Sleep Stages Research Articles

A New Perspective in Epileptic Seizure Classification: Applying the Taxonomy of Seizure Dynamotypes to Noninvasive EEG and Examining Dynamical Changes across Sleep Stages.

Epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, significantly impacts patient quality of life. Current classification methods focus primarily on clinical observations and electroencephalography (EEG) analysis, often overlooking the underlying dynamics driving seizures. This study uses surface EEG data to identify seizure transitions using a dynamical systems-based framework-the taxonomy of seizure dynamotypes-previously examined only in invasive data. We applied principal component and independent component (IC) analysis to surface EEG recordings from 1,177 seizures in 158 patients with focal epilepsy, decomposing the signals into ICs. The ICs were visually labeled for clear seizure transitions and bifurcation morphologies (BifMs), which were then examined using Bayesian multilevel modeling in the context of clinical factors. Our analysis reveals that certain onset bifurcations (saddle node on invariant circle and supercritical Hopf) are more prevalent during wakefulness compared with their reduced rate during nonrapid eye movement (NREM) sleep, particularly NREM3. We discuss the possible implications of our results in the context of modeling approaches and suggest additional avenues to continue this exploration. Furthermore, we demonstrate the feasibility of automating this classification process using machine learning, achieving high performance in identifying seizure-related ICs and classifying interspike interval changes. Our findings suggest that the noise in surface EEG may obscure certain BifMs, and we suggest technical improvements that could enhance detection accuracy. Expanding the dataset and incorporating long-term biological rhythms, such as circadian and multiday cycles, may provide a more comprehensive understanding of seizure dynamics and improve clinical decision-making.

Evaluation of the sedative-hypnotic effects of Menyanthes trifoliata L. extract in mice.

Insomnia is a pervasive and prominent problem worldwide, afflicting approximately one-third of the population and profoundly affecting patients' quality of life. Efficient and safe sedative-hypnotic medications are required. Menyanthes trifoliata L. (Mt), a sleeping herb in China, is used as a hypnotic remedy in ethnomedicines; however, there are few studies on this herb. We systematically evaluated the potential of Mt as a sedative-hypnotic candidate. The chemical constituents of the Mt extract were analyzed by lLiquid chromatography with photodiode array detection and mass spectrometry (LC-PDA-MS). The sedative-hypnotic effects of Mt extract (0.5, 2, and 4g/kg) were investigated using the pentobarbital-induced sleep test (PIST), the caffeine-induced insomnia model (CIIM), and the open field test (OFT). Furthermore, the effect of Mt on sleep architecture was ‌investigated using electroencephalography/electromyography (EEG/EMG). The safety of the Mt extract was evaluated using the maximum tolerated dose method. Fifteen phenolic compounds were identified based on their UV absorption and MS fragmentation using LC-PDA-MS analysis. In the CIIM, PIST, and OFT, Mt extract exhibited a dose-dependent reduction in sleep latency, an extension of total sleep duration, and a decrease in locomotor activity. Moreover, it increased the duration of non-rapid eye movement (NREM) sleep and reduced wakefulness after one day's administration, according to EEG/EMG. Additionally, no signs of toxicity were observed at a dose of 30g/kg (equivalent to 316.46g/kg of crude drugs). This study supports the potential medicinal use of Mt extract for sleep promotion.

Norepinephrine-mediated slow vasomotion drives glymphatic clearance during sleep.

As the brain transitions from wakefulness to sleep, processing of external information diminishes while restorative processes, such as glymphatic removal of waste products, are activated. Yet, it is not known what drives brain clearance during sleep. We here employed an array of technologies and identified tightly synchronized oscillations in norepinephrine, cerebral blood volume, and cerebrospinal fluid (CSF) as the strongest predictors of glymphatic clearance during NREM sleep. Optogenetic stimulation of the locus coeruleus induced anti-correlated changes in vasomotion and CSF signal. Furthermore, stimulation of arterial oscillations enhanced CSF inflow, demonstrating that vasomotion acts as a pump driving CSF into the brain. On the contrary, the sleep aid zolpidem suppressed norepinephrine oscillations and glymphatic flow, highlighting the critical role of norepinephrine-driven vascular dynamics in brain clearance. Thus, the micro-architectural organization of NREM sleep, driven by norepinephrine fluctuations and vascular dynamics, is a key determinant for glymphatic clearance.

Spontaneous oxycodone withdrawal disrupts sleep, diurnal, and electrophysiological dynamics in rats.

Opioid dependence is defined by an aversive withdrawal syndrome upon drug cessation that can motivate continued drug-taking, development of opioid use disorder, and precipitate relapse. An understudied but common opioid withdrawal symptom is disrupted sleep, reported as both insomnia and daytime sleepiness. Despite the prevalence and severity of sleep disturbances during opioid withdrawal, there is a gap in our understanding of their interactions. The goal of this study was to establish an in-depth, temporal signature of spontaneous oxycodone withdrawal effects on the diurnal composition of discrete sleep stages and the dynamic spectral properties of the electroencephalogram (EEG) signal in male rats. We continuously recorded EEG and electromyography (EMG) signals for 8 d of spontaneous withdrawal after a 14-d escalating-dose oxycodone regimen (0.5-8.0 mg/kg, 2×d; SC). During withdrawal, there was a profound loss (peaking on days 2-3) and gradual return of diurnal structure in sleep, body temperature, and locomotor activity, as well as decreased sleep and wake bout durations dependent on lights on/off. Withdrawal was associated with significant alterations in the slope of the aperiodic 1/f component of the EEG power spectrum, an established biomarker of arousal level. Early in withdrawal, NREM exhibited an acute flattening and return to baseline of both low (1-4 Hz) and high (15-50 Hz) frequency components of the 1/f spectrum. These findings suggest temporally dependent withdrawal effects on sleep, reflecting the complex way in which the allostatic forces of opioid withdrawal impinge upon sleep and diurnal processes. These foundational data based on continuous tracking of vigilance state, sleep stage composition, and spectral EEG properties provide a detailed construct with which to form and test hypotheses on the mechanisms of opioid-sleep interactions.

Predicting All-Cause Mortality in Patients With Obstructive Sleep Apnea Using Sleep-Related Features: A Machine-Learning Approach.

Obstructive sleep apnea (OSA) is associated with an increased risk of adverse outcomes, including mortality. Machine-learning algorithms have shown potential in predicting clinical outcomes in patients with OSA. This study aimed to develop and evaluate a machine-learning algorithm for predicting 10- and 15-year all-cause mortality in patients with OSA. Patients with OSA were stratified into deceased and alive groups based on mortality outcomes. Various sleep-related features were analyzed, including objective sleep measures and the heart-rate variability during various sleep stages. The light gradient-boosting machine (LGBM) algorithm was employed to construct a risk-stratification model. The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC) for predicting mortality over 10 and 15 years. Survival analysis was conducted using Kaplan-Meier plots and Cox proportional-hazards model. This study found that parasympathetic activity was higher in OSA patients with worse outcomes than in those with better outcomes. The LGBM-based prediction model with sleep-related features was moderately accurate, with a mean AUC of 0.806 for predicting 10- and 15-year mortality. Furthermore, survival analysis demonstrated that LGBM could significantly distinguish the high- and low-risk groups, as evidenced by Kaplan-Meier plots and Cox regression results. This study has confirmed the potential of sleep-related feature analysis and the LGBM algorithm for evaluating the mortality risk in OSA patients. The developed risk-stratification model offers an efficient and interpretable tool for clinicians that emphasizes the significance of patient-specific autonomic responses in mortality prediction. Incorporating survival analysis further validated the robustness of the model in predicting long-term outcomes.

Sleep and breathing in children with Joubert syndrome and a review of other rare congenital hindbrain malformations

Background: Joubert syndrome (JS) is an autosomal recessive disorder with a distinctive mid-hindbrain malformation known as the “molar tooth sign” which involves the breathing control center and its connections with other structures. Literature has reported significant respiratory abnormalities which included hyperpnea interspersed with apneic episodes during wakefulness. Larger-scale studies looking at polysomnographic findings or subjective reports of sleep problems in this population have not yet been published. Objectives: The primary objectives were (1) compare a large group of children with JS and their unaffected siblings for caregiver-reported sleep difficulties. Secondary objectives were (1) present new polysomnography (PSG) data on our JS cohort; (2) review sleep disordered breathing (SDB) in other rare congenital hindbrain anatomic abnormalities. Design: We conducted a cross-sectional study on a cohort of 109 families affected by JS. Methods: Pediatric Sleep Questionnaire (PSQ) and the Children’s Sleep Habits Questionnaire (CSHQ) along with general medical health information focused on respiratory and sleep problems were mailed to all patients and families. Caregivers were asked to complete the survey for both children with JS and unaffected siblings, if any. Baseline diagnostic PSG was retrospectively reviewed for those with available studies, and the sleep parameters were compared to a referent cohort. Results: Study participants with JS were older than their unaffected siblings ( p = 0.02). Genetic mutations were available for 41 out of 118 individuals, with the most common mutation being MKS3 (31.4%). Patients with JS had higher scores in the PSQ compared to their unaffected siblings ( p < 0.001). PSG data showed severe SDB with apnea-hypopnea index (AHI) of 23 ± 15 events/h in patients with JS. Events were primarily obstructive (obstructive AHI 18 ± 15 events/h vs central AHI 4 ± 4 events/h). Abnormal sleep architecture with increased arousal indices, decreased efficiency, and more time awake and in light sleep or wakefulness when compared to the referent data. Conclusion: SDB is common and severe in patients with JS, and the significantly greater obstructive component reported in this cohort makes it necessary to perform complete PSG studies to address or prevent clinical manifestations in this at-risk population. PSQ could represent a viable method to screen for SDB in JS.

MIASS: A multi-interactive attention model for sleep staging via EEG and EOG signals

Sleep staging is essential for sleep analysis. Recent studies have attempted to integrate multi-modal signals such as electroencephalogram (EEG) and electrooculogram (EOG) to enhance model sensitivity. However, these attempts still face limitations in effectively fusing multi-modal signals, particularly in capturing both global and fine-grained interaction information in sleep epochs simultaneously. To address this, we propose a multi-interactive model (MIASS) that integrates two core modules, the global information interaction (GII) module and the fine-grained information interaction (FII) module. The GII module can effectively capture the global correlation paradigm in EEG and EOG at the epoch level by combining the global channel and spatial attentions with a residual network. The FII module explores the fine-grained correlation paradigm between small EEG and EOG segments within epochs using the cross-attention mechanism to achieve more fine-grained interaction information. The combination of these modules increased the accuracy of the model up to 89.2%, 86.6% and 89.7% on the SleepEDF-20, SleepEDF-78 and SHHS datasets, respectively, which outperforms the comparison models by 0.2–5.7%. The ablation study confirmed the benefits of integrating global and fine-grained correlation paradigms to enhance sleep staging performance, and the model input study demonstrated that MIASS maintains good performance under various input conditions.

Automatic Sleep Staging Method Using EEG Based on STFT and Residual Network

Automatic Sleep Staging Method Using EEG Based on STFT and Residual Network

Sleep Stage Classification from EEG Signals: Advances in Machine Learning Techniques

Sleep Stage Classification from EEG Signals: Advances in Machine Learning Techniques

Efficient Multi-View Fusion and Flexible Adaptation to View Missing in Cardiovascular System Signals

The progression of deep learning and the widespread adoption of sensors have facilitated automatic multi-view fusion (MVF) about the cardiovascular system (CVS) signals. However, prevalent MVF model architecture often amalgamates CVS signals from the same temporal step but different views into a unified representation, disregarding the asynchronous nature of cardiovascular events and the inherent heterogeneity across views, leading to catastrophic view confusion. Efficient training strategies specifically tailored for MVF models to attain comprehensive representations need simultaneous consideration. Crucially, real-world data frequently arrives with incomplete views, an aspect rarely noticed by researchers. Thus, the View-Centric Transformer (VCT) and Multitask Masked Autoencoder (M2AE) are specifically designed to emphasize the centrality of each view and harness unlabeled data to achieve superior fused representations. Additionally, we systematically define the missing-view problem for the first time and introduce prompt techniques to aid pretrained MVF models in flexibly adapting to various missing-view scenarios. Rigorous experiments involving atrial fibrillation detection, blood pressure estimation, and sleep staging—typical health monitoring tasks—demonstrate the remarkable advantage of our method in MVF compared to prevailing methodologies. Notably, the prompt technique requires finetuning less than 3% of the entire model's data, substantially fortifying the model's resilience to view missing while circumventing the need for complete retraining. The results demonstrate the effectiveness of our approaches, highlighting their potential for practical applications in cardiovascular health monitoring. Codes and models are released at URL.

FlexibleSleepNet:A Model for Automatic Sleep Stage Classification Based on Multi-Channel Polysomnography

FlexibleSleepNet:A Model for Automatic Sleep Stage Classification Based on Multi-Channel Polysomnography

아로마 흡입법이 학업스트레스, 대학생활 스트레스에 미치는 효과

In this study, the purpose of this study was to investigate the effects on academic and university life stress by conducting aroma-free groups (I) and aroma-free groups (II) in 20s college students in North Gyeongsang Province, which blended Eucalyptus, Lavender, and Sweet Orange aroma essential oils at a ratio of 2:6:3. After the blended oil was inhaled through the nose, brain waves and heart rate were measured. Before and after brain waves and heart rate measurements and 42 items of stress survey were investigated. There was no statistically significant change in α-wave, β-wave, and heart rate in groups I and II, but there was a slight decrease in β-wave and heart rate(Group I), and α-wave slightly increased and heart rate decreased (Group II). In the survey on stress, group II had lower stress scores than I in study(A), emotion(D), and deep sleep(G). As the interest in studying is high, it is stressful, so it is believed that if you make the use of natural aroma a habit, you will enjoy a healthy college life.

Scalp high-frequency oscillation spatial distribution is consistent over consecutive nights, while rates vary with antiseizure medication changes.

This study aimed to investigate two key aspects of scalp high-frequency oscillations (HFOs) in pediatric focal lesional epilepsy: (1) the stability of scalp HFO spatial distribution across consecutive nights, and (2) the variation in scalp HFO rates in response to changes in antiseizure medication (ASM). We analyzed 81 whole-night scalp electroencephalography (EEG) recordings from 20 children with focal lesional epilepsy. We used a previously validated automated HFO detector to assess scalp HFO rates (80-250 Hz) during non-rapid eye movement (NREM) sleep. The spatial distribution of HFO rates across consecutive nights was evaluated using Hamming similarity, and changes in ASM were classified as increased, decreased, or stable. For each patient, we analyzed 3 ± 1 whole-night scalp EEG recordings, with a mean duration of 650 ± 215 min per recording. The distribution of HFO remained stable across consecutive nights, with a Hamming similarity of 88% ± 6%. Four patients had at least one ASM dosage decrease, nine patients had both ASM dosage decreases and increases, two patients had only ASM dosage increases, and five patients had no changes in ASM during the study period. A decrease in ASM dosage was associated with increased HFO rates (from .16 ± .32 to .22 ± .36 HFO/min; p = .03), whereas an increase in ASM dosage led to decreased HFO rates (from .32 ± .54 HFO/min to .22 ± .38 HFO/min; p = .005) when comparing the last night to the first. The spatial distribution of scalp HFOs remained consistent across multiple nights, whereas fluctuations in HFO rates correlated with changes in ASM dosage. These findings suggest that scalp HFOs may not only help identify epileptogenic brain tissue but also monitor treatment response.

Sleep: A complex biological process

Sleep is one of the most basic physiological process essential to physical recovery, cognitive functioning and emotional wellbeing. Throughout this paper, sleep mechanisms and their biological significance e.g., energy restoration, tissue recovery, and neural detoxification are addressed. It explores the role of circadian rhythms, the firing of neurons and the hormone signals of the brain that help maintain the sleep-wake cycle and how disruption in them can throw the sleep-wake cycle off balance, which can result in sleep disorders and other issues. One of the basic methods of sleep research is the Electroencephalogram (EEG) method in which the electrical activity of the brain is recorded at various sleep stages. The paper reviews advancements in improving EEG-based methods for classifying sleep stages and detecting conditions such as epilepsy, Alzheimer’s disease and rapid-eye movement (REM) abnormalities. Recent developments in machine learning and signal processing tools such as time-frequency analysis and neural networks improved the accuracy of diagnosis and prediction of sleep disorders. However, the current contribution is an extensive review of experimental literature and demonstrate thopacuity of combining EEG features with modern computational techniques to provide a better grasp of the microstructure of sleep states and disorders. They facilitate superior automated systems for diagnosis that enhance patient outcomes, and translate to broader applications in health care.

Age‐related changes in large muscle group movements during sleep

SummaryThis study aims to investigate large muscle group movements (LMM) during sleep across the lifespan, from school‐aged children to older adults, and to examine their relationship with sleep architecture (REM and NREM). A total of 141 healthy participants were included, divided into five age groups: school‐aged children (n = 43), adolescents (n = 31), young adults (n = 27), adults (n = 16), and older adults (n = 24). Polysomnography (PSG) was used to record sleep data and LMM, along with LMM associated with arousals (LMMA) and awakenings (LMMW), were scored according to recently established criteria. The LMM indices and durations were analysed for each group, and statistical comparisons were made between age groups and across REM and NREM sleep. LMM indices significantly increased with age, with older adults exhibiting the highest frequency of LMM, LMMA, and LMMW. The LMM duration was longer in younger participants and shorter in older adults. Significant differences in LMM patterns between REM and NREM sleep were observed, particularly in adults and older adults, where LMM and LMMA were more prevalent in REM sleep. No significant sex differences were found across age groups. LMM during sleep showed clear age‐related trends, increasing in frequency and decreasing in duration with advancing age. These movements are more prevalent during REM sleep in older adults, reflecting age‐related sleep fragmentation and instability. The findings provide important normative data for LMM across the lifespan and demonstrate that sleep‐related motor activity is closely linked to changes in sleep architecture.

Heritability of sleep architecture based on home polysomnography

SummaryWe aimed to establish the heritability of polysomnography measures through the analysis of home polysomnography recordings from 648 participants in the Baependi Heart Study, a rural, family‐based, genetically admixed cohort based in the southeast of Brazil. Sleep polysomnography staging variables were computed, and narrow‐sense heritability values were derived. The heritability (h2) of polysomnography total sleep time was 0.18 ± 0.08 (p = 0.007). Including age and sex did not change the heritability estimates of the model. Wake after sleep onset did not show significant heritability (h2 = 0.02 ± 0.07, p = 0.39). The unadjusted model for N1 resulted in a heritability estimate of 0.22 ± 0.10 (p < 0.003), and of 0.26 ± 0.10 (p = 0.003) in the adjusted model. Time spent in N2 had an unadjusted heritability of 0.18 ± 0.09 (p = 0.01) and adjusted h2 of 0.22 ± 0.10 (p = 0.007). The heritability of the total time spent in N3 was 0.35 ± 0.09 (p < 0.001) in the unadjusted and 0.38 ± 0.09 (p < 0.001) when sex, age and age*age were considered in the model. By contrast, no variance in the total time spent in rapid eye movement could be significantly attributed to genetic variance. In terms of the heritability of the apnea–hypopnea index, 17% of its variance could be attributed to genetic factors (0.17 ± 0.08, p = 0.02). This is the first report of heritability of electroencephalographic‐derived sleep parameters from a larger population sample, and the first one performed in a population with a majority above 25 years of age. Our findings indicate the potential feasibility of future genome‐wide association studies of non‐rapid eye movement sleep stages in pooled population samples.

Dexmedetomidine accelerates photoentrainment and affects sleep structure through the activation of SCNVIP neurons

Dexmedetomidine (DexM), a highly selective α2-adrenoceptor agonist, significantly reduces postoperative adverse effects, including sleep and circadian rhythm disruptions. Vasoactive intestinal peptide neurons in the suprachiasmatic nucleus (SCNVIP) regulate the synchronization of circadian rhythms with the external environment in mammals. We investigate the effects of DexM on sleep and circadian rhythms, as well as the underlying mechanisms. Using electrophysiological and chemogenetic methods, along with locomotor activity and electroencephalogram/electromyogram recordings, we found that DexM accelerates the rate of re-entrainment following an 8-hour phase advance in the 12-hour light:12-hour dark cycle, increases the amount of non-rapid eye movement sleep, and decreases the mean duration of rapid eye movement sleep. Chemogenetic inhibition of SCNVIP neurons hinders the acceleration of re-entrainment and the changes in the sleep-wakefulness cycle induced by DexM. Electrophysiological results show that DexM increases the firing rate and the frequency of spontaneous glutamatergic postsynaptic currents while decreasing the frequency of spontaneous GABAergic PSCs in SCNVIP neurons through the α2-adrenergic receptor. Additionally, DexM reduces the frequency of miniature GABAergic PSCs in SCNVIP neurons. In conclusion, these findings suggest that DexM promotes sleep and maintains the coordination of circadian rhythms with the external environment by activating SCNVIP neurons through the α2-adrenoceptor.

Short term GABA antagonist treatment improves long term sleep quality, memory, and decision-making in a Down syndrome mouse model

Abstract Down syndrome (DS) is a common genetic condition affecting people worldwide. It involves cognitive disabilities for which there are no drug therapies. The Ts65Dn mouse model of DS shows cognitive impairment due to a reduction in neuron number and connectivity as well as excessive neuronal activity, as GABA antagonist treatment restores memory in these mice. Our study showed the effects of GABA antagonist treatment on sleep and decision-making in Ts65Dn mice. We administered a daily, low oral dose of pentylenetetrazol (PTZ) in milk to Ts65Dn mice for 17 days. Decision-making was tested with and without PTZ treatment. Short and long-term memories were tested before, immediately after, and one month following PTZ treatment. Electro-encephalography (EEG) was also recorded at these three time points to study the effect of the treatment on sleep. We showed that PTZ treatment improved long-term recognition, but not short-term, memory and led to more Ts65Dn mice showing safer decision-making behavior. PTZ treatment showed a moderate and only global beneficial effect on sleep by decreasing the global amount of wake and increasing NREM sleep in the Ts65Dn mice, which may explain the observed cognitive improvements. These results bring new knowledge on the role of GABA in sleep, memory consolidation, and decision-making abilities in DS.

Oral administration of ethinyl estradiol and the brain-selective estrogen prodrug DHED in a female common marmoset model of menopause: Effects on cognition, thermoregulation, and sleep.

Menopausal symptoms of sleep disturbances, cognitive deficits, and hot flashes are understudied, in part due to the lack of animal models in which they co-occur. Common marmosets (Callithrix jacchus) are valuable nonhuman primates for studying these symptoms, and we examined changes in cognition (reversal learning), sleep (48h/wk of sleep recorded by telemetry), and thermoregulation (nose temperature in response to mild external warming) in middle-aged, surgically-induced menopausal marmosets studied at baseline, during 3-week phases of ethinyl estradiol (EE2, 4μg/kg/day, p.o.) treatment and after EE2 withdrawal. We also assessed a brain-selective hormonal therapy devoid of estrogenic effects in peripheral tissues on the same measures (cognition, sleep, thermoregulation) after treatment with the estrogen prodrug 10β,17β-dihydroxyestra-1,4-dien-3-one (DHED, 100μg/kg/day, p.o) and DHED withdrawal. Reversal learning performance was improved with EE2 or DHED treatment relative to phases without hormone administration, as indicated by a faster reversal of the stimulus/reward contingencies. Both EE2 and DHED increased non-REM sleep and reduced nighttime awakenings relative to baseline, but to the detriment of REM sleep which was highest at baseline. Nasal temperature in response to mild external warming was highest, and overnight core body temperature lowest, in the DHED treatment phase compared to both the EE2 and baseline phases. These results suggest that low dose estradiol, delivered either peripherally or centrally via DHED, benefits selective aspects of cognition and sleep in a marmoset menopause model. DHED appears a promising therapeutic candidate for alleviating the cognitive and sleep disruptions associated with estrogen deficiency in primates.

Effect of occlusal appliances on the sleep of individuals with bruxism: A systematic review and meta-analyses

ABSTRACT Objective This systematic review determined whether occlusal appliance use influences the sleep of individuals with sleep bruxism. Material and Methods Six databases and the gray literature were searched to identify randomized and non-randomized clinical trials comparing slow wave sleep and sleep quality of sleep bruxers before and after occlusal appliance use. The Risk of Bias was assessed using Cochrane tools and Meta-analyses were performed to compare data. The Certainty of the evidence was evaluated by Grading of Recommendations Assessment, Development, and Evaluation approach. Results Two randomized and eight non-randomized studies were included. Meta-analyses indicated that soft and hard appliances influenced the slow-wave sleep and sleep quality of sleep bruxers, as determined by polysomnography (Z = 5.58; p < .001), the Pittsburgh Sleep Quality Index (Z = 3.45; p < .001), and the Sleep Assessment Questionnaire (Z = 48.1; p < .001). Conclusion Occlusal appliance use significantly impacts slow-wave sleep and sleep quality in sleep bruxism individuals, regardless of appliance design or material.