neurological involvement; however, overlapping clinical features increasingly suggest a continuum of phenotypes rather than distinct categories. In this prospective observational cohort study, we conducted a multidisciplinary assessment of patients with GD to identify and monitor neurological, cognitive, auditory, and visual impairments. Materials and Methods: A comprehensive clinical and instrumental evaluation was performed at baseline and repeated at follow-up, with a median interval of 37 months (IQR 36–38). Neurological assessments included physical examination, clinical rating scales, video-EEG, and brain MRI. Cognitive status was assessed using a standardized battery of neuropsychological tests. Detailed audiological and ophthalmological evaluations were also conducted. Paired parametric or non-parametric tests were applied as appropriate, with Bonferroni correction for cognitive outcomes (p < 0.05). Results: Of the 22 patients assessed at baseline, 18 completed the follow-up evaluation. Neurological assessments showed a worsening of subtle parkinsonian signs, with significant increases in Movement Disorder Society–Unified Parkinson’s Disease Rating Scale Part III scores (p = 0.04) and non-motor symptom scores (p = 0.01). Two of the eighteen patients developed epilepsy during follow-up. A high prevalence of sleep disturbances was confirmed, with 27.8% exhibiting excessive daytime sleepiness and 16.7% reporting REM sleep behaviour disorder on standardized questionnaires. Compared with baseline, cognitive assessments revealed a higher proportion of patients with performance below normative population scores in at least one cognitive domain, particularly memory. Sensorineural hearing loss was confirmed in 11 of 15 patients (73.3%) who underwent audiological evaluation, with progressive worsening of audiometric thresholds observed in 7 of 11 (64%). Ophthalmological evaluations showed no changes in visual acuity or OCT findings; however, multifocal electroretinography abnormalities were detected in 12 of 13 patients. Conclusions: Through in-depth phenotyping, this study identifies measurable neurological, cognitive, and sensory progressive changes in patients with GD over time, supporting the value of tailored, multidisciplinary long-term care strategies to monitor and address emerging clinical needs in this rare disease. Full article

Sleep disturbances are early hallmarks of Alzheimer’s disease (AD) and other dementias, yet the molecular mechanisms remain poorly understood. We previously showed that dipeptidyl aminopeptidase-like protein 6-knockout (DPP6-KO) mice exhibit accelerated neurodegeneration with synaptic loss, neuronal death, and circadian dysfunction resembling AD pathology. Here, we investigate whether DPP6 deficiency directly causes sleep dysregulation and assess age-dependent effects using wireless EEG/EMG telemetry, behavioral monitoring, and body temperature recordings. We found striking age-dependent sleep phenotypes in DPP6-KO mice. Adult (3-month) DPP6-KO mice showed hyperactivity-driven REM sleep increases, while aged (12-month) DPP6-KO mice developed insomnia with fragmented sleep architecture. Critically, aged DPP6-KO mice exhibited decreased REM latency, a biomarker of depression, which we confirmed by behavioral assays. Conversely, DPP6 overexpression in aged wild-type mice increased NREM duration and reduced sleep fragmentation, demonstrating a protective effect. Throughout aging, DPP6-KO mice showed dysregulated locomotor activity and body temperature rhythms, suggesting broader disruption of circadian and metabolic homeostasis. These findings establish DPP6 as a critical regulator of sleep architecture whose loss recapitulates key sleep disturbances observed in AD/dementia. The progressive nature of sleep dysfunction in DPP6-KO mice, from REM abnormalities to insomnia, parallels human disease progression and positions DPP6 as a potential therapeutic target for sleep-related symptoms in neurodegenerative disorders. Full article

Background/Objectives: Acute total sleep deprivation (TSD) is known to impair working memory capacity. However, the specific relationship between alterations in the brain’s electrical power spectrum following TSD and working memory deficits remains poorly understood. Methods: In this study, 30 healthy young adults (14 males and 16 females) were enrolled, and 28 participants were finally included in the analysis after excluding EEG data with excessive noise, who underwent a verbal working memory task under two conditions: baseline sleep (BL) and 36 h of TSD. EEG data were recorded concurrently. Results: We observed a significant decrease in working memory accuracy and a significant prolongation of reaction time after TSD. Furthermore, TSD led to a significant enhancement of parietal alpha-ERD (at electrodes P3/Pz/P4) and theta-ERS, accompanied by a reduction in N2 and P3 wave amplitudes. Conclusions: These findings suggest that TSD may impair working memory by weakening parietal alpha-ERD and early conflict monitoring and late attention evaluation processes. The enhanced theta-ERS might represent a compensatory mechanism. Full article

Sleep disorders are prevalent in the world, and they lead to severe health issues such as cardiovascular disease and cognitive disabilities. Conventional polysomnography-based diagnosis is based on manual EEG analysis under the supervision of trained specialists, which is time-consuming and may have inter-rater variability. Although the predictions of deep learning (DL) models on the task of sleep classification of EEG have been promising, they, in many cases, do not explain the multiscale, temporal dynamics that physiological signals are characterized by. In this work, a hybrid model that is a combination of CNN and multifractal detrended fluctuation analysis (MF-DFA) was proposed to detect localized temporal features and long-term fractal-based dynamics of single-channel EEG recordings. The performance of the suggested model was tested using two separate polysomnographic datasets: the CAP Sleep Dataset of five-class sleep disorder classification (Healthy, Insomnia, Narcolepsy, PLM, and RBD) and the ISRUC Sleep Dataset on the three-class subject-independent validation. In the CAP dataset, the framework had an accuracy of 86.38%. Cross-dataset transfer to the ISRUC Sleep Dataset, where only the classification head was fine-tuned on a small labeled subset while all feature-extraction layers remained frozen from CAP training, achieved 87.50% accuracy, demonstrating that the learned representations generalize across differing recording protocols, sampling rates, and diagnostic label spaces. The experiments of ablation proved the paramount importance of the MF-DFA features, and the lack of them led to low classification rates. The findings demonstrate the clinical feasibility of applying fractal analysis in conjunction with DL to detect sleep disorders in an automated, generalizable manner, suitable for use in large-scale monitoring and resource-starved clinical environments. Full article

Background: Chronic insomnia disorder (CID) frequently coexists with diarrhea-predominant irritable bowel syndrome (IBS-D), a comorbidity characterized by gut–brain axis dysfunction and persistent inflammatory activation. However, the molecular mechanisms underlying this overlap remain incompletely understood, and effective multitarget interventions are lacking. Objectives: This study aimed to identify quercetin as a potential bioactive compound for IBS-D-associated insomnia and to investigate whether its protective effects are associated with modulation of the PI3K/AKT/NF-κB signaling pathway. Methods: CID- and IBS-D-related targets were collected from public databases. Candidate compounds were screened using bioinformatics and network pharmacology analyses, followed by molecular docking. Experimental validation was conducted in 36 male C57BL/6J mice assigned to control, CID+IBS-D model, quercetin-treated, and quercetin-plus-Recilisib-treated groups. Sleep-related behavior, EEG/EMG-derived sleep architecture, intestinal function, inflammatory markers, and pathway-related proteins were assessed. Results: Quercetin was identified as a core candidate compound. Network pharmacology revealed 43 shared targets among CID, IBS-D, and quercetin, with significant enrichment in PI3K/AKT-related signaling. In vivo, quercetin improved sleep-associated phenotypes and intestinal dysfunction; reduced visceral hypersensitivity; restored ZO-1 and Occludin expression; suppressed hypothalamic and colonic inflammatory responses; and was accompanied by reduced phosphorylation of PI3K, AKT, IκB, and NF-κB p65 in the hypothalamus. Quercetin also increased hypothalamic 5-HT1A and GABA_A Rα5 expression. These effects were partially reversed by Recilisib, supporting the involvement of PI3K/AKT-associated signaling in quercetin-mediated protection. Conclusions: Quercetin alleviated key sleep-related and IBS-D-like phenotypes in a composite murine model of gut–sleep comorbidity. The protective effects were associated with reduced inflammatory activation and modulation of PI3K/AKT/NF-κB-related signaling. These findings support quercetin as a promising candidate for gut–brain axis-related comorbid disorders, while further studies are needed to define pathway specificity, tissue exposure, and translational applicability. Full article

Frontal alpha asymmetry (FAA), a pattern of brain activity that reflects the difference in alpha wave power between the left and right frontal areas of the brain, is considered a stable marker for an individual’s tendency to experience either more approach-related or withdrawal-related emotions. On the other hand, electrodermal activity (EDA) measures arousal by tracking changes in skin sweat, which are controlled by the sympathetic nervous system. This study explores the interrelation between EDA features, obtained from time and frequency domains, with FAA by means of the mutual information. Multiple cognitive tasks such as EAT, ship search, PVT and N-Back were analyzed in 10 participants in intervals of two hours over 24 h (12 trials), in which they had to face sleep deprivation conditions. The most informative EDA features about FAA, were used to identify the two main clusters associated to high and low FAA values through the hierarchical agglomerative clustering approach. Once data is labeled, a supervised classifier based on support vector machines (SVMs) is used to identify positive and negative emotional states by using a rigorous one-trial out cross-validation scheme. Results show consistent performance within tasks and trials, achieving accuracy values over 80% on average, giving an important insight about the use of EDA signal as an alternative to the more complex FAA measurement for tracking positive or negative emotional states. Full article

Objective: This study examined whether changes in electroencephalography (EEG)-derived indices, photoplethysmography (PPG)-derived autonomic nervous system indices, heart rate, and rating of perceived exertion (RPE) post-high-intensity exercise differ depending on sleep duration. Methods: Forty physically healthy female university students in their twenties were randomly assigned to the sleep restriction (SR) or normal sleep (NS) group. EEG-derived indices—the theta-to-beta ratio (TBR) and spectral edge frequency at 90% (SEF-90)—and PPG-derived autonomic nervous system indices (HRV index, sympathetic activity, and parasympathetic activity) were measured for one minute at rest before exercise and for one minute immediately after exercise. Heart rate was assessed at rest, immediately after exercise, and at 5, 10, and 15 min post-exercise. The group × time interaction effects were assessed using two-way mixed-design analysis of variance, followed by post hoc analyses. Results: TBR increased significantly post-exercise in the SR group (p = 0.002) with no significant change in the NS group. SEF-90 decreased significantly in the SR group (p < 0.001) with no significant change in the NS group. The HRV index decreased significantly in the SR group (p = 0.004) with no significant change in the NS group. Sympathetic activity increased and parasympathetic activity decreased significantly in the SR group (both p < 0.001). Heart rate was significantly higher in the SR group at rest (p < 0.001), immediately after exercise (p = 0.020), and 5 min post-exercise (p = 0.009). RPE was significantly higher in the SR group (p = 0.003). Conclusions: In healthy young adult women, the central and autonomic nervous systems respond differently to high-intensity exercise depending on sleep duration. Full article

Long-term home-based sleep monitoring requires wearable sensing devices that strictly balance signal precision with power constraints. This study presents the design and implementation of a low-noise, low-power wearable single-channel electroencephalography (EEG) system for automatic sleep staging. The hardware architecture integrates a TI ADS1298 analog front-end with an STM32F4 microcontroller, utilizing differential sampling and hardware-based filtering to effectively suppress power-line interference and baseline drift. System-level testing demonstrates an average power consumption of approximately 150.85 mW, enabling over 24.6 h of continuous operation on a 1000 mAh battery, which meets the requirements for overnight monitoring. To achieve accurate staging without draining the wearable’s battery, we adopted and deployed a lightweight deep learning model, SleePyCo, on the cloud backend. This architecture was specifically optimized for our edge–cloud collaborative execution, which combines contrastive representation learning with temporal dependency modeling. Validation on the ISRUC dataset yielded an overall accuracy of 79.3% ± 3.0%, with a notable F1-score of 88.3% for Deep Sleep (N3). Furthermore, practical field trials involving 10 healthy subjects verified the system’s engineering stability, achieving a valid data rate exceeding 97% and a Bluetooth packet loss rate of only 0.8%. These results confirm that the proposed hardware–software co-designed system provides a robust, energy-efficient IoMT sensing solution for daily sleep health management. Full article

Background: Celiac disease (CD) is a multisystem immune-mediated disorder increasingly recognized to affect sleep and neurobehavioral functioning. Pediatric data remain limited, and no prior study has examined especially for sleep microstructure in this population. This study evaluates the prevalence and patterns of sleep disturbances in children with CD using the Sleep Disturbance Scale for Children (SDSC) and explores potential electrophysiological correlates through N2 sleep spindle analysis. Methods: Children with biopsy-confirmed CD (n = 31) and age-matched controls (n = 25) completed the SDSC. A subgroup of CD patients with SDSC ≥ 35 and healthy controls underwent quantitative sleep spindle analysis (C3, C4, O1, O2) using automated and visual verification methods combined. Results: Clinically significant sleep disturbances were substantially more prevalent in CD than in controls (77.4% vs. 12%). Excessive somnolence, sleep–wake transition disorders, and sleep hyperhidrosis were the most affected domains. Moreover, among children with CD, those noncompliant with a gluten-free diet exhibited higher rates of excessive somnolence and sleep–wake transition disorders. While spindle parameters did not differ between groups, higher SDSC scores (≥35)—particularly in the somnolence and sleep–wake transition disorder domains—are associated with reduced spindle amplitude and density, suggesting that spindle alterations are linked to sleep disturbance severity rather than disease status per se. Conclusions: Sleep disturbances are common in pediatric CD and worsen with poor dietary adherence. Although sleep microarchitecture is largely preserved, reduced spindle activity is evident in children with higher subjective sleep burden, suggesting that spindle metrics may serve as potential objective markers for sleep disturbance. Longitudinal studies are required for validation. Full article