Submitted:
04 August 2026
Posted:
06 August 2026
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Abstract
Background: Sleep and epilepsy are linked through a complex, bidirectional relationship in which physiological sleep processes influence cortical excitability and seizure susceptibility, while epileptic activity and its treatments profoundly affect sleep architecture and quality. Sleep disturbances (including insomnia, obstructive sleep apnea, circadian rhythm disorders, excessive daytime sleepiness, and fragmented sleep) are highly prevalent among people with epilepsy and contribute to impaired seizure control, cognitive dysfunction, psychiatric comorbidities, reduced quality of life, and increased healthcare utilization. Although substantial research has examined individual aspects of this relationship, the evidence remains fragmented across multiple disciplines, limiting its translation into comprehensive clinical practice.Objective: This scoping review aimed to systematically map the contemporary evidence regarding the role of sleep across the epilepsy care continuum, encompassing pathophysiological mechanisms, clinical assessment, diagnostic strategies, therapeutic interventions, digital health technologies, and emerging research priorities, while identifying critical knowledge gaps to guide future investigation and precision medicine approaches. Methods: This scoping review was conducted according to the methodological framework proposed by Arksey and O'Malley, refined by Levac and colleagues, and further finalized by the Joanna Briggs Institute, and was reported in accordance with the PRISMA Extension for Scoping Reviews (PRISMA-ScR). Comprehensive searches of PubMed/MEDLINE, Embase, Scopus, Web of Science, and the Cochrane Library identified English-language studies published from January 2020 onward. Eligible publications included adults and children with epilepsy or seizure disorders addressing at least one sleep-related domain. Evidence was descriptively synthesized across predefined domains, including sleep physiology and epileptogenesis; sleep disorders and epilepsy; effects of antiseizure medications on sleep; sleep deprivation and seizure risk; diagnostic modalities, including polysomnography, electroencephalography, actigraphy, and wearable technologies; digital health and artificial intelligence; therapeutic interventions; perioperative and long-term follow-up; implementation challenges; and health equity. Conclusions: By providing the first comprehensive mapping of sleep throughout the epilepsy care continuum, this review establishes an integrated conceptual framework linking mechanistic, diagnostic, therapeutic, and technological advances. The findings highlight priorities for multidisciplinary research, support the implementation of precision sleep medicine in epilepsy, and inform future evidence-based clinical strategies designed to optimize seizure control, neurological outcomes, and patient-centered care.
Keywords:
epilepsy
; sleep
; sleep wake disorders
; epileptic seizures
; polysomnography
; electroencephalography
; circadian rhythm
; sleep deprivation
; wearable electronic devices
; artificial intelligence
Introduction
Sleep and epilepsy are interconnected through a complex and dynamic bidirectional relationship that extends far beyond the conventional observation that sleep deprivation may precipitate seizures [1]. Physiological sleep modifies neuronal excitability, synchronization, cortical connectivity, autonomic regulation, and the balance between inhibitory and excitatory networks, thereby influencing both the expression of interictal epileptiform discharges and the probability, timing, and phenotype of seizures. Conversely, epileptic activity may disrupt sleep continuity, architecture, and restorative function, while nocturnal seizures, interictal discharges, antiseizure medications, psychiatric comorbidities, and behavioral adaptations can collectively impair sleep quality. Sleep should therefore be regarded not merely as a contextual modifier of epilepsy, but as an integral component of its pathophysiology, clinical expression, diagnosis, treatment, and long-term outcomes [2,3].
The influence of vigilance state on epileptic activity is particularly evident during non-rapid eye movement sleep, when increased neuronal synchrony facilitates the propagation and scalp detection of epileptiform discharges [4]. Rapid eye movement sleep, by contrast, is generally associated with cortical desynchronization and reduced propagation of epileptic activity [5]. These state-dependent effects vary across epilepsy syndromes and anatomical networks, contributing to the preferential occurrence of seizures during sleep, awakening, or sleep-wake transitions. Sleep-related hypermotor epilepsy, idiopathic generalized epilepsies, juvenile myoclonic epilepsy, and developmental and epileptic encephalopathies with spike-and-wave activation during sleep exemplify the clinical importance of this interaction. In some pediatric syndromes, epileptiform activity during sleep may interfere with neurodevelopment, cognition, language, behavior, and memory consolidation even when overt seizures are infrequent [3,6,7].
Evidence also indicates that epilepsy is associated with measurable disturbances in sleep macrostructure and microstructure. A systematic review and meta-analysis of idiopathic generalized epilepsies demonstrated reduced total sleep time and sleep efficiency, a lower proportion of stage N2 sleep, prolonged rapid eye movement latency, and poorer subjectively perceived sleep quality [6]. More broadly, patients may experience sleep fragmentation, prolonged sleep latency, altered circadian organization, excessive daytime sleepiness, and impaired restorative sleep [8]. These abnormalities are unlikely to be clinically neutral: disturbed sleep may worsen cognitive performance, emotional regulation, treatment adherence, daytime functioning, and quality of life, while potentially lowering the threshold for subsequent seizures. Importantly, seizure risk may depend not only on sleep duration, but also on deviations from an individual’s habitual bedtime and awakening schedule, suggesting that sleep regularity and circadian alignment may be more informative than sleep quantity alone [9].
The burden of comorbid sleep disorders adds a further layer of complexity. Insomnia, obstructive sleep apnea, sleep-related movement disorders, parasomnias, circadian rhythm sleep-wake disorders, and excessive daytime sleepiness appear to be disproportionately represented among people with epilepsy [8,10]. Obstructive sleep apnea is of particular clinical relevance because intermittent hypoxemia, sleep fragmentation, autonomic instability, and excessive sleepiness may compound neurological and cardiovascular vulnerability. A meta-analysis found a substantial burden of obstructive sleep apnea among patients with epilepsy and suggested that treatment may improve seizure control in selected individuals [11]. Nevertheless, sleep disorders frequently remain unrecognized because symptoms are attributed to epilepsy itself, antiseizure treatment, mood disturbances, or the psychosocial consequences of chronic disease [8]. This diagnostic overshadowing may prevent the identification of potentially modifiable contributors to poor seizure control and diminished quality of life.
Treatment further illustrates the reciprocal nature of the relationship. Antiseizure medications may improve sleep indirectly by reducing nocturnal seizures and interictal activity, but they may also alter sleep architecture, promote daytime somnolence, or contribute to insomnia [12]. Available evidence suggests heterogeneous and drug-specific effects, yet direct comparisons remain limited, outcome definitions are inconsistent, and polysomnographic evaluation has been conducted for only a proportion of commonly prescribed agents [13]. Epilepsy surgery and neuromodulation may similarly influence sleep through seizure reduction, medication modification, or direct effects on sleep-regulating networks [14]. Conversely, therapeutic strategies directed primarily at sleep (including optimization of sleep hygiene, treatment of obstructive sleep apnea, behavioral interventions for insomnia, circadian stabilization, exercise, and melatonin) may have consequences for seizure severity and overall well-being [15]. A recent meta-analysis suggested that adjunctive melatonin may improve sleep latency and possibly seizure severity, although the available trials remain limited by methodological heterogeneity and inadequate long-term evaluation [16].
Sleep is also central to the diagnostic evaluation of suspected epilepsy. Routine and sleep-deprived electroencephalography exploit the activating effects of drowsiness and sleep on interictal epileptiform discharges, whereas prolonged video-electroencephalographic monitoring enables the temporal relationship among sleep stages, electrographic abnormalities, and clinical events to be characterized [17,18]. Polysomnography is essential when nocturnal seizures must be differentiated from parasomnias, movement disorders, or sleep-disordered breathing [19]. However, access to laboratory-based assessment remains uneven, and a single night of observation may insufficiently represent habitual sleep or infrequent seizures. These limitations have accelerated interest in ambulatory electroencephalography, actigraphy, wearable sensors, cardiorespiratory monitoring, automated video analysis, and multimodal home-based systems.
Digital technologies offer particular potential to transform this field. Long-term wearable electroencephalography has demonstrated the feasibility of extending seizure monitoring from hospital environments into patients’ homes [20], while multimodal systems combining movement and cardiovascular signals can detect major nocturnal seizures in real-world pediatric settings [21]. Wearable-derived sleep features may also provide dynamic information regarding seizure susceptibility: reduced nocturnal sleep efficiency has been observed before seizures, supporting the concept that sleep may contribute to individualized seizure forecasting [22]. These advances create opportunities for artificial intelligence-supported classification, automated sleep staging, remote surveillance, digital phenotyping, chronotherapy, and personalized behavioral recommendations. Nevertheless, questions concerning algorithmic validity, false alarms, device tolerability, data governance, digital inequity, integration into clinical workflows, and meaningful patient-centered outcomes remain unresolved.
Despite the breadth of the literature, knowledge remains fragmented across neurophysiology, epileptology, sleep medicine, pediatrics, pharmacology, biomedical engineering, and digital health. Previous reviews have generally addressed individual components (such as sleep architecture, obstructive sleep apnea, specific epilepsy syndromes, medication effects, or seizure detection) rather than mapping sleep across the complete epilepsy care continuum. A scoping review is therefore warranted to integrate mechanistic and clinical evidence; characterize sleep disorders and syndrome-specific patterns; examine diagnostic and monitoring technologies; map pharmacological, behavioral, surgical, and sleep-directed interventions; and evaluate implementation, accessibility, and equity. Such an approach may clarify where evidence is sufficiently mature to inform clinical practice, where uncertainty persists, and how sleep-related phenotyping and digital biomarkers could support a transition toward precision epilepsy care.
Methods
Study Design
This scoping review was conducted to comprehensively map the contemporary evidence regarding the multifaceted relationship between sleep and epilepsy across the entire continuum of clinical care. Given the breadth and heterogeneity of the available literature, encompassing basic neuroscience, clinical neurology, sleep medicine, biomedical engineering, digital health, and implementation science, a scoping review methodology was considered the most appropriate approach to systematically identify, characterize, and synthesize existing knowledge while highlighting evidence gaps and future research priorities.
The review methodology followed the original framework proposed by Arksey et al. [23], subsequently refined by Levac et al. [24], and further informed by the most recent recommendations of the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis [25]. Reporting was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), ensuring methodological transparency, reproducibility, and comprehensive reporting [26].
Eligibility Criteria
The eligibility criteria were developed a priori according to the Population-Concept-Context (PCC) framework recommended by the Joanna Briggs Institute [27].
Population
Studies involving individuals of any age diagnosed with epilepsy or recurrent unprovoked seizures were considered eligible. Studies focusing on specific epilepsy syndromes, focal or generalized epilepsies, developmental and epileptic encephalopathies, post-traumatic epilepsy, genetic epilepsies, drug-resistant epilepsy, and epilepsy surgery populations were all included. Studies enrolling both pediatric and adult populations were equally eligible.
Concept
Eligible studies addressed at least one dimension of the complex relationship between sleep and epilepsy. Topics of interest included sleep physiology, neurobiology and architecture; sleep deprivation; circadian rhythm disturbances; sleep disorders, including insomnia, obstructive sleep apnea, parasomnias, and excessive daytime sleepiness; diagnostic modalities such as polysomnography, sleep electroencephalography, actigraphy, and wearable technologies; digital biomarkers, artificial intelligence, and seizure prediction; the effects of antiseizure medications and therapeutic interventions; epilepsy surgery, neuromodulation; implementation strategies; and patient-reported sleep outcomes.:
Context
Studies conducted in any healthcare setting, including outpatient clinics, epilepsy monitoring units, sleep laboratories, rehabilitation centers, primary care, community-based settings, and home-based digital monitoring environments, were considered eligible.
Information Sources
A comprehensive literature search was conducted in PubMed/MEDLINE, Embase, Scopus, Web of Science Core Collection, and the Cochrane Library. To maximize retrieval, the reference lists of eligible studies and relevant reviews were manually screened, and citation tracking of influential publications was undertaken to identify additional pertinent articles. Grey literature, conference abstracts, editorials, letters without original data, dissertations, study protocols, opinion papers, and preprints were excluded to ensure that the review mapped high-quality, peer-reviewed evidence with direct relevance to clinical practice and future research.
Search Strategy
The search strategy was developed collaboratively by investigators with expertise in epilepsy, sleep medicine, and evidence synthesis (NC, YVT, PVP, MLGL). Controlled vocabulary (Medical Subject Headings [MeSH] and Emtree terms) was combined with free-text keywords using Boolean operators to maximize both sensitivity and specificity. Search terms encompassed epilepsy, epileptic seizures, sleep and sleep disorders, sleep deprivation, sleep architecture, polysomnography, electroencephalography, circadian rhythm, actigraphy, wearable technologies, digital health, remote monitoring, artificial intelligence, and seizure prediction. The complete search strategies for all databases are provided in the Supplementary Material. The literature search covered all records available from the inception of each database through July 2026, ensuring a comprehensive assessment of both seminal and contemporary evidence. Only articles published in English were considered eligible.
Study Selection
All retrieved records were imported into a dedicated reference management software, where duplicate citations were identified and removed through automated procedures followed by manual verification. Study selection was conducted independently by two reviewers using a two-stage screening process consisting of title and abstract review followed by full-text eligibility assessment. Any disagreements were resolved through discussion and, when necessary, by consultation with a third senior reviewer. The entire selection process was documented using the PRISMA 2020 flow diagram adapted for scoping reviews, ensuring transparency and reproducibility.
Data Extraction
Data were extracted independently by three investigators (YVT, PVP, JN) using a standardized electronic data-charting form that was pilot-tested before implementation. Whenever available, information was collected on study characteristics, design, objectives, population, epilepsy syndrome, sleep-related outcomes, diagnostic and assessment methods, digital technologies, interventions, comparators, principal findings, implementation aspects, study limitations, and authors’ conclusions. Any discrepancies in data extraction were resolved through discussion until consensus was achieved.
Data Synthesis
In keeping with the objectives of a scoping review, no quantitative meta-analysis was undertaken. Instead, the evidence was synthesized descriptively and narratively across predefined domains spanning the epilepsy care continuum. These included the neurobiology of sleep and epileptogenesis; sleep architecture and sleep disorders; circadian rhythms and seizure susceptibility; the effects of antiseizure medications; diagnostic approaches, including polysomnography, sleep electroencephalography, actigraphy, and multimodal monitoring; digital health technologies, wearable devices, artificial intelligence, and seizure prediction; sleep-targeted therapeutic interventions; epilepsy surgery and neuromodulation; patient-reported outcomes; implementation barriers and facilitators; and health equity and precision medicine. Whenever appropriate, findings were further stratified according to age group, epilepsy subtype, drug responsiveness, and monitoring setting to facilitate a more comprehensive interpretation of the available evidence.
Critical Appraisal
In accordance with current Joanna Briggs Institute guidance for scoping reviews, formal methodological quality assessment or risk-of-bias evaluation was not undertaken because the primary objective was to map the extent, characteristics, and distribution of the available evidence rather than determine intervention effectiveness or generate pooled estimates. Nevertheless, study design, methodological characteristics, and major limitations were systematically recorded during data charting to facilitate interpretation of the evidence landscape and to identify areas requiring higher-quality investigation.
Involvement and Ethical Considerations
Patients or members of the public were not involved in the design, conduct, reporting, or dissemination plans of this scoping review. As this review synthesized data derived exclusively from previously published studies, institutional ethics committee approval and informed consent were not required.
Protocol Registration
Although prospective registration is not mandatory for scoping reviews, the review protocol was developed before initiation of the literature search following JBI methodological guidance and PRISMA-ScR recommendations to maximize methodological rigor, transparency, and reproducibility.
Results
Characteristics and Evolution of the Evidence Base
The literature exploring the relationship between sleep and epilepsy spans more than five decades and reflects the progressive integration of epileptology, sleep medicine, clinical neurophysiology, pediatrics, pharmacology, biomedical engineering, and digital health. The study-selection process is illustrated in the PRISMA flow diagram (Figure 1), whereas the principal characteristics of the included publications are summarized in Table 1.
The evidence has evolved through several interconnected phases. Early investigations were primarily physiological and diagnostic, employing conventional electroencephalography (EEG), sleep-deprived EEG, video-EEG monitoring, and polysomnography to investigate the activation of interictal epileptiform discharges during sleep and the temporal distribution of seizures across vigilance states. These seminal studies established that sleep is an active regulator of epileptic network synchronization rather than a passive background condition, with non-rapid eye movement (NREM) sleep generally facilitating epileptiform activity. More recent research has expanded this paradigm by incorporating syndrome-specific analyses, sleep microstructure, cyclic alternating patterns, circadian and multidien rhythms, and habitual sleep timing. Notably, deviations in bedtime and wake time appear to be more closely associated with seizure occurrence than sleep duration alone, highlighting the importance of sleep regularity and circadian alignment [9,28].
Subsequent investigations broadened their focus to sleep architecture, subjective sleep quality, daytime functioning, and comorbid sleep disorders. Meta-analytic evidence consistently demonstrates that people with epilepsy experience poorer sleep quality and greater daytime sleepiness than healthy controls, although substantial heterogeneity exists across epilepsy syndromes, treatment regimens, and assessment methods [29]. Objective polysomnographic studies have similarly documented reduced sleep efficiency, alterations in REM sleep, and disruption of restorative sleep stages [30,31]. In idiopathic generalized epilepsies, available evidence suggests shorter total sleep time, lower sleep efficiency, prolonged REM latency, and impaired subjective sleep quality, although the supporting literature remains limited and methodologically heterogeneous [30].
Pediatric studies represent a distinct and rapidly expanding body of evidence, addressing sleep disturbances, neurodevelopment, cognition, behavior, memory consolidation, family functioning, and developmental and epileptic encephalopathies. Systematic reviews indicate that children and adolescents with epilepsy exhibit shorter sleep duration, reduced sleep efficiency, more frequent nocturnal awakenings, and greater sleep fragmentation than typically developing peers [32]. Nevertheless, pediatric research remains clinically heterogeneous, encompassing diverse epilepsy syndromes and developmental profiles. Sleep-dependent memory consolidation and broader functional outcomes (including cognition, behavior, quality of life, and family well-being) remain comparatively underexplored despite their recognized clinical importance [32,33,34].
A substantial proportion of the literature has focused on comorbid sleep disorders, particularly obstructive sleep apnea (OSA), insomnia, parasomnias, restless legs syndrome, and excessive daytime sleepiness. OSA has received the greatest attention because intermittent hypoxemia, recurrent arousals, and sleep fragmentation may increase seizure susceptibility while contributing to cognitive impairment and reduced quality of life [35]. Meta-analytic evidence indicates a high prevalence of OSA among individuals with epilepsy and suggests that effective treatment may improve seizure control in selected patients, although available studies remain predominantly observational [36].
Therapeutic research has become increasingly diverse, encompassing antiseizure medications, behavioral interventions, treatment of sleep disorders, melatonin, exercise, epilepsy surgery, and neuromodulation. However, evidence remains uneven across interventions. Some antiseizure medications have been evaluated using polysomnography or validated sleep questionnaires, whereas others are supported only by small observational studies [13]. Similarly, exercise interventions appear promising but are supported by relatively few heterogeneous studies [37].
More recently, the field has undergone a profound methodological transformation through the introduction of prolonged ambulatory monitoring, wearable technologies, digital biomarkers, and artificial intelligence. Wearable EEG systems have demonstrated the feasibility of long-term monitoring in both hospital and home environments [20], while multimodal wearable devices have shown encouraging performance for nocturnal seizure detection [21]. Sleep-derived physiological signals are increasingly being investigated as predictive biomarkers, with reductions in nocturnal sleep efficiency and alterations in sleep timing preceding seizures in exploratory studies [22,38]. Despite these advances, external validation, standardization, and real-world implementation remain limited.
Overall, the included studies comprised observational cohorts, cross-sectional and case-control investigations, diagnostic-accuracy studies, clinical trials, physiological experiments, implementation studies, systematic reviews, and meta-analyses, with observational designs predominating. Collectively, the evidence demonstrates a clear conceptual evolution: sleep has progressed from being viewed primarily as a diagnostic facilitator of epileptiform activity to becoming a multidimensional determinant of health, a modifiable therapeutic target, and a source of dynamic digital biomarkers. Nevertheless, important evidence gaps persist, particularly regarding intervention effectiveness, implementation science, health equity, cost-effectiveness, and the systematic integration of sleep assessment into routine epilepsy care pathways.
Epidemiology of Sleep Disturbances in Epilepsy, and Clinical Burden
Sleep disturbances were among the most consistently investigated clinical manifestations across the included studies and were reported throughout the lifespan, affecting individuals with newly diagnosed, chronic, focal, generalized, genetic, developmental, and drug-resistant epilepsies. Although prevalence estimates varied according to study design, diagnostic criteria, epilepsy syndrome, and methods of sleep assessment, the collective evidence unequivocally demonstrated that sleep disorders occur significantly more frequently in people with epilepsy than in the general population [29,39].
Insomnia emerged as the most commonly reported sleep complaint. Across observational studies and patient-reported surveys, difficulties initiating or maintaining sleep, early morning awakening, non-restorative sleep, and poor subjective sleep quality were consistently described [29,30,40]. These disturbances were particularly frequent among patients with uncontrolled seizures, psychiatric comorbidities, polytherapy, and drug-resistant epilepsy.
Excessive daytime sleepiness was another prominent finding, most commonly assessed using the Epworth Sleepiness Scale. Regardless of the reported prevalence, excessive daytime sleepiness was consistently associated with impaired daily functioning and poorer health-related quality of life. The available evidence suggests that this symptom reflects the combined effects of nocturnal seizures, sleep fragmentation, antiseizure medications, psychiatric comorbidity, and coexisting sleep disorders rather than a single pathophysiological mechanism [29,41].
Among sleep comorbidities, OSA received the greatest attention. Systematic reviews and meta-analyses consistently demonstrated a substantially higher prevalence of OSA in people with epilepsy than in the general population, despite considerable variability attributable to differences in screening tools, diagnostic criteria, patient selection, and epilepsy severity [35,36]. OSA therefore represents one of the most clinically relevant and potentially modifiable sleep disorders in epilepsy.
Less frequently investigated conditions, including restless legs syndrome, periodic limb movement disorder, parasomnias, circadian rhythm sleep-wake disorders, and hypersomnolence, also appeared more prevalent than expected in healthy populations. However, the available evidence remains fragmented, largely because of methodological heterogeneity and the limited number of multicenter studies [30].
Sleep disturbances were particularly common in drug-resistant epilepsy and sleep-related epilepsy syndromes, whereas patients with well-controlled epilepsy generally reported fewer sleep complaints. Pediatric studies likewise documented a substantial burden of sleep disturbances, although manifestations more frequently included behavioral sleep problems, fragmented sleep, bedtime resistance, and adverse effects on family functioning [32,34].
Overall, the available evidence consistently identifies disturbed sleep as one of the most prevalent and clinically relevant comorbidities in epilepsy. Although methodological heterogeneity and geographical disparities continue to limit precise epidemiological estimates, disturbances of sleep quality, sleep continuity, daytime alertness, and sleep-disordered breathing clearly represent integral components of the epilepsy phenotype across all age groups, underscoring the need for their systematic assessment in both clinical practice and future research.
Neurobiology of Sleep and Epileptogenesis
The literature consistently characterized sleep as a dynamic neurophysiological state capable of modifying the expression, synchronization, spatial distribution, and propagation of epileptic activity. Electroencephalographic, polysomnographic, and intracranial-recording studies demonstrated that these effects depend on vigilance state, sleep stage, oscillatory dynamics, epilepsy syndrome, and the anatomical organization of the epileptic network. NREM and REM sleep generally exerted opposing influences: NREM sleep facilitated epileptiform activity, whereas REM sleep predominantly restricted its occurrence and propagation [42,43].
NREM Sleep and Epileptic Network Synchronization
NREM sleep was consistently associated with increased interictal epileptiform discharges and, in several syndromes, greater seizure susceptibility. Its activating influence reflects the synchronization of cortical and thalamocortical networks through slow oscillations, delta activity, sleep spindles, and alternating neuronal up- and downstates. These physiological rhythms provide a temporal framework within which pathological neuronal populations may become synchronized and propagate across distributed networks [43].
Epileptiform activity was not uniformly distributed throughout NREM sleep but was preferentially associated with particular oscillatory events, sleep-state transitions, and fluctuations in arousal. Intracranial recordings in children with refractory focal epilepsy further demonstrated temporal coupling between sleep spindles, interictal discharges, and pathological high-frequency oscillations, extending beyond the clinically defined epileptogenic zone and correlating with poorer neurocognitive function [44].
Sleep Microstructure and the Cyclic Alternating Pattern
The cyclic alternating pattern (CAP) provided a particularly informative framework for understanding NREM instability. Ictal and interictal events occurred preferentially during CAP phase A, especially the highly synchronized A1 subtype, whereas phase B was associated with relative inhibition. This modulation was evident in generalized epilepsy and lesional frontotemporal epilepsies, although it was not uniform across all syndromes [45]. In temporal lobe epilepsy, morning recovery sleep after deprivation increased CAP instability and concentrated interictal discharges within phase A, supporting a microstructural contribution to the activating effect of sleep deprivation [46].
REM Sleep and Suppression of Epileptic Activity
REM sleep was associated with a markedly lower frequency of seizures and interictal discharges. Its cortical desynchronization appears to constrain recruitment and propagation within epileptic networks, explaining both the relative rarity of seizures during REM sleep and the reduced spatial extent of persisting discharges [47].
Although uncommon, REM-associated epileptiform discharges may offer greater localizing value than those recorded during NREM sleep because they remain more spatially restricted. A systematic review nevertheless emphasized methodological heterogeneity and cautioned that REM does not invariably provide superior localization [48].
Thalamocortical Networks and Developmental Epilepsies
Sleep-dependent activation was particularly prominent in epilepsies involving thalamocortical circuitry. In idiopathic generalized epilepsies, generalized spike-and-wave discharges were facilitated during unstable transitions between wakefulness and NREM sleep, whereas alert wakefulness and consolidated REM sleep exerted relative inhibition [49].
In developmental and epileptic encephalopathies with marked activation during sleep, pathological discharges may occupy a substantial proportion of slow-wave sleep and interfere with physiological oscillations supporting neurodevelopment, synaptic plasticity, and cognition. The coupling of interictal and high-frequency activity with sleep spindles provides direct evidence that epileptic networks may disrupt normally adaptive sleep-dependent communication [44].
Hippocampal-Cortical Coupling and Memory-Related Oscillations
In temporal lobe epilepsy, pathological ripples and interictal discharges may appropriate the physiological coupling among hippocampal sharp-wave ripples, cortical slow oscillations, and thalamocortical spindles that ordinarily supports memory consolidation [50]. Human recordings have similarly linked mesial-temporal epileptic ripples with poorer verbal memory performance [51].
Sleep Deprivation and Epileptogenesis
Sleep-deprived EEG increases the detection of interictal abnormalities, particularly in focal epilepsy, although the relative contributions of deprivation itself, subsequent sleep, circadian timing, and increased NREM instability remain incompletely separated [52].
Overall, the evidence robustly demonstrates that sleep regulates the expression of established epileptic networks through state-dependent synchronization. By contrast, direct human evidence that chronic sleep disturbance independently initiates or accelerates epileptogenesis remains limited; this relationship continues to rely predominantly on experimental and translational observations.
Sleep Architecture in Epilepsy
Alterations in sleep architecture represent one of the most reproducible objective findings in epilepsy and have been documented across children and adults with focal, generalized, genetic, developmental, and drug-resistant epilepsies. Evidence derives predominantly from overnight polysomnography, prolonged video-electroencephalographic (video-EEG) monitoring, and, more recently, ambulatory sleep assessment. Although the magnitude of abnormalities varies according to epilepsy syndrome, seizure burden, antiseizure medication exposure, age, and comorbid sleep disorders, the overall evidence consistently demonstrates impaired sleep continuity, reduced sleep efficiency, alterations in NREM organization, and particularly reduced or delayed REM sleep [30,32].
Overall Sleep Macrostructure
Systematic reviews have shown that adults with epilepsy exhibit a characteristic disruption of sleep macrostructure compared with healthy individuals, including lower sleep efficiency, prolonged sleep latency, increased wake after sleep onset, and changes in the relative distribution of NREM and REM sleep. Nevertheless, considerable heterogeneity persists because published studies include diverse epilepsy syndromes, variable seizure control, different polysomnographic methodologies, and inconsistent adjustment for antiseizure medications [30].
A subsequent meta-analysis incorporating more than 1,300 participants confirmed reduced sleep efficiency as the most consistent polysomnographic abnormality across epilepsy populations [32]. Interestingly, patients with focal epilepsy showed lower REM sleep proportions, whereas generalized epilepsy was characterized by reduced sleep efficiency together with a relative increase in slow-wave sleep. Importantly, similar abnormalities were observed in untreated patients, indicating that epilepsy itself contributes to altered sleep organization independently of pharmacological treatment.
Sleep Continuity and NREM Organization
Reduced sleep efficiency primarily reflected impaired sleep continuity, including prolonged sleep latency, recurrent nocturnal awakenings, and increased wake after sleep onset. Objective polysomnographic studies in newly diagnosed, drug-naïve patients demonstrated that these abnormalities are already present at disease onset, supporting the concept that disrupted sleep architecture is an intrinsic feature of epilepsy rather than merely a consequence of chronic disease or medication exposure [53].
Alterations within NREM sleep were less uniform but consistently indicated reduced sleep stability. Patients with focal epilepsy, particularly temporal lobe epilepsy, frequently demonstrated increased proportions of lighter NREM sleep (stages N1 and N2) together with reduced slow-wave sleep, whereas generalized epilepsies occasionally showed preserved or even increased N3 sleep [30]. These findings suggest that epilepsy does not produce a single stereotyped NREM phenotype but rather syndrome-specific alterations reflecting differences in epileptic network organization.
Beyond conventional sleep staging, studies of NREM microstructure have demonstrated increased instability of the cyclic alternating pattern (CAP), indicating that apparently normal sleep may conceal important disturbances in cortical regulation. CAP analysis therefore provides complementary information to standard polysomnographic measures and may better reflect the physiological substrate that facilitates epileptiform synchronization [43,45].
REM Sleep Abnormalities
Among individual sleep stages, REM sleep exhibited the most reproducible abnormalities. Reduced REM sleep duration and prolonged REM latency have been documented across focal and generalized epilepsies and appear particularly pronounced in refractory disease. Because similar findings have also been observed in untreated patients, these alterations cannot be explained solely by antiseizure medication effects [30].
Recent neurophysiological studies further suggest that epilepsy affects not only REM quantity but also its physiological organization. High-density EEG recordings demonstrated reduced REM-associated sawtooth-wave activity in focal epilepsy, indicating that pathological network activity may modify intrinsic REM oscillatory mechanisms even during periods without overt epileptiform discharges [42].
Differences Across Epilepsy Syndromes
Sleep architecture varies substantially according to epilepsy subtype. Focal epilepsies generally exhibit more pronounced reductions in REM sleep and sleep efficiency, whereas generalized epilepsies more frequently show altered NREM distribution. Drug-resistant epilepsy, particularly temporal lobe epilepsy associated with structural lesions such as hippocampal sclerosis, is consistently associated with greater sleep fragmentation, lower efficiency, reduced REM sleep, and increased lighter NREM stages. However, these observations are influenced by higher seizure frequency, longer disease duration, nocturnal seizures, and polytherapy, making it difficult to isolate the independent contribution of structural pathology or pharmacoresistance [13,30].
Pediatric populations demonstrate a similarly consistent pattern of disrupted sleep architecture. A systematic review and meta-analysis showed that children and adolescents with epilepsy experience shorter sleep duration, reduced sleep efficiency, more nocturnal awakenings, and increased sleep fragmentation compared with typically developing peers [32]. However, interpretation remains complicated by developmental changes in normal sleep architecture and the marked heterogeneity of pediatric epilepsy syndromes.
Methodological Considerations
Interpretation of the available literature is constrained by important methodological heterogeneity. Studies differ with respect to recording environment, seizure occurrence during monitoring, inclusion of adaptation nights, exclusion of coexisting sleep disorders, epilepsy classification, and polysomnographic scoring criteria. Antiseizure medication exposure represents an additional source of variability because relatively few investigations have evaluated untreated or newly diagnosed patients. Nevertheless, the persistence of architectural abnormalities in drug-naïve cohorts strongly supports the conclusion that epilepsy itself contributes to altered sleep organization [30].
Overall, the available evidence consistently indicates that epilepsy is characterized by impaired sleep continuity, reduced sleep efficiency, altered NREM stability, and particularly diminished REM sleep. Although the precise pattern differs across epilepsy syndromes, age groups, and disease severity, these abnormalities are evident even before antiseizure treatment and constitute a core neurophysiological feature of epilepsy rather than a secondary consequence of therapy alone.
Sleep Disorders in Epilepsy
Specific sleep disorders are highly prevalent across the epilepsy population and contribute to impaired daytime functioning, psychiatric morbidity, poorer quality of life, and diagnostic uncertainty. The literature has focused principally on insomnia, obstructive sleep apnea (OSA), excessive daytime sleepiness, restless legs syndrome (RLS), and parasomnias, with comparatively fewer studies addressing circadian rhythm sleep-wake disorders. Reported frequencies vary substantially according to recruitment setting, epilepsy phenotype, diagnostic criteria, and whether sleep disturbance was established through questionnaires, clinical interviews, polysomnography, or objective daytime-vigilance testing.
Insomnia
Insomnia is among the most frequently reported sleep disorders in epilepsy, encompassing difficulty initiating or maintaining sleep, early awakening, and non-restorative sleep. A dedicated review found consistently higher frequencies of insomnia symptoms and formally diagnosed insomnia among people with epilepsy than among controls, while also documenting wide prevalence ranges attributable to differences in populations and diagnostic methods [54].
Insomnia severity is closely associated with depression, anxiety, reduced total sleep time, and impaired quality of life. In a questionnaire-based study, insomnia and poor sleep were common and were associated with worse quality-of-life scores [55]. A prospective investigation further showed that insomnia was associated with continuing seizures during follow-up and poorer quality of life, although its observational design did not establish whether insomnia worsened seizure control or reflected greater epilepsy-related burden [40].
The evidence therefore supports insomnia as a clinically meaningful comorbidity rather than a simple consequence of seizure frequency. Nevertheless, psychiatric symptoms, nocturnal seizures, medication effects, and maladaptive sleep behaviors frequently coexist, making causal attribution difficult. Moreover, most studies assessed insomnia symptoms rather than chronic insomnia disorder through structured diagnostic interviews, and epilepsy-specific interventional evidence remains limited.
Obstructive Sleep Apnea and Sleep-Disordered Breathing
OSA is the most extensively investigated objectively diagnosable sleep disorder in epilepsy. A meta-analysis demonstrated a substantial burden of OSA among people with epilepsy, with prevalence influenced by age, diagnostic threshold, referral setting, and the method used to identify respiratory events [11]. Established risk factors, including obesity, male sex, advancing age, hypertension, and habitual snoring, remain relevant, but symptoms such as fatigue, cognitive dysfunction, nocturnal awakening, and daytime somnolence may be incorrectly attributed to epilepsy or antiseizure treatment.
The clinical relationship between OSA and seizure control remains incompletely resolved. A randomized pilot trial established the feasibility of therapeutic continuous positive airway pressure in adults with epilepsy and OSA but was not sufficiently powered to determine a definitive antiseizure effect [56]. Accordingly, OSA should be identified and treated because of its established respiratory, cardiovascular, cognitive, and quality-of-life consequences, while any independent improvement in seizure control should be regarded as plausible but not conclusively demonstrated.
Pediatric evidence likewise indicates increased sleep-disordered breathing within a broader pattern of sleep disruption. A systematic review and meta-analysis found that children and adolescents with epilepsy experienced more nocturnal waking, parasomnias, and sleep-disordered breathing, together with shorter sleep duration and reduced sleep efficiency, than healthy controls [32].
Excessive Daytime Sleepiness
Excessive daytime sleepiness is common but etiologically heterogeneous. A systematic review concluded that sleepiness may arise from insufficient or fragmented sleep, undiagnosed sleep disorders, nocturnal seizures, psychiatric comorbidity, and antiseizure medications, rather than from epilepsy alone [41]. The largest quantitative synthesis similarly demonstrated greater daytime sleepiness and poorer subjective sleep quality in people with epilepsy than in controls, although heterogeneity across studies was substantial [29].
Most investigations relied on the Epworth Sleepiness Scale, which cannot reliably distinguish physiological hypersomnolence from fatigue, sedation, cognitive slowing, or diminished motivation. Importantly, a prospective study incorporating the Multiple Sleep Latency Test found both subjective and objective hypersomnolence to be highly prevalent and not fully explained by seizure frequency, medication burden, insomnia, depression, or conventional polysomnographic findings [57]. These findings support direct evaluation of sleepiness rather than its automatic attribution to antiseizure therapy.
Restless Legs Syndrome
RLS has been studied less extensively but appears more frequent in adults with epilepsy than in healthy populations. A recent systematic review and meta-analysis estimated a pooled prevalence of approximately 15%, while emphasizing substantial heterogeneity and limitations within the contributing observational studies [58].
Recognition may be complicated by nocturnal motor activity, sensory seizures, akathisia, peripheral neuropathy, medication adverse effects, and periodic limb movements. Available studies have associated RLS with poorer sleep and daytime impairment, but relationships with seizure type, treatment, and epilepsy severity remain inconsistent. Evidence regarding targeted RLS treatment in epilepsy is still insufficient.
Parasomnias and Diagnostic Overlap
Parasomnias are particularly important because their clinical manifestations may resemble sleep-related epileptic seizures. NREM disorders of arousal can involve complex movements, vocalization, autonomic activation, altered responsiveness, and incomplete recall. Conversely, sleep-related hypermotor epilepsy may produce bizarre motor behaviors with limited or obscured scalp-EEG changes.
The Frontal Lobe Epilepsy and Parasomnias scale showed that clinical history can assist differentiation, with brief, stereotyped, clustered, and recurrent events favoring epilepsy [59]. Video-EEG analysis subsequently demonstrated that interactive behavior, event variability, and prolonged duration more strongly favored NREM parasomnias, whereas epileptic events were generally shorter and more stereotyped [60]. When uncertainty persists, prolonged video-EEG or video-polysomnography with an expanded EEG montage remains essential. Multidisciplinary consensus recommendations support a structured diagnostic pathway integrating detailed semiology, home video, EEG, video-EEG, and polysomnography according to the clinical question [61].
Circadian Rhythm Disturbances
Chronotype and circadian sleep-wake organization remain comparatively underexplored. A recent scoping review found a limited and heterogeneous evidence base, with inconsistent associations among morningness-eveningness preference, epilepsy subtype, seizure control, and medication use [62]. A prospective controlled study nonetheless demonstrated a greater burden of sleep disorders and chronotype abnormalities in patients with drug-resistant focal or generalized epilepsy than in healthy controls [63]. Formal circadian rhythm sleep-wake disorders, however, remain insufficiently characterized.
Overall, insomnia, OSA, excessive daytime sleepiness, RLS, and parasomnias constitute clinically relevant components of epilepsy care. OSA has the strongest objective diagnostic and interventional evidence, whereas insomnia and sleepiness are highly prevalent but difficult to disentangle from psychiatric comorbidity, medication effects, and seizure burden. RLS appears more frequent than in control populations, while parasomnias require particular attention because of their diagnostic overlap with nocturnal epilepsy. Systematic, disorder-specific screening and appropriately selected physiological testing should therefore complement routine seizure assessment.
Circadian and Multidien Organization of Epileptic Activity
The included literature consistently demonstrated that seizures and interictal epileptiform activity are temporally organized rather than randomly distributed. Recurrent fluctuations occur across circadian and multidien timescales, while ultradian and circannual patterns have been reported less consistently. The phase, amplitude, and clinical expression of these rhythms vary markedly between individuals and according to epilepsy syndrome, seizure-onset region, sleep-wake state, treatment, and recording methodology [64]. Technological advances, including implanted neurostimulation systems, ultra-long-term subcutaneous EEG, wearable sensors, and electronic diaries, have enabled continuous observation over months or years, revealing that daily rhythms coexist with slower, patient-specific cycles of seizure susceptibility [65].
Circadian Distribution of Seizures
Circadian modulation has been documented across focal and generalized epilepsies, although no universal peak period exists. Seizure timing differs according to anatomical localization and seizure type: temporal lobe seizures tend to occur preferentially during waking hours, whereas frontal and parietal seizures show a stronger association with sleep. In pediatric video-EEG cohorts, seizure occurrence varied systematically by localization, seizure semiology, and vigilance state, confirming that clock time alone incompletely captures temporal organization [66].
Generalized seizure types also display distinct temporal profiles. In children and adolescents, absence, myoclonic, and generalized tonic-clonic seizures were differentially distributed across the 24-hour day and the sleep-wake cycle; generalized tonic-clonic seizures occurred predominantly during wakefulness and around awakening, whereas other generalized seizure types exhibited more variable state dependence [67]. Evolution to bilateral tonic-clonic activity likewise followed non-random circadian and sleep-wake patterns, although estimates were limited by relatively small event numbers [68].
Sleep-Wake State Versus Circadian Phase
Distinguishing endogenous circadian modulation from the effects of sleep and wakefulness remains challenging because habitual sleep occurs at a relatively stable biological phase. Apparent day-night differences may therefore reflect sleep stage, behavioral state, intrinsic circadian timing, or their interaction. Importantly, time of day is an imperfect surrogate for vigilance state; inpatient video-EEG data have shown that seizures occurring at similar clock times may arise from either sleep or wakefulness [69].
Long-term intracranial recordings nevertheless demonstrate stable circadian oscillations in interictal epileptiform activity that extend beyond immediate sleep effects. Seizures preferentially occur at particular phases of these internal rhythms, and the preferred phase varies between patients. Accordingly, an individual’s biological cycle may be more informative than population-level clock-time categories [28].
Habitual sleep timing may also contribute to risk. In a prospective longitudinal study using wearable-derived sleep estimates and seizure diaries, deviations in bedtime and wake time were associated with subsequent seizures in more participants than reductions in sleep duration [9]. These findings suggest that sleep regularity may be more relevant than sleep quantity for some individuals, although no universal threshold was identified.
Chronotype
Chronotype remains comparatively underinvestigated. A scoping review identified only 11 major studies and found heterogeneous results regarding morningness-eveningness preference, epilepsy type, and seizure control [62]. In a controlled study of adults with late-onset focal epilepsy, participants more frequently described themselves as morning-oriented; however, dim-light melatonin onset did not demonstrate a corresponding advance in endogenous circadian phase [70]. Subjective chronotype, habitual sleep timing, physiological circadian phase, and seizure periodicity should therefore be regarded as related but non-equivalent constructs.
Multidien Rhythms and Seizure Forecasting
Ultra-long-term monitoring has expanded the temporal framework beyond the 24-hour cycle. In 37 individuals with drug-resistant focal epilepsy implanted with responsive neurostimulation systems, interictal epileptiform activity exhibited robust circadian and patient-specific multidien rhythms, commonly lasting 20-30 days and remaining stable for years [28]. Seizures occurred preferentially during the rising phase of multidien activity, while the interaction of circadian and multidien phases defined periods of higher and lower susceptibility.
A larger analysis of chronic intracranial recordings confirmed substantial interindividual diversity, identifying multiple circadian peak profiles and multidien cycles ranging from weekly to longer periodicities [71]. These rhythms enabled personalized forecasting: models based on implanted-device recordings stratified seizure risk several days in advance in adults with focal epilepsy [38].
Minimally invasive subcutaneous EEG provided corroborating evidence. Continuous monitoring over 230 days detected more seizures than the patient diary and revealed both circadian and approximately five-day cycles, illustrating the limitations of self-reported event timing [72]. Subsequent research demonstrated proof of principle for individualized forecasting using subcutaneous EEG [73]. Multiday physiological rhythms have also been detected non-invasively: smartwatch-derived resting heart-rate cycles were associated with seizure likelihood in some participants, although similar rhythms in healthy controls indicated that the underlying oscillation was not epilepsy-specific [74].
Chronotherapy and Methodological Considerations
Predictable seizure peaks provide a rationale for chronotherapy, in which drug exposure is increased during an individual’s highest-risk interval. Small pediatric studies reported improved control of nocturnal and early-morning seizures with higher evening dosing, but these findings arose from selected, non-randomized cohorts [75,76]. Reviews therefore regard chronopharmacology as promising but insufficiently validated for routine use, emphasizing the need for adequately powered randomized trials [77].
Interpretation remains constrained by underreporting in diaries, short video-EEG observation windows, highly selected implanted-device populations, changing detection algorithms, and heterogeneous statistical methods. Overall, the evidence establishes that epileptic activity is organized across interacting circadian and multidien timescales. These patterns are clinically meaningful but profoundly individualized, supporting prolonged objective monitoring and personalized forecasting rather than reliance on broad labels such as “morning” or “nocturnal” epilepsy.
Antiseizure Medications, Sleep Architecture, and Daytime Vigilance
Antiseizure medications (ASMs) may influence sleep through direct pharmacological effects on sleep initiation, continuity, stage distribution, NREM microstructure, REM sleep, and daytime alertness. Their net clinical effect is nevertheless more complex: improved seizure control may reduce nocturnal awakenings and epileptiform activity, whereas psychiatric adverse effects, weight gain, respiratory vulnerability, and polytherapy may impair sleep indirectly. Accordingly, medication-related changes cannot be interpreted independently of epilepsy syndrome, seizure burden, dose, treatment duration, comorbid sleep disorders, and baseline sleep architecture.
Overall Evidence
Current evidence supports heterogeneous, medication-specific effects rather than a uniform ASM class effect. A comprehensive review of 25 agents found that eslicarbazepine acetate, lacosamide, and perampanel generally improved or did not materially alter sleep, whereas phenobarbital was more consistently associated with daytime sleepiness and findings for valproate were mixed [13]. Carbamazepine, cannabidiol, and levetiracetam were largely neutral in the available epilepsy-specific literature, although the quality and quantity of evidence varied markedly among drugs.
A systematic review and meta-analysis of randomized polysomnographic studies confirmed that ASMs can modify sleep-stage distribution, sleep efficiency, latency, and arousal indices, but drug-specific estimates were frequently based on very few studies [78]. Importantly, participants were not restricted to people with epilepsy, limiting direct clinical extrapolation. A broader systematic review similarly concluded that newer agents generally exert fewer or less pronounced effects on sleep architecture than older medications, while emphasizing extensive methodological heterogeneity and the scarcity of robust comparative trials [79].
Sleep abnormalities cannot, however, be attributed exclusively to pharmacotherapy. Meta-analytic evidence demonstrates reduced sleep efficiency in both treated and untreated epilepsy, indicating an intrinsic contribution of the disease itself [31]. Treatment may partially normalize selected abnormalities, particularly REM sleep in generalized epilepsy, but such changes may reflect improved seizure control rather than a direct pharmacological action.
Predominantly Sedating and Older Agents
Phenobarbital and benzodiazepines produce the most recognizable sedative profiles. They may facilitate sleep initiation and reduce nocturnal awakenings, yet simultaneously alter physiological stage organization and impair daytime vigilance. Phenobarbital has been associated particularly with daytime somnolence, while benzodiazepines may suppress arousability and modify REM and slow-wave sleep. In susceptible patients, respiratory depressant effects may compound underlying sleep-disordered breathing. Nevertheless, much of this evidence derives from older, small, or non-epilepsy studies, and tolerance to sedation may develop over time [13,79].
Evidence for phenytoin remains insufficient and inconsistent. Carbamazepine may initially increase slow-wave sleep while reducing REM sleep, although these changes appear less consistent during prolonged treatment. Valproate generally has limited or variable direct polysomnographic effects; however, somnolence and treatment-related weight gain may indirectly worsen daytime functioning or increase OSA susceptibility. These indirect pathways have rarely been evaluated longitudinally.
Sodium-Channel Modulators
The sleep profiles of sodium-channel modulators are heterogeneous. Lamotrigine has been associated clinically with insomnia, yet objective evidence does not uniformly show impaired sleep continuity. In a small epilepsy cohort, lamotrigine reduced arousals and stage shifts, increased stage N2 sleep and the number of REM periods, and decreased slow-wave sleep without worsening daytime sleepiness [80]. These findings illustrate the incomplete correspondence between spontaneously reported insomnia and polysomnographic architecture.
Lacosamide appears comparatively sleep-neutral. A preliminary prospective study of monotherapy documented improved sleep efficiency without significant deterioration in REM sleep or daytime vigilance, although concurrent seizure reduction and the limited sample prevented attribution of the effect solely to the medication [81]. Available evidence for eslicarbazepine acetate likewise suggests neutral or potentially favorable effects, but dedicated polysomnographic studies and active comparisons remain sparse.
SV2A Ligands and Other Broad-Spectrum Agents
Levetiracetam has been investigated more extensively than many newer ASMs. In healthy volunteers, it consolidated nocturnal sleep without impairing objective daytime vigilance [82]. Epilepsy studies have generally supported a neutral architectural profile, although individual patients may experience insomnia, somnolence, irritability, or mood-related sleep disruption. Small physiological studies may inadequately capture these behavioral effects.
Perampanel may have a neutral or favorable sleep profile. In patients with refractory focal epilepsy, adjunctive treatment improved selected sleep-architecture measures without worsening daytime sleepiness [83]. However, psychiatric adverse effects may indirectly disturb sleep in vulnerable individuals, and the evidence remains insufficient to establish superiority over other agents.
Objective sleep data for brivaracetam, cenobamate, cannabidiol, fenfluramine, and several therapies used in developmental and epileptic encephalopathies remain limited. Somnolence is frequently recorded in clinical trials, but adverse-event reporting cannot establish effects on physiological sleep architecture. With cannabidiol, concomitant clobazam and pharmacokinetic interaction further complicate attribution.
α2δ Ligands
Gabapentin and pregabalin frequently increase slow-wave sleep, improve continuity, and reduce awakenings in studies of neuropathic pain, insomnia, and other non-epilepsy conditions. Their sedative properties may facilitate sleep initiation but can also produce daytime somnolence, particularly during titration, in older adults, or with other central nervous system depressants. Because most objective evidence originates outside epilepsy, these findings should not be extrapolated uncritically to patients whose sleep disturbance arises from seizures or nocturnal epileptiform activity [84].
Polytherapy and Daytime Vigilance
Polytherapy is repeatedly associated with poorer subjective sleep and greater daytime sleepiness, but it is also a marker of drug-resistant and more severe epilepsy. Causality therefore remains uncertain. Moreover, somnolence, fatigue, sedation, and objective hypersomnolence are frequently conflated despite representing distinct constructs. The Epworth Sleepiness Scale cannot determine whether impaired alertness reflects medication exposure, insufficient sleep, OSA, depression, or nocturnal seizures; objective vigilance testing has rarely been incorporated.
Overall, ASM effects on sleep are heterogeneous and generally modest relative to the interacting influences of epilepsy, seizure control, psychiatric comorbidity, and sleep disorders. Medication selection should therefore not be based on polysomnographic effects alone, but should integrate the patient’s seizure phenotype, baseline sleep complaint, respiratory risk, psychiatric profile, age, treatment burden, and vulnerability to daytime impairment. Definitive comparative rankings await adequately powered, active-comparator studies combining longitudinal polysomnography, validated patient-reported outcomes, objective vigilance testing, and rigorous control of seizure response and comorbidity.
Diagnostic Technologies at the Sleep–Epilepsy Interface
Diagnostic assessment at the sleep–epilepsy interface requires a modality selected according to the clinical question. Routine and sleep EEG primarily improve detection of interictal epileptiform discharges; prolonged video-EEG establishes electroclinical correlation; video-polysomnography with an extended EEG montage distinguishes nocturnal seizures from primary sleep disorders; ambulatory video-EEG captures habitual events in the home environment; and actigraphy characterizes longitudinal sleep–wake behavior. No single investigation addresses all these objectives.
Routine and Sleep-Deprived EEG
Sleep increases the probability of detecting epileptiform abnormalities, particularly after an unrevealing routine EEG. However, the incremental value of sleep deprivation is variable. A recent systematic review found only a modest overall diagnostic benefit and substantial heterogeneity among protocols, populations, and reference standards, supporting selective rather than indiscriminate use [85]. Earlier prospective evidence likewise showed that repeat EEG incorporating partial sleep deprivation may yield additional diagnostic information, largely because epileptiform abnormalities are sometimes expressed only during sleep [86].
Prolonged Video-EEG and Video-Polysomnography
Prolonged video-EEG is essential when diagnosis depends on correlating paroxysmal behavior with cerebral electrical activity. It is particularly valuable for recurrent nocturnal events, although absence of a clear scalp ictal pattern does not exclude epilepsy when seizure onset is deep or obscured by movement artifact.
When the differential diagnosis includes parasomnia, sleep-disordered breathing, or another primary sleep disorder, video-polysomnography with comprehensive EEG provides a more complete assessment. This approach combines sleep staging, respiratory and electromyographic variables, synchronized behavior, and expanded EEG recording. In children with epilepsy or unexplained nocturnal spells, combined video-EEG and polysomnography frequently clarified whether events were epileptic, behavioral, respiratory, or parasomnic and directly informed clinical management [87]. Reviews of nocturnal seizures and parasomnias similarly emphasize video-polysomnography with comprehensive EEG when clinical history alone remains inconclusive [88].
Ambulatory and Home Video-EEG
Ambulatory video-EEG extends recording into the habitual environment, increasing the likelihood of capturing natural sleep and typical events. Analysis of a large home-monitoring dataset suggested that at least 48 hours is generally appropriate for children and approximately 72 hours for adults and older patients, although infrequent events may require longer monitoring [89]. More recent evidence confirmed that prolonged ambulatory video-EEG frequently captures clinically relevant events and resolves the referring diagnostic question [90].
Actigraphy
Actigraphy provides objective longitudinal estimates of sleep timing, duration, awakenings, and day-to-day variability but cannot diagnose epilepsy or reliably define sleep stages. In children with refractory epilepsy, actigraphic sleep-wake measures correlated strongly with simultaneous continuous video-EEG, supporting its use for habitual sleep assessment while confirming its complementary, not substitutive, role [91].
Overall, diagnostic accuracy is greatest when technology is matched to the unresolved clinical problem. Electrophysiological methods establish epilepsy, polysomnographic integration identifies competing sleep diagnoses, ambulatory recording improves ecological validity, and actigraphy quantifies longitudinal sleep behavior.
Digital Technologies and Seizure Prediction
Digital health technologies are progressively shifting epilepsy assessment from brief, laboratory-based observation toward continuous monitoring in the patient’s habitual environment. The principal approaches include wearable and ambulatory EEG, multimodal physiological sensors, smartphone-supported reporting, and machine-learning models developed for seizure detection and forecasting.
Wearable EEG systems enable prolonged electrophysiological recording beyond conventional inpatient monitoring. In a prospective study of focal epilepsy, a wearable EEG device proved feasible in both hospital and home settings, provided clinically useful long-term recordings, and achieved generally favorable user acceptance [20]. Multimodal devices integrating movement and autonomic signals have shown particular value for nocturnal major motor seizures. A prospective multicenter home trial in children demonstrated high sensitivity for clinically urgent nocturnal seizures and reduced caregiver stress, although performance cannot be generalized to focal seizures without prominent motor manifestations [21].
Sleep-derived measurements are increasingly being investigated as digital biomarkers of seizure susceptibility. Wearable monitoring identified lower nocturnal sleep efficiency before seizure days in an exploratory cohort [22]. Similarly, deviations in habitual bedtimes and wake times were associated with subsequent seizures in more participants than reduced sleep duration, suggesting that individualized sleep regularity may be more informative than sleep quantity alone [9].
Artificial intelligence has extended these observations from retrospective association to individualized risk estimation. A prospective study generated daily forecasts from wearable-derived sleep characteristics in 78 hospitalized patients, demonstrating that sleep variables can contribute to machine-learning models of seizure probability [92]. Other wearable-based forecasting studies have shown that circadian and multiday physiological cycles, particularly heart-rate rhythms, may stratify hourly and daily seizure risk above chance in selected patients [93].
Overall, digital technologies substantially expand the temporal and ecological resolution of sleep and seizure monitoring. However, most studies remain small, highly selected, and device-specific. External validation, standardized performance metrics, transparent algorithms, acceptable false-alarm rates, and demonstration of improved clinical outcomes remain essential before these systems can be incorporated routinely into epilepsy care.
Sleep-Directed Therapeutic Interventions
Sleep-directed treatment in epilepsy has focused principally on managing comorbid obstructive sleep apnea (OSA), improving insomnia and maladaptive sleep behaviors, stabilizing sleep timing, and evaluating melatonin, exercise, and chronotherapeutic strategies. Across these domains, improvement in sleep-related outcomes is achievable; however, evidence that sleep interventions independently reduce seizure frequency remains considerably less definitive.
Treatment of Obstructive Sleep Apnea
OSA treatment has the most established physiological rationale and the strongest objective evidence. In a randomized pilot trial, adults with epilepsy and polysomnographically confirmed OSA were assigned to therapeutic or sham continuous positive airway pressure (CPAP). The study demonstrated feasibility but was underpowered to establish a definitive reduction in seizure frequency [56]. Earlier prospective observational evidence suggested improved seizure control after OSA treatment, but the small uncontrolled cohort precluded firm causal conclusions [56]. A subsequent meta-analysis likewise found better seizure control among CPAP-treated than untreated patients, although this estimate was derived predominantly from observational studies [11]. Thus, OSA should be systematically identified and treated for its established respiratory, cardiovascular, cognitive, and daytime-functioning consequences, while its independent antiseizure benefit remains probable but incompletely proven.
Cognitive Behavioral and Educational Interventions
Epilepsy-specific evidence for cognitive behavioral therapy for insomnia (CBT-I) is encouraging. A randomized trial of an app-delivered CBT-I program in 320 adults with epilepsy and moderate-to-severe insomnia demonstrated sustained improvements in insomnia severity, sleep quality, sleep hygiene behavior, sleep-onset latency, anxiety, depression, and quality of life compared with patient education [94]. A smaller group-based pilot study also suggested that structured cognitive behavioral treatment is feasible and may improve sleep-related and quality-of-life outcomes, although its limited sample restricted inference [35].
Behavioral-educational interventions have also shown objective benefit in children. In a randomized trial involving 100 toddlers and preschool-aged children with epilepsy, a clinic-based intervention improved actigraphically measured sleep efficiency and nocturnal sleep duration over 12 months [95]. These findings support incorporating structured sleep education, regular routines, stimulus control, and caregiver guidance into epilepsy care, although seizure outcomes were not established as direct consequences of improved sleep.
Sleep Regularity and Hygiene
Sleep hygiene alone has rarely been evaluated as an isolated treatment. Nevertheless, longitudinal wearable monitoring indicates that deviations from habitual bedtime and wake time may be more consistently associated with seizure occurrence than reduced sleep duration, supporting emphasis on regularity rather than generic recommendations to “sleep more” [9]. Sleep hygiene should therefore be regarded as a low-risk supportive strategy, but not as an evidence-based substitute for diagnosing and treating insomnia, OSA, or other specific sleep disorders.
Melatonin
Evidence for melatonin remains heterogeneous. The Cochrane review concluded that available trials were methodologically insufficient to determine whether melatonin reduced seizures or improved quality of life [96]. A more recent systematic review reported improvements in sleep latency and seizure severity with adjunctive melatonin but emphasized inconsistent outcome assessment, limited safety reporting, and inadequate long-term follow-up [16]. Melatonin may therefore be considered selectively for sleep-onset or circadian difficulties, particularly in pediatric practice, but current evidence does not justify presenting it as an established antiseizure therapy.
Exercise and Chronotherapy
A recent systematic review found that the effect of exercise on sleep in epilepsy remains uncertain because interventions and outcomes were heterogeneous, despite broader evidence supporting benefits for fitness, mood, cognition, and quality of life [37]. Exercise should be encouraged for general health, but its sleep-specific efficacy requires better-designed trials.
Chronotherapy seeks to align antiseizure-medication exposure with reproducible periods of heightened seizure susceptibility. Small observational pediatric studies reported improved control of nocturnal or early-morning seizures after shifting a greater proportion of medication to the evening [75,76]. However, the absence of adequately powered randomized trials, together with pharmacokinetic and tolerability considerations, currently confines chronotherapy to carefully selected patients with strongly time-locked seizure patterns.
Overall, treatment of a defined comorbid sleep disorder, particularly OSA or insomnia, has the clearest clinical justification. Behavioral interventions improve subjective and, in some studies, objective sleep outcomes; melatonin, exercise, and chronotherapy remain promising but incompletely validated. Future multicenter trials should prespecify both sleep and seizure outcomes, incorporate objective monitoring, and determine whether sustained sleep improvement translates into meaningful neurological and patient-reported benefits.
Epilepsy Surgery, Neuromodulation, and Sleep
Evidence addressing sleep outcomes after epilepsy surgery or neuromodulation remains substantially less developed than evidence addressing seizure control. Most studies have examined resective surgery for temporal lobe epilepsy or the respiratory consequences of vagus nerve stimulation (VNS), whereas sleep architecture and daytime vigilance have rarely been prespecified outcomes in trials of deep brain stimulation (DBS) or responsive neurostimulation (RNS).
Resective Epilepsy Surgery
Prospective polysomnographic studies indicate that sleep abnormalities may persist despite successful resective surgery. In patients with drug-resistant temporal lobe epilepsy associated with hippocampal sclerosis, preoperative sleep was characterized by increased lighter NREM stages, reduced slow-wave sleep, and lower sleep efficiency among those receiving more extensive polytherapy. Short-term postoperative reassessment did not demonstrate consistent improvement in subjective sleep quality, daytime sleepiness, or principal polysomnographic measures [97]. A subsequent prospective controlled study documented modifications in selected indices of sleep macrostructure and cyclic alternating pattern after hippocampal surgery, but the small cohort and heterogeneity of postoperative outcomes precluded the conclusion that seizure freedom reliably normalizes sleep physiology [98]. These findings support dedicated postoperative sleep reassessment rather than assuming that sleep improves in parallel with seizure control [97].
Vagus Nerve Stimulation
VNS has the clearest treatment-specific interaction with sleep, principally through sleep-disordered breathing. A prospective study demonstrated improved seizure control after implantation but a significant increase in obstructive respiratory events, supporting careful surveillance for newly developed or aggravated obstructive sleep apnea [99]. A systematic review similarly found inconsistent effects on subjective sleep quality but a more reproducible association with worsening apnea-related indices; age, stimulation parameters, and baseline respiratory vulnerability appeared to modify risk [100]. VNS should therefore not be withheld when clinically indicated, but patients with snoring, witnessed apneas, obesity, or excessive daytime sleepiness warrant targeted respiratory assessment and individualized programming.
Deep Brain and Responsive Neurostimulation
Direct sleep evidence for DBS remains limited and highly parameter-dependent. Recent intracranial observations suggest that high-frequency centromedian stimulation during sleep may disrupt sleep architecture, whereas lower-frequency nocturnal stimulation may improve both sleep organization and seizure control; these findings require confirmation in larger controlled cohorts [101]. For RNS, small polysomnographic studies indicate that responsive stimulation does not substantially disrupt sleep, but available samples are insufficient to establish broader effects on sleep disorders or daytime vigilance [102].
Overall, resective surgery does not consistently normalize sleep, VNS may provoke or worsen sleep-disordered breathing, and the direct sleep effects of DBS and RNS remain insufficiently characterized.
Patient-Reported Outcomes, Health-Related Quality of Life, and Caregiver Burden
Patient-reported outcomes revealed a substantial sleep-related burden that was not adequately represented by seizure frequency alone. Across the included studies, the most frequently assessed domains were subjective sleep quality, insomnia, excessive daytime sleepiness, fatigue, mood symptoms, daily functioning, and epilepsy-specific health-related quality of life. However, considerable overlap among sleep disturbance, depression, anxiety, medication burden, and seizure severity limited attribution of independent effects.
In adults, a systematic review and meta-analysis of 25 studies involving 8,196 participants demonstrated significantly poorer subjective sleep quality and greater daytime sleepiness in people with epilepsy than in controls, although between-study heterogeneity was considerable [29]. Insomnia appeared particularly consequential: a prospective study associated greater insomnia severity with continuing seizures during follow-up and poorer quality of life, while appropriately cautioning that causality could not be established [40]. Excessive daytime sleepiness and fatigue should nevertheless be distinguished. A systematic review suggested that sleepiness may more often reflect unrecognized sleep disorders than epilepsy-specific factors [41]; whereas a separate meta-analysis identified depression, impaired sleep, and poor sleep quality as principal correlates of fatigue [103].
Pediatric evidence demonstrated similarly important associations. Children and adolescents with epilepsy experienced shorter sleep, lower sleep efficiency, more nocturnal awakenings, parasomnias, and sleep-disordered breathing than healthy controls [32]. More recent cohort evidence linked poorer sleep, particularly in children with nocturnal seizures, to reduced health-related quality of life [104]. Proxy reporting remains indispensable in younger children and those with intellectual disability, although caregiver distress may influence perceived symptom severity.
The burden extends beyond the patient. A systematic review and meta-analysis confirmed substantial sleep disturbance among parents of children and adolescents with epilepsy [34]. In Dravet syndrome, caregiver sleep quality was severely impaired and correlated with anxiety, depressive symptoms, caregiver burden, and sleep disturbance in the affected individual [105].
Overall, sleep-related patient- and caregiver-reported outcomes constitute essential dimensions of epilepsy burden. Their systematic assessment should complement seizure outcomes, while future studies should integrate validated questionnaires with objective sleep measures and longitudinal designs capable of clarifying causal pathways.
Implementation Barriers and Facilitators
Despite the high prevalence and clinical impact of sleep disorders in epilepsy, their systematic assessment remains insufficiently integrated into routine clinical practice. The principal barriers include under-recognition of sleep complaints, inconsistent screening, fragmented referral pathways, limited access to specialized sleep investigations, and inadequate incorporation of sleep outcomes into longitudinal epilepsy care.
Sleep disorders frequently remain undiagnosed because symptoms such as fatigue, impaired concentration, daytime sleepiness, and nocturnal awakenings are often attributed to epilepsy itself, antiseizure medications, or psychiatric comorbidity rather than investigated as potentially treatable conditions. Recognizing this gap, a joint consensus from the European Academy of Neurology (EAN), European Sleep Research Society (ESRS), and ILAE-Europe proposed standardized diagnostic pathways for patients with sleep-related epilepsies and comorbid sleep disorders, emphasizing structured clinical history, appropriate use of EEG, video-EEG, polysomnography, and multidisciplinary collaboration between epileptologists and sleep physicians [106]. These recommendations provide an important framework for translating available diagnostic technologies into routine clinical practice.
Structured screening represents one of the most practical implementation strategies. A quality-improvement initiative demonstrated that introducing the STOP-BANG questionnaire into an epilepsy clinic increased OSA screening from 3.3% to 41.6%, substantially improving identification and referral of patients at high risk for obstructive sleep apnea [107]. However, screening alone is insufficient; its clinical value depends on access to confirmatory sleep testing, treatment initiation, and longitudinal follow-up.
Service-level barriers remain considerable. Access to polysomnography, specialized sleep laboratories, and integrated neurophysiology services varies widely across healthcare systems, particularly in resource-limited settings. Telemedicine has emerged as an important facilitator for screening, follow-up, behavioral interventions, and review of home-recorded events. The International League Against Epilepsy (ILAE) Telemedicine Task Force concluded that telemedicine can substantially improve continuity of epilepsy care, although physiological investigations and complex nocturnal events still require in-person assessment [108]. During the COVID-19 pandemic, a nationwide Italian survey further demonstrated that poor sleep quality independently predicted seizure worsening, highlighting the importance of maintaining clinical follow-up through remote care when conventional services are disrupted [109].
Overall, the available evidence indicates that implementation gaps are largely organizational rather than scientific. Routine multidomain sleep screening, standardized referral pathways, multidisciplinary epilepsy-sleep collaboration, and judicious integration of telemedicine represent the principal facilitators for improving sleep assessment and management. Future implementation research should evaluate not only diagnostic yield but also patient-centered outcomes, cost-effectiveness, and long-term sustainability within diverse healthcare systems.
Evidence Gaps and Priorities for Future Research
Although the relationship between sleep and epilepsy is firmly established, the evidence remains dominated by cross-sectional, single-center, and observational studies. Consequently, temporal direction and causality are often uncertain: sleep disruption may increase seizure susceptibility, whereas seizures, interictal activity, antiseizure medications, psychiatric comorbidity, and fear of nocturnal events may themselves impair sleep. Longitudinal studies using repeated objective and patient-reported measures are therefore essential. Wearable-based monitoring has already shown that deviations from habitual bedtimes and wake times may be associated with seizure occurrence more consistently than sleep duration, but these relationships are strongly individualized and do not yet support universal clinical thresholds [9].
Outcome heterogeneity represents a second major limitation. Studies employ disparate questionnaires, diagnostic thresholds, sleep diaries, actigraphy, polysomnography, and proprietary wearable metrics, restricting comparability and quantitative synthesis. The recently developed international standard outcome set for epilepsy appropriately includes sleep quality among essential non-seizure outcomes and provides a foundation for harmonized assessment across clinical care and research [110]. Future investigations should combine standardized patient-reported outcomes with objective sleep measures, verified seizure timing, medication exposure, mood, cognition, and functional status.
Objective research should also become more ecologically valid. A systematic review of adult polysomnographic studies identified substantial heterogeneity in epilepsy classification, medication exposure, recording protocols, and control of peri-recording seizures, preventing definition of a uniform sleep phenotype [30]. Laboratory polysomnography should therefore be complemented by longitudinal home EEG, actigraphy, wearable sensors, and electronic diaries capable of capturing night-to-night variability.
Interventional evidence remains comparatively sparse. Trials of continuous positive airway pressure, cognitive behavioral therapy, melatonin, exercise, sleep education, and chronotherapy are generally small or methodologically heterogeneous. Similarly, medication-specific sleep effects are incompletely characterized; available syntheses demonstrate substantial differences among antiseizure medications but emphasize uneven evidence, polytherapy confounding, and limited active-comparator data [13,78].
Important population gaps also persist. Pediatric evidence confirms shorter sleep duration, reduced efficiency, and greater sleep disruption, yet remains heterogeneous across developmental stages and epilepsy syndromes [32]. Older adults, individuals with intellectual disability, developmental and epileptic encephalopathies, and populations from low-resource settings remain insufficiently represented.
Finally, wearable technologies and artificial intelligence require external validation beyond major motor seizures. The ILAE-IFCN guideline supports automated detection of generalized tonic-clonic and focal-to-bilateral tonic-clonic seizures but identifies weaker evidence for other seizure types and for meaningful clinical benefit [111]. Future implementation research should evaluate screening uptake, referral completion, cost-effectiveness, false-alarm burden, acceptability, equity, and integration into clinical workflows. Standardized multidisciplinary pathways already exist, but their real-world adoption and impact remain insufficiently studied [61].
Discussion
This scoping review demonstrates that sleep is not merely a contextual modifier of epilepsy but a fundamental biological and clinical dimension of the disorder. Across the included literature, sleep influenced neuronal synchronization, seizure timing, diagnostic yield, treatment tolerability, functional outcomes, and quality of life. Conversely, epilepsy disrupted sleep architecture, continuity, respiratory function, circadian organization, and daytime vigilance. Taken together, these findings support a genuinely bidirectional model in which sleep and epilepsy interact continuously across the disease course rather than through isolated episodes of sleep deprivation or nocturnal seizures.
The mechanistic evidence was strongest for the modulation of established epileptic networks. NREM sleep facilitated interictal and ictal activity through synchronized cortical and thalamocortical oscillations, whereas REM sleep generally restricted propagation [42,43,44,45,46,47,48,49]. Sleep microstructure, particularly cyclic alternating pattern dynamics, further refined this state-dependent model [45,46]. Long-term recordings expanded the temporal framework beyond individual sleep stages by demonstrating patient-specific circadian and multidien cycles of seizure susceptibility [28,38,64,71,72,73,74]. These observations challenge the traditional perception of seizures as temporally random events and provide a physiological foundation for individualized monitoring and chronotherapeutic strategies. However, direct human evidence that disturbed sleep independently initiates epileptogenesis remains limited; the available literature more convincingly demonstrates modulation of existing epileptic networks than disease causation.
Clinically, the sleep phenotype of epilepsy was heterogeneous but consistently burdensome. Poor subjective sleep quality, reduced efficiency, REM abnormalities, insomnia, excessive daytime sleepiness, obstructive sleep apnoea, restless legs syndrome, and parasomnias were reported across age groups and epilepsy classifications [11,29,30,31,32,40,41,54,55,56,57,58]. No single architecture pattern characterized all patients, probably because sleep reflects interacting effects of epilepsy syndrome, seizure burden, nocturnal events, psychiatric comorbidity, treatment exposure, age, and coexisting sleep disorders. This heterogeneity has practical implications: fatigue, cognitive slowing, nocturnal awakening, and daytime somnolence should not automatically be attributed to epilepsy or antiseizure medication. They may instead indicate a potentially treatable sleep disorder.
The pediatric findings reinforce the need for a lifespan perspective. In children, sleep disturbance extends beyond the affected individual to cognition, behaviour, neurodevelopment, parental sleep, and family functioning [32,33,34]. Older adults, by contrast, remain insufficiently represented despite their susceptibility to insomnia, sleep-disordered breathing, cognitive impairment, polypharmacy, and medication-related sedation. Future research should therefore use developmentally appropriate designs rather than treating pediatric and adult epilepsy as homogeneous populations.
Therapeutic evidence was less mature than mechanistic and observational research. Antiseizure medications showed heterogeneous and drug-specific effects on sleep, but comparative interpretation was limited by small studies, polytherapy, and inconsistent objective assessment [13,78,79,80,81,82,83,84]. Treatment of established sleep disorders had the clearest rationale. Continuous positive airway pressure, behavioural interventions, and sleep education improved respiratory or sleep-related outcomes, although an independent effect on seizure control remains uncertain [11,35,56,94,95,96]. Melatonin, exercise, chronotherapy, epilepsy surgery, and neuromodulation appeared promising in selected contexts but lacked sufficiently robust and consistent evidence for broad recommendations [16,37,75,76,77,97,98,99,100,101,102]. Sleep outcomes should therefore be incorporated prospectively into future treatment trials rather than considered secondary or incidental endpoints.
Diagnostic technologies have evolved from brief laboratory recordings towards longitudinal, ecologically valid assessment. Video-EEG and polysomnography remain essential when electroclinical correlation or differentiation from parasomnias and sleep-disordered breathing is required [59,60,61,85,86,87,88]. Ambulatory EEG, actigraphy, wearable sensors, and multimodal monitoring extend observation into habitual environments and facilitate characterization of sleep regularity, nocturnal events, and long-term seizure cycles [20,21,22,89,90,91,92,93]. Nevertheless, evidence of technical performance currently exceeds evidence of clinical utility. External validation, standardized performance measures, false-alarm burden, interoperability, and demonstration of patient-important benefit remain essential before digital monitoring and seizure forecasting can be integrated routinely into care.
A central translational finding was the persistent failure to embed sleep systematically within epilepsy pathways. Sleep-related patient-reported outcomes captured disease burden not reflected by seizure frequency alone, including fatigue, mood disturbance, impaired quality of life, and caregiver burden [29,34,40,103,104,105]. Yet screening, referral, and access to specialized assessment remained inconsistent [61,106,107,108,109]. The principal implementation need is therefore not another demonstration that sleep matters, but the development of coherent clinical pathways linking validated screening, appropriate diagnostic testing, disorder-specific treatment, and longitudinal follow-up.
Future research should prioritize prospective multicentre cohorts integrating objective sleep assessment, patient-reported outcomes, verified seizure timing, medication exposure, psychiatric comorbidity, and environmental factors. Standardized outcome frameworks should include sleep quality, daytime vigilance, cognition, mood, caregiver burden, and health-related quality of life alongside seizure outcomes [110]. Interventional trials should target defined phenotypes—such as chronic insomnia, polysomnographically confirmed obstructive sleep apnoea, or reproducible circadian instability—rather than heterogeneous populations with nonspecific sleep complaints. Wearable and artificial-intelligence applications should be evaluated prospectively for clinical decision-making, safety, cost-effectiveness, equity, and quality-of-life benefit rather than technical accuracy alone [111].
Limitations
This review has several limitations. The breadth of the topic required inclusion of highly heterogeneous populations, designs, assessment methods, and outcomes, limiting direct comparison and precluding quantitative synthesis. Consistent with scoping-review methodology, no formal risk-of-bias assessment was performed; inclusion within the evidence map therefore does not imply equivalent methodological quality. Restriction to English-language, peer-reviewed publications may have introduced language and publication bias and may have underrepresented low-resource settings. Several conclusions relied predominantly on cross-sectional or single-centre studies, while emerging fields such as wearable monitoring, artificial intelligence, and neuromodulation were represented by small and selected cohorts. Finally, the rapid evolution of digital technologies means that some findings may require reassessment as larger validation and implementation studies become available.
Overall, the evidence supports integrating sleep into the routine conceptualization and management of epilepsy. The next challenge is to determine whether systematic sleep phenotyping and targeted intervention can modify not only sleep-related symptoms, but also neurological, functional, and patient-centred outcomes.
Conclusions
Sleep is an integral component of epilepsy biology and clinical care, influencing neuronal network dynamics, seizure susceptibility, diagnostic accuracy, treatment response, and long-term patient outcomes. The available evidence supports systematic assessment of sleep disturbances as a core element of epilepsy management rather than an adjunctive consideration. Emerging digital technologies, wearable monitoring, and artificial intelligence offer unprecedented opportunities for continuous sleep and seizure phenotyping, although robust validation and demonstration of clinical benefit remain essential. Future research should prioritize standardized outcome measures, prospective multicentre studies, and disorder-specific interventional trials to determine whether optimizing sleep can improve seizure control, cognition, quality of life, and other patient-centred outcomes. Integrating sleep medicine into precision epilepsy care represents one of the most promising and achievable directions for advancing both research and clinical practice.
Author Contributions
All authors have read and approved the final version to be published and agreed to be accountable for all aspects of the work.
Data Availability
All data is publicly available on the cited references in PubMed.
Acknowledgments
The authors wish to express their sincere gratitude to the Visionary International Board for Research and Analgesia (VIBRA), part of the Fondazione Paolo Procacci, for the valuable scientific discussions that contributed to the development of this article. The Fondazione Paolo Procacci is committed to advancing scientific research, fostering international collaboration and promoting academic excellence in pain medicine and related fields.
Conflicts of Interest
The authors have no conflict of interest to declare.
Conceptualization and study design
Giustino Varrassi.
Methodology
Giustino Varrassi, Matteo Luigi Giuseppe Leoni.
Literature review and evidence synthesis
Y Van Tran, Phong Van Pham, and Jessica Nicolas.
Writing – original draft
Giustino Varrassi, Y Van Tran, Phong Van Pham, Matteo Luigi Giuseppe Leoni.
Writing – review and editing
Giacomo Farì, Matteo Luigi Giuseppe Leoni, Jessica Nicolas, Giustino Varrassi.
Supervision
Giustino Varrassi.
Compliance: with Ethical Standards
is not applicable.
Human: and Animal Rights and Informed Consent
All reported studies/experiments with human or animal subjects performed by the authors have been previously published and complied with all applicable ethical standards (including the Helsinki declaration and its amendments, institutional/national research committee standards, and international/national/institutional guidelines).
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