Insufficient Sleep, Electroencephalogram Activation, and Seizure Risk: Re-Evaluating the Evidence

A polysomnographic study of slow-wave sleep loss in elderly patients with epilepsy

Objective

The primary objective is to explore what causes slow-wave sleep loss in elderly patients with epilepsy. The secondary objective is to identify the PSG characteristics in elderly patients with epilepsy. The clinical demographics, sleep architecture, sleep-related events, and interictal epileptiform discharges are to be evaluated in the objectives.

Methods

The video electroencephalography (VEEG) and polysomnogram (PSG) data from 44 elderly patients with epilepsy and 52 elderly patients with sleep disorders but without definite central nervous system diseases were analysed. This was a case-control study. The differences in the PSG sleep architecture parameters (total sleep time (TST), sleep efficiency, wake after sleep onset, etc.) and sleep-related events (apnea hypopnea index, oxygen desaturation index (ODI), periodic limb movement index, etc.) between the epilepsy and control groups. As Additionally, these parameters were assessed within the elderly patients with epilepsy, comparing the slow-wave sleep existence and slow-wave sleep loss groups, using VEEG and PSG.

Results

The epileptic group exhibited significantly lower TST (343.477 ± 96.3046min vs 389.115 ± 61.5727min, p < 0.05), rapid eye movement (%) (13.011 ± 7.5384 vs 16.992 ± 6.7025, p < 0.05), non-rapid eye movement stage 3 (%) (1.35[0,7.225] vs 3.65[0.425,13.75], p < 0.05), and sleep efficiency (%) (69.482 ± 14.1771% vs 77.242 ± 10.6171%, p < 0.05). Conversely, the ODI (25.6[9.825,51.775] events/hour vs 16.85[5.3,30.425] events/hour, p < 0.05) and spontaneous arousal index (4.0455[2.1805,6.9609] events/hour vs 2.9709[1.4747,5.0554] events/hour, p < 0.05) were significantly higher in elderly patients with epilepsy. The prevalence of obstructive sleep apnea-hypopnea syndrome (OSAHS) was significantly higher in the slow-wave sleep loss group than in the slow-wave sleep existence group (100% vs 77.8%, p < 0.05). The incidence of slow-wave sleep loss was lower in patients with epilepsy aged between 75 and 85 years compared to those aged between 65 and 75 years.

Conclusion

Elderly patients with epilepsy exhibit higher levels of ODI and spontaneous arousal index. Our findings indicate that OSAHS could be a contributing factor to slow-wave sleep loss in this population. The incidence of slow-wave sleep loss was lower in patients aged above 75 years among elderly patients with epilepsy.

Epilepsy monitoring unit practices and safety among NAEC epilepsy centers: A census survey

OBJECTIVE

An epilepsy monitoring unit (EMU) is a specialized unit designed for capturing and characterizing seizures and other paroxysmal events with continuous video electroencephalography (vEEG). Nearly 260 epilepsy centers in the United States are accredited by the National Association of Epilepsy Centers (NAEC) based on adherence to specific clinical standards to improve epilepsy care, safety, and quality. This study examines EMU staffing, safety practices, and reported outcomes.

METHOD

We analyzed NAEC annual report data and results from a supplemental survey specific to EMU practices reported in 2019 from 341 pediatric or adult center directors. Data on staffing, resources, safety practices and complications were collated with epilepsy center characteristics. We summarized using frequency (percentage) for categorical variables and median (inter-quartile range) for continuous variables. We used chi-square or Fisher's exact tests to compare staff responsibilities.

RESULTS

The supplemental survey response rate was 100%. Spell classification (39%) and phase 1 testing (28%) were the most common goals of the 91,069 reported admissions. The goal ratio of EEG technologist to beds of 1:4 was the most common during the day (68%) and off-hours (43%). Compared to residents and fellows, advanced practice providers served more roles in the EMU at level 3 or pediatric-only centers. Status epilepticus (SE) was the most common reported complication (1.6% of admissions), while cardiac arrest occurred in 0.1% of admissions.

SIGNIFICANCE

EMU staffing and safety practices vary across US epilepsy centers. Reported complications in EMUs are rare but could be further reduced, such as with more effective treatment or prevention of SE. These findings have potential implications for improving EMU safety and quality care.

Seizure forecasting: Bifurcations in the long and winding road

To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.

The Relationship Between Sleep, Epilepsy, and Development: a Review

PURPOSE OF REVIEW

To review the relationship between sleep, neurodevelopment, and epilepsy and potential underlying physiological mechanisms.

RECENT FINDINGS

Recent studies have advanced our understanding of the role of sleep in early brain development and epilepsy. Epileptogenesis has been proposed to occur when there is a failure of normal adaptive processes of synaptic and homeostatic plasticity. This sleep-dependent transformation may explain the cognitive impairment seen in epilepsy, especially when occurring early in life. The glymphatic system, a recently discovered waste clearance system of the central nervous system, has been described as a potential mechanism underlying the relationship between sleep and seizures and may account for the common association between sleep deprivation and increased seizure risk. Epilepsy and associated sleep disturbances can critically affect brain development and neurocognition. Here we highlight recent findings on this topic and emphasize the importance of screening for sleep concerns in people with epilepsy.

Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration

Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Sixty subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n = 37 921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n = 34), sleep features were analysed for significant changes prior to seizure days. A total of 4956 reported seizures (mean = 83, standard deviation = 130) and 30 485 recorded sleep nights (mean = 508, standard deviation = 445) were included in the study. There was a trend for participants with epilepsy to sleep longer than the general population, although this difference was not significant. Just 5 of 34 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 14 of 34 subjects showed significant differences between their sleep onset (bed) and/or offset (wake) times before seizure occurrence. In contrast to previous studies, the current study found undersleeping was associated with a marginal 2% decrease in seizure risk in the following 48 h (P < 0.01). Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48 h. Overall, the presented results demonstrated that day-to-day changes in sleep duration had a minimal effect on reported seizures, while patient-specific changes in bed and wake times were more important for identifying seizure risk the following day. Nocturnal seizures were the only factor that significantly increased the risk of seizures in the following 48 h on a group level. Wearables can be used to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.

Melatonin usage in children and young adults, a registry-based cohort study

Sleep disorder is common in children and adolescents, particularly in those with attention deficit hyperactivity disorder (ADHD) or autism spectrum disorder (ASD). While non-pharmacological treatment is first line, occasionally an add-on of an oral drug is needed. The endogenous hormone melatonin is increasingly used for sleep disorders in children and adolescents. In this registry-based cohort study we follow dispensation of melatonin in young individuals, 0-25 years of age, in Stockholm, Sweden during 2016-2019. In all 9980 individuals, were dispensed melatonin in 2016 and followed for 3 years. Child psychiatrist was the most common prescribing specialty, 55% of all prescriptions. Only 20% had a recorded diagnosis of sleep disorder. The majority, 65% had a neuro psychiatric diagnose. Half of the individuals had at least 4 prescribed drugs dispensed during the follow-up. Almost half of our cohort were dispensed melatonin during the entire study period and doses and volumes of drug dispensed increased by 50 and 100%, respectively. Continuous medication was most common among children 6-12 years, where 7 out of 10 individuals were still adherent after three years. As long-term safety data is lacking, we find this concerning, and this illustrates the need of long-term follow-up of melatonin use in children and young individuals.

Sleep and epilepsy: A snapshot of knowledge and future research lines

Sleep and epilepsy have a reciprocal relationship, and have been recognized as bedfellows since antiquity. However, research on this topic has made a big step forward only in recent years. In this narrative review we summarize the most stimulating discoveries and insights reached by the "European school." In particular, different aspects concerning the sleep-epilepsy interactions are analysed: (a) the effects of sleep on epilepsy; (b) the effects of epilepsy on sleep structure; (c) the relationship between epilepsy, sleep and epileptogenesis; (d) the impact of epileptic activity during sleep on cognition; (e) the relationship between epilepsy and the circadian rhythm; (f) the history and features of sleep hypermotor epilepsy and its differential diagnosis; (g) the relationship between epilepsy and sleep disorders.

Minimum standards for inpatient long-term video-EEG monitoring: A clinical practice guideline of the international league against epilepsy and international federation of clinical neurophysiology

The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events (see Table S1). For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and establish its clinical utility.

Minimum standards for inpatient long-term video-electroencephalographic monitoring: A clinical practice guideline of the International League Against Epilepsy and International Federation of Clinical Neurophysiology

The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events. For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and to establish its clinical utility.

Sleep Deprivation Exacerbates Seizures and Diminishes GABAergic Tonic Inhibition

Patients with epilepsy report that sleep deprivation is a common trigger for breakthrough seizures. The basic mechanism of this phenomenon is unknown. In the Kv1.1-/- mouse model of epilepsy, daily sleep deprivation indeed exacerbated seizures though these effects were lost after the third day. Sleep deprivation also accelerated mortality in ~ 52% of Kv1.1-/- mice, not observed in controls. Voltage-clamp experiments on the day after recovery from sleep deprivation showed reductions in GABAergic tonic inhibition in dentate granule cells in epileptic Kv1.1-/- mice. Our results suggest that sleep deprivation is detrimental to seizures and survival, possibly due to reductions in GABAergic tonic inhibition. ANN NEUROL 2021;90:840-844.

Seizure likelihood varies with day-to-day variations in sleep duration in patients with refractory focal epilepsy: A longitudinal electroencephalography investigation

BACKGROUND

While the effects of prolonged sleep deprivation (≥24 h) on seizure occurrence has been thoroughly explored, little is known about the effects of day-to-day variations in the duration and quality of sleep on seizure probability. A better understanding of the interaction between sleep and seizures may help to improve seizure management.

METHODS

To explore how sleep and epileptic seizures are associated, we analysed continuous intracranial electroencephalography (EEG) recordings collected from 10 patients with refractory focal epilepsy undergoing ordinary life activities between 2010 and 2012 from three clinical centres (Austin Health, The Royal Melbourne Hospital, and St Vincent's Hospital of the Melbourne University Epilepsy Group). A total of 4340 days of sleep-wake data were analysed (average 434 days per patient). EEG data were sleep scored using a semi-automated machine learning approach into wake, stages one, two, and three non-rapid eye movement sleep, and rapid eye movement sleep categories.

FINDINGS

Seizure probability changes with day-to-day variations in sleep duration. Logistic regression models revealed that an increase in sleep duration, by 1·66 ± 0·52 h, lowered the odds of seizure by 27% in the following 48 h. Following a seizure, patients slept for longer durations and if a seizure occurred during sleep, then sleep quality was also reduced with increased time spent aroused from sleep and reduced rapid eye movement sleep.

INTERPRETATION

Our results suggest that day-to-day deviations from regular sleep duration correlates with changes in seizure probability. Sleeping longer, by 1·66 ± 0·52 h, may offer protective effects for patients with refractory focal epilepsy, reducing seizure risk. Furthermore, the occurrence of a seizure may disrupt sleep patterns by elongating sleep and, if the seizure occurs during sleep, reducing its quality.

Sleep, neuronal hyperexcitability, inflammation and neurodegeneration: Does early chronic short sleep trigger and is it the key to overcoming Alzheimer's disease?

Evidence links neuroinflammation to Alzheimer's disease (AD); however, its exact contribution to the onset and progression of the disease is poorly understood. Symptoms of AD can be seen as the tip of an iceberg, consisting of a neuropathological build-up in the brain of extracellular amyloid-β (Aβ) plaques and intraneuronal hyperphosphorylated aggregates of Tau (pTau), which are thought to stem from an imbalance between its production and clearance resulting in loss of synaptic health and dysfunctional cortical connectivity. The glymphatic drainage system, which is particularly active during sleep, plays a key role in the clearance of proteinopathies. Poor sleep can cause hyperexcitability and promote Aβ and tau pathology leading to systemic inflammation. The early neuronal hyperexcitability of γ-aminobutyric acid (GABA)-ergic inhibitory interneurons and impaired inhibitory control of cortical pyramidal neurons lie at the crossroads of excitatory/inhibitory imbalance and inflammation. We outline, with a prospective framework, a possible vicious spiral linking early chronic short sleep, neuronal hyperexcitability, inflammation and neurodegeneration. Understanding the early predictors of AD, through an integrative approach, may hold promise for reducing attrition in the late stages of neuroprotective drug development.

Cycles in epilepsy

Epilepsy is among the most dynamic disorders in neurology. A canonical view holds that seizures, the characteristic sign of epilepsy, occur at random, but, for centuries, humans have looked for patterns of temporal organization in seizure occurrence. Observations that seizures are cyclical date back to antiquity, but recent technological advances have, for the first time, enabled cycles of seizure occurrence to be quantitatively characterized with direct brain recordings. Chronic recordings of brain activity in humans and in animals have yielded converging evidence for the existence of cycles of epileptic brain activity that operate over diverse timescales: daily (circadian), multi-day (multidien) and yearly (circannual). Here, we review this evidence, synthesizing data from historical observational studies, modern implanted devices, electronic seizure diaries and laboratory-based animal neurophysiology. We discuss advances in our understanding of the mechanistic underpinnings of these cycles and highlight the knowledge gaps that remain. The potential clinical applications of a knowledge of cycles in epilepsy, including seizure forecasting and chronotherapy, are discussed in the context of the emerging concept of seizure risk. In essence, this Review addresses the broad question of why seizures occur when they occur.

Untangling a Web: Basic Mechanisms of the Complex Interactions Between Sleep, Circadian Rhythms, and Epilepsy

Seizures have sleep–wake and circadian patterns in various epilepsies and, in turn, disrupt sleep and circadian rhythms. The resultant sleep deprivation (SD) is an exacerbating factor for seizures that sets up a vicious cycle that can potentially lead to disease progression and even to epilepsy-related mortality. A variety of cellular or network electrophysiological changes and changes in expression of clock-controlled genes or other transcription factors underlie sleep–wake and circadian distribution of seizures, as well as the disruptions seen in both. A broad understanding of these mechanisms may help in designing better treatments to prevent SD-induced seizure exacerbation, disrupt the vicious cycle of disease progression, and reduce epilepsy-related mortality.

Under-sampling in epilepsy: Limitations of conventional EEG

The cyclical structure of epilepsy was recently (re)-discovered through years-long intracranial electroencephalography (EEG) obtained with implanted devices. In this review, we discuss how new revelations from chronic EEG relate to the practice and interpretation of conventional EEG. We argue for an electrographic definition of seizures and highlight the caveats of counting epileptiform discharges in EEG recordings of short duration. Limitations of conventional EEG have practical implications with regard to titrating anti-seizure medications and allowing patients to drive, and we propose that chronic monitoring of brain activity could greatly improve epilepsy care. An impending paradigm shift in epilepsy will involve using next-generation devices for chronic EEG to leverage known biomarkers of disease state.

Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast

OBJECTIVE

Most seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient-specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts.

METHODS

This study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0-2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location-specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective.

RESULTS

For the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature.

SIGNIFICANCE

Environmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.

A new era in electroencephalographic monitoring? Subscalp devices for ultra-long-term recordings

Inaccurate subjective seizure counting poses treatment and diagnostic challenges and thus suboptimal quality in epilepsy management. The limitations of existing hospital- and home-based monitoring solutions are motivating the development of minimally invasive, subscalp, implantable electroencephalography (EEG) systems with accompanying cloud-based software. This new generation of ultra-long-term brain monitoring systems is setting expectations for a sea change in the field of clinical epilepsy. From definitive diagnoses and reliable seizure logs to treatment optimization and presurgical seizure foci localization, the clinical need for continuous monitoring of brain electrophysiological activity in epilepsy patients is evident. This paper presents the converging solutions developed independently by researchers and organizations working at the forefront of next generation EEG monitoring. The immediate value of these devices is discussed as well as the potential drivers and hurdles to adoption. Additionally, this paper discusses what the expected value of ultra-long-term EEG data might be in the future with respect to alarms for especially focal seizures, seizure forecasting, and treatment personalization.

Cues for seizure timing

The cyclical organization of seizures in epilepsy has been described since antiquity. However, historical explanations for seizure cycles-based on celestial, hormonal, and environmental factors-have only recently become testable with the advent of chronic electroencephalography (cEEG) and modern statistical techniques. Here, factors purported over millennia to influence seizure timing are viewed through a contemporary lens. We discuss the emerging concept that seizures are organized over multiple timescales, each involving differential influences of external and endogenous rhythm generators. Leveraging large cEEG datasets and circular statistics appropriate for cyclical phenomena, we present new evidence for circadian (day-night), multidien (multi-day), and circannual (about-yearly) variation in seizure activity. Modulation of seizure timing by multiscale temporal variables has implications for diagnosis and therapy in clinical epilepsy. Uncovering the mechanistic basis for seizure cycles, particularly the factors that govern multidien periodicity, will be a major focus of future work.