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

More variable circadian rhythms in epilepsy captured by long-term heart rate recordings from wearable sensors

OBJECTIVE

The circadian rhythm synchronizes physiological and behavioral patterns with the 24-h light-dark cycle. Disruption to the circadian rhythm is linked to various health conditions, although optimal methods to describe these disruptions remain unclear. An emerging approach is to examine the intraindividual variability in measurable properties of the circadian rhythm over extended periods. Epileptic seizures are modulated by circadian rhythms, but the relevance of circadian rhythm disruption in epilepsy remains unexplored. Our study investigates intraindividual circadian variability in epilepsy and its relationship with seizures.

METHODS

We retrospectively analyzed >70 000 h of wearable smartwatch data (Fitbit) from 143 people with epilepsy (PWE) and 31 healthy controls. Circadian oscillations in heart rate time series were extracted, daily estimates of circadian period, acrophase, and amplitude properties were produced, and estimates of the intraindividual variability of these properties over an entire recording were calculated.

RESULTS

PWE exhibited greater intraindividual variability in period (76 vs. 57 min, d = .66, p < .001) and acrophase (64 vs. 48 min, d = .49, p = .004) compared to controls, but not in amplitude (2 beats per minute, d = -.15, p = .49). Variability in circadian properties showed no correlation with seizure frequency nor any differences between weeks with and without seizures.

SIGNIFICANCE

For the first time, we show that heart rate circadian rhythms are more variable in PWE, detectable via consumer wearable devices. However, no association with seizure frequency or occurrence was found, suggesting that this variability might be underpinned by the epilepsy etiology rather than being a seizure-driven effect.

Causal relationship between sleep traits and risk of Epilepsy: A Mendelian randomization study

BACKGROUND

Observational studies have identified a strong correlation between epilepsy and sleep traits, highlighting their interactive relationship. However, no studies have specifically examined the associations between sleep traits and epilepsy. In this context, we conducted a Mendelian Randomization (MR) investigation to explore the causal nature of these associations.

METHODS

We performed a two-sample Mendelian Randomization (MR) analysis model to genetically predict the causal effects of morning chronotype on epilepsy. Five MR analysis methods were conducted to analyze the final results. The inverse-variance weighted (IVW) method was used as the primary outcome. The other MR analysis methods (MR-Egger; Weighted Mode; and Weighted median (WM)) were conducted as the complement to IVW. In addition, the robustness of the MR analysis results was assessed by leave-one-out analysis.

RESULTS

In forward MR, epilepsy showed causal relationships with sleep duration (IVW beta = 0.008, P = 0.015). Specifically, doubling the odds of inheriting epilepsy may be associated with a 0.0075 standard deviation (SD) (95 % CI: 1.001 to 1.014) increase in sleep duration. In reverse MR, we found statistically significant associations between chronotype (evening preference) (OR = 1.397, p = 0.007) and insomnia (OR = 2.280, p = 0.025) and the risk of epilepsy.

CONCLUSION

Our two-sample Mendelian randomization analyses indicate that individuals with epilepsy frequently experience extended sleep duration. Additionally, we identified insomnia and chronotype (evening preference) as significant risk factors that increase the likelihood of developing epilepsy.

Mechanistic insights into the interaction between epilepsy and sleep

Epidemiological evidence has demonstrated associations between sleep and epilepsy, but we lack a mechanistic understanding of these associations. If sleep affects the pathophysiology of epilepsy and the risk of seizures, as suggested by correlative evidence, then understanding these effects could provide crucial insight into the basic mechanisms that underlie the development of epilepsy and the generation of seizures. In this Review, we provide in-depth discussion of the associations between epilepsy and sleep at the cellular, network and system levels and consider the mechanistic underpinnings of these associations. We also discuss the clinical relevance of these associations, highlighting how they could contribute to improvements in the management of epilepsy. A better understanding of the mechanisms that govern the interactions between epilepsy and sleep could guide further research and the development of novel approaches to the management of epilepsy.

Dynamic multiday seizure cycles and evolving rhythms in a tetanus toxin rat model of epilepsy

Epilepsy is characterized by recurrent, unpredictable seizures that impose significant challenges in daily management and treatment. One emerging area of interest is the identification of seizure cycles, including multiday patterns, which may offer insights into seizure prediction and treatment optimization. This study investigated multiday seizure cycles in a Tetanus Toxin (TT) rat model of epilepsy. Six TT-injected rats were observed over a 40-day period, with continuous EEG monitoring to record seizure events. Wavelet transform analysis revealed significant multiday cycles in seizure occurrences, with periods ranging from 4 to 7 days across different rats. Synchronization Index (SI) analysis demonstrated variable phase locking, with some rats showing strong synchronization of seizures with specific phases of the cycle. Importantly, the study revealed that these seizure cycles are dynamic and evolve over time, with some rats exhibiting shifts in cycle periods during the recording period. This suggests that the underlying neural mechanisms driving these cycles may change as the epileptic state progresses. The identification of stable and evolving multiday rhythms in seizure activity, independent of external factors, highlights a potential intrinsic biological basis for seizure timing. These findings offer promising avenues for improving seizure forecasting and designing personalized, timing-based therapeutic interventions in epilepsy. Future research should explore the underlying neural mechanisms and clinical applications of multiday seizure cycles.

Circadian rhythm and epilepsy: a nationally representative cross-sectional study based on actigraphy data

Objective

The study aims to assess the relationship between epilepsy and circadian rhythms.

Method

This study included a cohort of 7,410 participants sourced from the 2013-2014 National Health and Nutrition Examination Survey (NHANES) database. The investigation focused on the comparative analysis of seven nonparametric indices associated with circadian rhythms (Interdaily Stability (IS), Intradaily Variability (IV), Relative Amplitude (RA), L5, M10, L5 start time, and M10 start time) between the overall population and patients with epilepsy. Logistic regression analysis was utilized to assess the potential correlation between the rest-activity circadian rhythm patterns and the presence of epilepsy within the cohort.

Results

Compared to the general population, individuals with epilepsy exhibited lower values of IS and M10. Multivariable logistic regression analysis, when IS, RA, and M10 were categorized into four groups based on quartiles, revealed that the odds ratio (IS: OR = 0.36, 95% CI: 0.13, 0.89; RA: OR = 0.25, 95% CI: 0.06, 0.77; M10: OR = 0.24, 95% CI: 0.06, 0.73) for the highest quartile was lower than that for the lowest quartile. Furthermore, after adjustment for confounding factors, participants in the highest quartile compared to those in the lowest quartile of IV and M10 start time demonstrated a higher prevalence of epilepsy.

Conclusion

Individuals with epilepsy demonstrate significant alterations in circadian rhythms.

Night shift work, poor sleep quality and unhealthy sleep behaviors are positively associated with the risk of epilepsy disease

BACKGROUND

Night shift work and poor sleep quality are gradually becoming more prevalent in modern society. Nevertheless, there have been limited studies assessing the association between night shift work, sleep behaviors, and risk of epilepsy. The aim of our study was to ascertain whether a positive association exists between night shift work, sleep quality, sleep behaviors, and risk of epilepsy.

METHODS

Our study included a total of over 270,000 individuals with or without epilepsy from the UK Biobank, followed up over a period of 13.5 years. Information on current night shift work and major sleep behaviors was also obtained. We used Cox proportional hazard models to assess the association between night shift work, sleep quality, sleep behaviors, and the risk of epilepsy after adjusting for multiple variables.

RESULTS

Night shift work was positively associated with a higher risk of epilepsy (P for trend = 0.059). There was a gradual increase in epilepsy risk from 'never/rarely' to 'usual/permanent' night shifts, with 'usual/permanent' night shifts work presenting the highest risk [hazard ratio (HR) 1.29, 95% confidence interval (CI) 1.01-1.65). Additionally, there was a significant association between sleep quality and risk of epilepsy (P < 0.001). Among the five major sleep behaviors, sleep duration (< 7 or > 8 h/day), frequent insomnia, and daytime sleepiness were significantly associated with a higher risk of epilepsy (HR 1.19, 95% CI 1.11-1.28; HR 1.19, 95% CI 1.09-1.30; HR 1.46, 95% CI 1.24-1.72, respectively). Furthermore, sleep duration exhibited a 'U-shaped' association with epilepsy risk. Nevertheless, no significant association was found between sleep chronotype and snoring and the risk of incident epilepsy (HR 1.04, 95% CI 0.96-1.12; HR 0.96, 95% CI 0.89-1.04).

CONCLUSIONS

'Usual/permanent' night shifts and poor sleep quality were positively associated with a greater risk of incident epilepsy. Major sleep behaviors, including unhealthy sleep duration (< 7 or > 8 h/day), frequent insomnia, and daytime sleepiness, also tended to increase the risk of epilepsy.

The value of self-reported variables in epilepsy monitoring and management. A systematic scoping review

PURPOSE

Self-reported records of seizure occurrences, seizure triggers and prodromal symptoms via paper or electronic tools are essential components of epilepsy management. Despite recent studies indicating that this information could hold important clinical value, the adoption of self-reported information in clinical practice is inconsistent and of uncertain value.

METHODS

We performed a systematic scoping review of the literature following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A combination of different digital libraries was used (Embase, MEDLINE, Global Health, PsycINFO). The review examined acceptability, adherence, and ability to self-report or predict seizures, along with innovative applications of self-reported data. We comprehensively outline study characteristics, key results, and identified strengths and limitations.

RESULTS

Sixty-eight full-text and two abstracts were included, where a total of 10 electronic tools were identified. Studies revealed high patient interest and acceptable adherence, particularly when tools were well-designed, and data shared with healthcare providers. While patients faced challenges in self-reporting or predicting seizures, a subgroup exhibited higher accuracy and compliance. Studies underscored the value of self-report information in identifying seizure clusters, understanding associations between self-reported seizure frequency and triggers, developing personalized seizure risk, forecasting and prediction models, and the potential benefits when integrated with wearable or implantable devices. Limitations included population selection, repeated dataset use, and the absence of gold standards for seizure counting.

CONCLUSION

Personalizing tools to collect self-report information, integrating them with wearable technologies, utilizing collected data for clinical outcomes, and merging them with electronic health records could provide a reliable resource for epilepsy monitoring and management.

A comprehensive digital mental health screening tool for people with epilepsy: A feasibility study in UK outpatients

BACKGROUND

Mental health symptoms are common in people with epilepsy, impacting medication adherence, quality of life, and mortality. Early detection and timely interventions for mental health symptoms will be crucial for improved outcomes but the absence of standardized screening procedures and time constraints hinder regular assessment and management.

PURPOSE

To evaluate feasibility, acceptability and, value of a digital tool for identifying mental health symptoms in adult and paediatric epilepsy outpatients using electronic Patient-Reported Outcome Measures (ePROMs).

METHODS AND MATERIALS

The study used an established local platform (IMPARTS - Integrating Mental and Physical Healthcare: Research Training and Services) to develop an online tool using e-PROMS for a comprehensive mental health screen (psychiatric symptoms, neurodevelopmental traits, and psychosocial/behavioural risk factors) of people with epilepsy. Prior to attending the outpatient clinical epilepsy services at King's College Hospital, participants were invited to complete the online screening tool through an SMS appointment link.

RESULTS

Out of 1081 epilepsy patients (955 adults, 126 paediatric), 38.2% of adults and 51.6% of carers of paediatric patients accessed the ePROMs, with modest completion rates of 15% and 14%, respectively. Adults reported mild to significant anxiety (37.4%), minor to major depression symptoms (29.2%), and occasionally psychotic symptoms (11.1%). Adults with self-reported psychiatric symptoms reported significantly higher number of seizures, seizure burden, insomnia, autistic and ADHD traits and lower quality of life and perceived social support. Only 21% of those reporting psychiatric symptoms were receiving any form of mental health support. A large proportion of paediatric patients presented emotional/behavioural difficulties (32%), high impulsivity (38.8%), low self-esteem (27.7%), sleep difficulties (50%), comorbid neurodevelopmental syndromes (27.7%). Both groups reported good level of perceived social support.

CONCLUSION

Our epilepsy adapted IMPARTS e-PROMS allowed remote screening for mental health symptoms, neurodevelopmental and resilience factors. Integrating these tools into electronic patient records might enhance early identification and facilitate referral to appropriate care pathways.

Association of baseline sleep duration and sleep quality with seizure recurrence in newly treated patients with epilepsy

OBJECTIVE

Although sleep duration and sleep quality are considered to be significant factors associated with epilepsy and seizure risk, findings are inconsistent, and their joint association remains uncertain. This study aimed to determine independent and joint associations of these two modifiable sleep features with seizure recurrence risk in newly treated patients with epilepsy (PWE).

METHODS

This is a prospective cohort study of newly treated PWE at a comprehensive epilepsy center in northeast China between June 2020 and December 2023. Self-reported sleep duration and sleep quality were collected at baseline. All patients were followed for 12 months for recurrent seizures. Cox proportional hazard regression models were used to estimate the hazard ratios (HRs) of seizure recurrence. Models fitted with restricted cubic spline were conducted to test for linear and nonlinear shapes of each association.

RESULTS

A total of 209 patients were included, and 103 experienced seizure recurrence during follow-up. Baseline short sleep was significantly associated with greater risk of seizure recurrence (adjusted HR = 2.282, 95% confidence interval [CI] = 1.436-3.628, p < .001). Sleep duration (h/day) and recurrent seizure risk showed a significant nonlinear U-shaped association, with a nadir at 8 h/day. Baseline poor sleep quality was significantly associated with greater risk of seizure recurrence (adjusted HR = 1.985, 95% CI = 1.321-2.984, p < .001). Pittsburgh Sleep Quality Index score and seizure recurrence risk exhibited a positive linear association. Participants with a combination of poor quality-short sleep showed the highest risk of seizure recurrence (adjusted HR = 3.13, 95% CI = 1.779-5.507, p < .001) compared to the referent good quality-intermediate sleep group.

SIGNIFICANCE

Baseline sleep duration and sleep quality were independently and jointly associated with risk of seizure recurrence in newly treated PWE. Our results point to an important potential role of baseline sleep duration and sleep quality in shaping seizure risk.

Timing Mechanisms for Circadian Seizures

For centuries, epileptic seizures have been noticed to recur with temporal regularity, suggesting that an underlying biological rhythm may play a crucial role in their timing. In this review, we propose to adopt the framework of chronobiology to study the circadian timing of seizures. We first review observations made on seizure timing in patients with epilepsy and animal models of the disorder. We then present the existing chronobiology paradigm to disentangle intertwined circadian and sleep-wake timing mechanisms. In the light of this framework, we review the existing evidence for specific timing mechanisms in specific epilepsy syndromes and highlight that current knowledge is far from sufficient. We propose that individual seizure chronotypes may result from an interplay between independent timing mechanisms. We conclude with a research agenda to help solve the urgency of ticking seizures.

Classifier Combination Supported by the Sleep-Wake Cycle Improves EEG Seizure Prediction Performance

OBJECTIVE

Seizure prediction is a promising solution to improve the quality of life for drug-resistant patients, which concerns nearly 30% of patients with epilepsy. The present study aimed to ascertain the impact of incorporating sleep-wake information in seizure prediction.

METHODS

We developed five patient-specific prediction approaches that use vigilance state information differently: i) using it as an input feature, ii) building a pool of two classifiers, each with different weights to sleep/wake training samples, iii) building a pool of two classifiers, each with only sleep/wake samples, iv) changing the alarm-threshold concerning each sleep/wake state, and v) adjusting the alarm-threshold after a sleep-wake transition. We compared these approaches with a control method that did not integrate sleep-wake information. Our models were tested with data (43 seizures and 482 hours) acquired during presurgical monitoring of 17 patients from the EPILEPSIAE database. As EPILEPSIAE does not contain vigilance state annotations, we developed a sleep-wake classifier using 33 patients diagnosed with nocturnal frontal lobe epilepsy from the CAP Sleep database.

RESULTS

Although different patients may require different strategies, our best approach, the pool of weighted predictors, obtained 65% of patients performing above chance level with a surrogate analysis (against 41% in the control method).

CONCLUSION

The inclusion of vigilance state information improves seizure prediction. Higher results and testing with long-term recordings from daily-life conditions are necessary to ensure clinical acceptance.

SIGNIFICANCE

As automated sleep-wake detection is possible, it would be feasible to incorporate these algorithms into future devices for seizure prediction.

The prevalence and predictors of poor sleep quality and excessive daytime sleepiness in epilepsy: A single tertiary centre experience in Malaysia

BACKGROUND AND OBJECTIVE

People with epilepsy frequently encounter sleep disruptions that can stem from a variety of complex factors. Epilepsy-related sleep disturbance can lead to reduced quality of life and excessive daytime hypersomnolence. Identification of sleep disturbances may help in the overall management of epilepsy patients. This study was conducted to determine the prevalence and predictors of poor sleep quality and daytime sleepiness in epilepsy.

METHODS

A cross-sectional study on 284 epilepsy patients was performed in a local tertiary centre. The demographic and clinical epilepsy data were collected. The Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) questionnaires were utilised to determine the quality of life and daytime hypersomnolence of epilepsy patients, respectively.

RESULTS

Poor sleep quality was reported in 78 (27.5%) patients while daytime hypersomnolence was present in 17 (6%) patients. The predictors of poor sleep quality include structural causes (OR = 2.749; 95% CI: 1.436, 5.264, p = 0.002), generalised seizures (OR = 1.959, 95% CI: 1.04, 3.689, p = 0.037), and antiseizure medications such as Carbamazepine (OR = 2.34; 95% CI: 1.095, 5.001, p = 0.028) and Topiramate (OR 2.487; 95% CI: 1.028, 6.014, p = 0.043). Females are 3.797 times more likely score higher in ESS assessment (OR 3.797; 95% CI: 1.064, 13.555 p = 0.04).

DISCUSSION

Sleep disturbances frequently coexist with epilepsy. Patients should be actively evaluated using the PSQI and ESS questionnaires. It is imperative to identify the key factors that lead to reduced sleep quality and heightened daytime sleepiness in patients with epilepsy, as this is essential to properly manage their condition.

Sleep and epilepsy: A clinical and pathophysiological overview

The interaction between sleep and epilepsy is complex. A better understanding of the mechanisms linking sleep and epilepsy appears increasingly important as it may improve diagnosis and therapeutic strategies in patients with epilepsy. In this narrative review, we aim to (i) provide an overview of the physiological and pathophysiological processes linking sleep and epilepsy; (ii) present common sleep disorders in patients with epilepsy; (iii) discuss how sleep and sleep disorders should be considered in new therapeutic approaches to epilepsy such as neurostimulation; and (iv) present the overall nocturnal manifestations and differential diagnosis between epileptic seizures and parasomnia.

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.