Wearable devices for sudden unexpected death in epilepsy prevention

Mortality in older adults with epilepsy: An understudied entity

Despite the recognition of Sudden Unexpected Death in Epilepsy (SUDEP) and other risks of premature mortality in people with epilepsy (PWE), mortality in older PWE remains an understudied entity. This review provides a comprehensive overview of the multifaceted causes of premature mortality in older adults with epilepsy and emphasizes the need for targeted interventions to reduce mortality and enhance the quality of life in this vulnerable population. It underscores the heightened prevalence of epilepsy among older adults and the interplay of intrinsic and extrinsic factors contributing to their mortality. Further, this paper delves into the nuances of diagnosing SUDEP in older adults and the underestimation of its incidence due to misclassification and lack of standardized protocols. Factors such as frailty, comorbidities, and the bidirectional relationship between epilepsy and conditions such as dementia and stroke further compound the mortality risks. Key factors, including status epilepticus, comorbid conditions (such as cardiovascular diseases, cerebrovascular events, and neurodegenerative disorders), and external causes like accidents, falls, and suicide, are discussed. It also examines the implications of anti-seizure medications, particularly polypharmacy, and their adverse effects on this population. Future directions include implementing enhanced diagnostic protocols, developing treatment plans, and integrating real-time monitoring technologies to reduce the risk of sudden death and multifaceted premature mortality in this patient population. Increasing awareness among healthcare providers and families about the risks and management of epilepsy in older adults, along with fostering collaborative research efforts, is essential to improve mortality outcomes. PLAIN LANGUAGE SUMMARY: There is a heightened risk of mortality in older people with epilepsy due to many causes unique to their population. Despite the risk, Sudden Unexpected Death in Epilepsy and early mortality in older adults with epilepsy are underestimated. Unique contributing factors include comorbid conditions like dementia, stroke, and frailty, adverse effects from polypharmacy, and increased risks of cardiovascular complications and external injuries such as falls and suicide. A careful consideration of all these factors can help mitigate the mortality in older adults with epilepsy.

Seizure Detection Devices

Goals of automated detection of epileptic seizures using wearable devices include objective documentation of seizures, prevention of sudden unexpected death in epilepsy (SUDEP) and seizure-related injuries, obviating both the unpredictability of seizures and potential social embarrassment, and finally to develop seizure-triggered on-demand therapies. Automated seizure detection devices are based on the analysis of EEG signals (scalp-EEG, subcutaneous EEG and intracranial EEG), of motor manifestations of seizures (surface EMG, accelerometry), and of physiologic autonomic changes caused by seizures (heart and respiration rate, oxygen saturation, sweat secretion, body temperature). While the detection of generalized tonic-clonic and of focal to bilateral tonic-clonic seizures can be achieved with high sensitivity and low false alarm rates, the detection of focal seizures is still suboptimal, especially in the everyday ambulatory setting. Multimodal seizure detection devices in general provide better performance than devices based on single measurement parameters. Long-term use of seizure detection devices in home environments helps to improve the accuracy of seizure diaries and to reduce seizure-related injuries, while evidence for prevention of SUDEP is still lacking. Automated seizure detection devices are generally well accepted by patients and caregivers.

Update review on SUDEP: Risk assessment, background & seizure detection devices

This review focusses on sudden unexpected death in epilepsy patients (SUDEP) and incorporates risk stratification (through SUDEP risk factors and SUDEP risk scores), hypotheses on the mechanism of SUDEP and eligible seizure detection devices (SDDs) for further SUDEP prevention studies. The main risk factors for SUDEP are the presence and the frequency of generalized tonic-clonic seizures (GTC). In Swedish population-based case control study, the Odds ratio of the presence of GTC in the absence of bedroom sharing is 67. SUDEP risk scoring systems express a score that represents the cumulative presence of SUDEP risk factors, but not the exact effect of their combination. We describe 4 of the available scoring systems: SUDEP-7 inventory, SUDEP-3 inventory, SUDEP-ClinicAl Risk scorE (SUDEP-CARE score) and Kempenhaeghe SUDEP risk score. Although they all include GTC, their design is often different. Three of 4 scoring systems were validated (SUDEP-7 inventory, SUDEP-3 inventory and SUDEP-CARE score). None of the available scoring systems has been sufficiently validated for the use in a general epilepsy population. Plausible mechanisms of SUDEP are discussed. In the MORTEMUS-study (Mortality in Epilepsy Monitoring Unit Study), SUDEP was a postictal cardiorespiratory arrest after a GTC. The parallel respiratory and cardiac dysfunction in SUDEP suggests a central dysfunction of the brainstem centers that are involved in the control of respiration and heart rhythm. In the (consequent) adenosine serotonin hypotheses SUDEP occurs when a postictal adenosine-mediated respiratory depression is not compensated by the effect of serotonin. Other (adjuvant) mechanisms and factors are discussed. Seizure detection devices (SDDs) may help to improve nocturnal supervision. Five SDDs have been validated in phase 3 studies for the detection of TC: Seizure Link®, Epi-Care®, NightWatch, Empatica, Nelli®. They have demonstrated a sensitivity of at least 90 % combined with an acceptable false positive alarm rate. It has not yet been proven that the use will actually lead to SUDEP prevention, but clinical experience supports their effectiveness.

eDeeplepsy: An artificial neural framework to reveal different brain states in children with epileptic spasms

OBJECTIVE

Despite advances, analysis and interpretation of EEG still essentially rely on visual inspection by a super-specialized physician. Considering the vast amount of data that composes the EEG, much of the detail inevitably escapes ordinary human scrutiny. Significant information may not be evident and is missed, and misinterpretation remains a serious problem. Can we develop an artificial intelligence system to accurately and efficiently classify EEG and even reveal novel information? In this study, deep learning techniques and, in particular, Convolutional Neural Networks, have been used to develop a model (which we have named eDeeplepsy) for distinguishing different brain states in children with epilepsy.

METHODS

A novel EEG database from a homogenous pediatric population with epileptic spasms beyond infancy was constituted by epileptologists, representing a particularly intriguing seizure type and challenging EEG. The analysis was performed on such samples from long-term video-EEG recordings, previously coded as images showing how different parts of the epileptic brain are distinctly activated during varying states within and around this seizure type.

RESULTS

Results show that not only could eDeeplepsy differentiate ictal from interictal states but also discriminate brain activity between spasms within a cluster from activity away from clusters, usually undifferentiated by visual inspection. Accuracies between 86 % and 94 % were obtained for the proposed use cases.

SIGNIFICANCE

We present a model for computer-assisted discrimination that can consistently detect subtle differences in the various brain states of children with epileptic spasms, and which can be used in other settings in epilepsy with the purpose of reducing workload and discrepancies or misinterpretations. The research also reveals previously undisclosed information that allows for a better understanding of the pathophysiology and evolving characteristics of this particular seizure type. It does so by documenting a different state (interspasms) that indicates a potentially non-standard signal with distinctive epileptogenicity at that period.

Ambulatory seizure detection

PURPOSE OF REVIEW

To review recent advances in the field of seizure detection in ambulatory patients with epilepsy.

RECENT FINDINGS

Recent studies have shown that wrist or arm wearable sensors, using 3D-accelerometry, electrodermal activity or photoplethysmography, in isolation or in combination, can reliably detect focal-to-bilateral and generalized tonic-clonic seizures (GTCS), with a sensitivity over 90%, and false alarm rates varying from 0.1 to 1.2 per day. A headband EEG has also demonstrated a high sensitivity for detecting and help monitoring generalized absence seizures. In contrast, no appropriate solution is yet available to detect focal seizures, though some promising findings were reported using ECG-based heart rate variability biomarkers and subcutaneous EEG.

SUMMARY

Several FDA and/or EU-certified solutions are available to detect GTCS and trigger an alarm with acceptable rates of false alarms. However, data are still missing regarding the impact of such intervention on patients' safety. Noninvasive solutions to reliably detect focal seizures in ambulatory patients, based on either EEG or non-EEG biosignals, remain to be developed. To this end, a number of challenges need to be addressed, including the performance, but also the transparency and interpretability of machine learning algorithms.

Report of the ILAE SUDEP Task Force on national recommendations and practices around the world regarding the use of wearable seizure detection devices: A global survey

Wearable seizure detection devices have the potential to address unmet needs of people with epilepsy. A recently published evidence-based international guideline recommends using such devices for safety indications in patients with tonic-clonic seizures (TCS). Our objective was to map existing guidelines and clinical practices at national level. We conducted a survey of the International League Against Epilepsy (ILAE) chapters regarding national recommendations and practical circumstances for prescribing seizure detection devices, and another survey of physicians in the ILAE constituency anywhere in the world, concerning their views and practices regarding recommendations for and prescription of such devices. Fifty-eight ILAE chapters (response rate 48%) and 157 physicians completed the surveys. More than two-thirds of responding countries do not have standards on wearables for seizure detection, although they indicated availability of such devices. The most often recognized indications were safety and objective seizure quantification. In nearly half of countries, devices are purchased by patients or caregivers, and either lack a uniform reimbursement scheme (41%) or patients pay the full cost for the device (48%). Tonic-clonic seizure frequency, nocturnal seizures, and previous injuries were the main factors that influenced the surveyed physicians to recommend wearable seizure detection devices. Our results document the need to implement international clinical practice guidelines at national level and to consider these when deciding upon reimbursement of seizure detection devices.

Non-electroencephalogram-based seizure detection devices: State of the art and future perspectives

INTRODUCTION AND PURPOSE

The continuously expanding research and development of wearable devices for automated seizure detection in epilepsy uses mostly non-invasive technology. Real-time alarms, triggered by seizure detection devices, are needed for safety and prevention to decrease seizure-related morbidity and mortality, as well as objective quantification of seizure frequency and severity. Our review strives to provide a state-of-the-art on automated seizure detection using non-invasive wearable devices in an ambulatory (home) environment and to highlight the prospects for future research.

METHODS

A joint working group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) recently published a clinical practice guideline on automated seizure detection using wearable devices. We updated the systematic literature search for the period since the last search by the joint working group. We selected studies qualifying minimally as phase-2 clinical validation trials, in accordance with standards for testing and validation of seizure detection devices.

RESULTS

High-level evidence (phases 3 and 4) is available only for the detection of tonic-clonic seizures and major motor seizures when using wearable devices based on accelerometry, surface electromyography (EMG), or a multimodal device combining accelerometry and heart rate. The reported sensitivity of these devices is 79.4-96%, with a false alarm rate of 0.20-1.92 per 24 hours (0-0.03 per night). A single phase-3 study validated the detection of absence seizures using a single-channel wearable EEG device. Two phase-4 studies showed overall user satisfaction with wearable seizure detection devices, which helped decrease injuries related to tonic-clonic seizures. Overall satisfaction, perceived sensitivity, and improvement in quality-of-life were significantly higher for validated devices.

CONCLUSIONS

Among the vast number of studies published on seizure detection devices, most are strongly affected by potential bias, providing a too-optimistic perspective. By applying the standards for clinical validation studies, potential bias can be reduced, and the quality of a continuously growing number of studies in this field can be assessed and compared. The ILAE-IFCN clinical practice guideline on automated seizure detection using wearable devices recommends using clinically validated wearable devices for automated detection of tonic-clonic seizures when significant safety concerns exist. The studies published after the guideline was issued only provide incremental knowledge and would not change the current recommendations.

Wrist-worn smartwatch and predictive models for seizures

OBJECTIVE

This study was undertaken to describe extracerebral biosignal characteristics of overall and various seizure types as compared with baseline physical activities using multimodal devices (Empatica E4); develop predictive models for overall and each seizure type; and assess diagnostic performance of each model.

METHODS

We prospectively recruited patients with focal epilepsy who were admitted to the epilepsy monitoring unit for presurgical evaluation during January to December 2020. All study participants were simultaneously applied gold standard long-term video-electroencephalographic (EEG) monitoring and an index test, E4. Two certified epileptologists independently determined whether captured events were seizures and then indicated ictal semiology and EEG information. Both were blind to multimodal biosignal findings detected by E4. Biosignals during 5-min epochs of both seizure events and baseline were collected and compared. Predictive models for occurrence overall and of each seizure type were developed using a generalized estimating equation. Diagnostic performance of each model was then assessed.

RESULTS

Thirty patients had events recorded and were recruited for analysis. One hundred eight seizure events and 120 baseline epochs were collected. Heart rate (HR), acceleration (ACC), and electrodermal activity (EDA) but not temperature were significantly elevated during seizures. Cluster analysis showed trends of greatest elevation of HR and ACC in bilateral tonic-clonic seizures (BTCs), as compared with non-BTCs and isolated auras. HR and ACC were independent predictors for overall seizure types, BTCs, and non-BTCs, whereas only HR was a predictor for isolated aura. Diagnostic performance including sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve of the predictive model for overall seizures were 77.78%, 60%, and .696 (95% confidence interval = .628-.764), respectively.

SIGNIFICANCE

Multimodal extracerebral biosignals (HR, ACC, EDA) detected by a wrist-worn smartwatch can help differentiate between epileptic seizures and normal physical activities. It would be worthwhile to implement our predictive algorithms in commercial seizure detection devices. However, larger studies to externally validate our predictive models are required.

Nocturnal seizure detection: What are the needs and expectations of adults with epilepsy receiving secondary care?

INTRODUCTION

Seizure detection devices (SDDs) may lower the risk of sudden unexpected death in epilepsy (SUDEP) and provide reassurance to people with epilepsy and their relatives. We aimed to explore the perspectives of those receiving secondary care on nocturnal SDDs and epilepsy in general.

MATERIALS AND METHODS

We recruited adults with tonic or tonic-clonic seizures who had at least one nocturnal seizure in the preceding year. We used semi-structured interviews and questionnaires to explore their views on SDDs and their experiences of living with epilepsy. None of the participants had any previous experience with SDDs. We analyzed the data using qualitative content analysis.

RESULTS

Eleven participants were included with a nocturnal seizure frequency ranging from once every few weeks to less than once a year. Some participants experienced little burden of disease, whereas others were extremely impaired. Opinions on the perceived benefit of seizure detection varied widely and did not always match the clinical profile. Some participants with high SUDEP risk displayed no interest at all, whereas others with a low risk for unattended seizures displayed a strong interest. Reasons for wanting to use SDDs included providing reassurance, SUDEP prevention, and improving night rest. Reasons for not wanting to use SDDs included not being able to afford it, having to deal with false alarms, not having anyone to act upon the alarms, having a relative that will notice any seizures, not feeling like the epilepsy is severe enough to warrant SDD usage or not trusting the device.

CONCLUSIONS

The interest in nocturnal seizure detection varies among participants with low seizure frequencies and does not always match the added value one would expect based on the clinical profile. Further developments should account for the heterogeneity in user groups.

Multimodal nocturnal seizure detection in children with epilepsy: A prospective, multicenter, long-term, in-home trial

OBJECTIVE

There is a pressing need for reliable automated seizure detection in epilepsy care. Performance evidence on ambulatory non-electroencephalography-based seizure detection devices is low, and evidence on their effect on caregiver's stress, sleep, and quality of life (QoL) is still lacking. We aimed to determine the performance of NightWatch, a wearable nocturnal seizure detection device, in children with epilepsy in the family home setting and to assess its impact on caregiver burden.

METHODS

We conducted a phase 4, multicenter, prospective, video-controlled, in-home NightWatch implementation study (NCT03909984). We included children aged 4-16 years, with ≥1 weekly nocturnal major motor seizure, living at home. We compared a 2-month baseline period with a 2-month NightWatch intervention. The primary outcome was the detection performance of NightWatch for major motor seizures (focal to bilateral or generalized tonic-clonic [TC] seizures, focal to bilateral or generalized tonic seizures lasting >30 s, hyperkinetic seizures, and a remainder category of focal to bilateral or generalized clonic seizures and "TC-like" seizures). Secondary outcomes included caregivers' stress (Caregiver Strain Index [CSI]), sleep (Pittsburgh Quality of Sleep Index), and QoL (EuroQol five-dimension five-level scale).

RESULTS

We included 53 children (55% male, mean age = 9.7 ± 3.6 years, 68% learning disability) and analyzed 2310 nights (28 173 h), including 552 major motor seizures. Nineteen participants did not experience any episode of interest during the trial. The median detection sensitivity per participant was 100% (range = 46%-100%), and the median individual false alarm rate was .04 per hour (range = 0-.53). Caregiver's stress decreased significantly (mean total CSI score = 8.0 vs. 7.1, p = .032), whereas caregiver's sleep and QoL did not change significantly during the trial.

SIGNIFICANCE

The NightWatch system demonstrated high sensitivity for detecting nocturnal major motor seizures in children in a family home setting and reduced caregiver stress.

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients' data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient's position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.

Personalized seizure detection using logistic regression machine learning based on wearable ECG-monitoring device

PURPOSE

Wearable automated detection devices of focal epileptic seizures are needed to alert patients and caregivers and to optimize the medical treatment. Heart rate variability (HRV)-based seizure detection devices have presented good detection sensitivity. However, false alarm rates (FAR) are too high.

METHODS

In this phase-2 study we pursued to decrease the FAR, by using patient-adaptive logistic regression machine learning (LRML) to improve the performance of a previously published HRV-based seizure detection algorithm. ECG-data were prospectively collected using a dedicated wearable electrocardiogram-device during long-term video-EEG monitoring. Sixty-two patients had 174 seizures during 4,614 h recording. The dataset was divided into training-, cross-validation-, and test-sets (chronological) in order to avoid overfitting. Patients with >50 beats/min change in heart rate during first recorded seizure were selected as responders. We compared 18 LRML-settings to find the optimal algorithm.

RESULTS

The patient-adaptive LRML-classifier in combination with using only responders to train the initial decision boundary was superior to both the generic approach and including non-responders to train the LRML-classifier. Using the optimal setting of the LRML in responders in the test dataset yielded a sensitivity of 78.2% and FAR of 0.62/24 h. The FAR was reduced by 31% compared to the previous method, upholding similar sensitivity.

CONCLUSION

The novel, patient-adaptive LRML seizure detection algorithm outperformed both the generic approach and the previously published patient-tailored method. The proposed method can be implemented in a wearable online HRV-based seizure detection system alerting patients and caregivers of seizures and improve seizure-count which may help optimizing the patient treatment.

Seizure-related complication rate in a residential population with epilepsy and intellectual disability (ECOMRAID-trial)

INTRODUCTION

The aim of the ECOMRAID trial (Epileptic seizure related Complication RAte in residential population of persons with epilepsy and Intellectual Disability) was to study seizure-related complications (status epilepticus, respiratory complications, or other severe complications) in people with epilepsy and intellectual disability living in a residential setting. The results of the present study are a prerequisite for performing a prospective study into the effectiveness of nocturnal surveillance patients with high risk for Sudden unexpected death in epilepsy (SUDEP).

MATERIAL AND METHODS

A retrospective study was conducted in three general residential care institutions and one residential specialized epilepsy clinic. In this 5-year cohort, we collected the following data: age (at inclusion and in case of death), sex, type of residential care, different types of complications, rescue/emergency medication administration, transfers to another department (internal midcare / monitoring unit or general hospital) and a self-designed SUDEP risk score. Our primary research questions were to assess the number of patients who experienced seizure-related complications and their individual complication rates. The secondary research questions were to document the relationship of these complications with the SUDEP risk score, with the type of residential living, and with the frequency of interventions by caregivers.

RESULTS

We included 370 patients (1790 patient-years) and in 135 of them, we found 717 seizure-related complications. The following complication rates were found: all complications: at 36%, status epilepticus: at 13%, respiratory complications: at 5%, and other complications at 26%. In residential care institutions, we found fewer patients with complications compared to the specialized epilepsy clinic (all complications 24% vs 42%, OR 0.44, p < 0.01; status epilepticus 5% vs 17%, OR 0.27, p < 0.01; other: complications 19% vs 30%, OR 0.56, p < 0.05). In residential care institutions, we found more "other complications" than in the specialized epilepsy clinic (89% vs 71%, OR 3.13, p < 0.0001). The annual frequency of all complications together was higher in residential care institutions (range 0 to 21 vs 0 to 10, p < 0.05). Rescue medication was given to 75% of the patients, but more often in the specialized epilepsy clinic (median 2.6 vs 0.5 times/patient/year, p < 0.001). In the specialized epilepsy clinic, more patients were transferred to a midcare / monitoring unit or general hospital (56% vs 9%, OR 13.44, p < 0.0001) with higher yearly frequencies (median 0.2 vs 0.0, p < 0.001). There were no reported cases of SUDEP. The median SUDEP risk score was higher in the specialized epilepsy clinic (5 vs 4, p < 0.05) and was weakly correlated with the status epilepticus (ρ = 0.20, p < 0.001) and (total) complication rate (ρ = 0.18, p < 0.001).

CONCLUSION

We found seizure-related complications in more than one-third of the patients with epilepsy and intellectual disability living in a residential setting over a period of 5 years. The data also quantify seizure-related complications in patients with epilepsy and intellectual disability.

A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.

Toward automated prediction of sudden unexpected death in epilepsy

Sudden unexpected death in epilepsy (SUDEP) is a devastating yet overlooked complication of epilepsy. The rare and complex nature of SUDEP makes it challenging to study. No prediction or prevention of SUDEP is currently available in a clinical setting. In the past decade, significant advances have been made in our knowledge of the pathophysiologic cascades that lead to SUDEP. In particular, studies of brain, heart, and respiratory functions in both human patients at the epilepsy monitoring unit and animal models during fatal seizures provide critical information to integrate computational tools for SUDEP prediction. The rapid advances in automated seizure detection and prediction algorithms provide a fundamental framework for their adaption in predicting SUDEP. If a SUDEP can be predicted, then there will be a potential for medical intervention to be administered, either by their caregivers or via an implanted device automatically delivering electrical stimulation or medication, and finally save lives from fatal seizures. This article presents recent developments of SUDEP studies focusing on the pathophysiologic basis of SUDEP and computational implications of machine learning techniques that can be adapted and extended for SUDEP prediction. This article also discusses some novel ideas for SUDEP prediction and rescue including principal component analysis and closed-loop intervention.

Sudden unexpected death in epilepsy

PURPOSE OF REVIEW

Sudden unexpected death in epilepsy (SUDEP) is a major contributor to premature mortality in people with epilepsy. This review provides an update on recent findings on the epidemiology of SUDEP, clinical risk factors and potential mechanisms.

RECENT FINDINGS

The overall risk rate of SUDEP is approximately 1 per 1000 patients per year in the general epilepsy population and that children and older adults have a similar incidence. Generalized convulsive seizures (GCS), perhaps through their effects on brainstem cardiopulmonary networks, can cause significant postictal respiratory and autonomic dysfunction though other mechanisms likely exist as well. Work in animal models of SUDEP has identified multiple neurotransmitter systems, which may be future targets for pharmacological intervention. There are also chronic functional and structural changes in autonomic function in patients who subsequently die from SUDEP suggesting that some SUDEP risk is dynamic. Modifiable risks for SUDEP include GCS seizure frequency, medication adherence and nighttime supervision.

SUMMARY

Current knowledge of SUDEP risk factors has identified multiple targets for SUDEP prevention today as we await more specific therapeutic targets that are emerging from translational research studies.

Parental preferences for seizure detection devices: a discrete choice experiment

Objective

Previous studies identified essential user preferences for seizure detection devices (SDDs), without addressing their relative strength. We performed a discrete choice experiment (DCE) to quantify attributes' strength, and to identify the determinants of user SDD preferences.

Methods

We designed an online questionnaire targeting parents of children with epilepsy to define the optimal balance between SDD sensitivity and positive predictive value (PPV) while accounting for individual seizure frequency. We selected five DCE attributes from a recent study. Using a Bayesian design, we constructed 11 unique choice tasks and analyzed these using a mixed multinomial logit model.

Results

One hundred parents responded to the online questionnaire link; 49 completed all tasks, whereas 28 completed the questions, but not the DCE. Most parents preferred a relatively high sensitivity (80%–90%) over a high PPV (>50%). The preferred sensitivity‐to‐PPV ratio correlated with seizure frequency (r = −.32), with a preference for relative high sensitivity and low PPV among those with relative low seizure frequency (p = .04). All DCE attributes significantly impacted parental choices. Parents expressed preferences for consulting a neurologist before device use, personally training the device's algorithm, interaction with their child via audio and video, alarms for all seizure types, and an interface detailing measurements during an alarm. Preferences varied between subgroups (learning disability or not, SDD experience, relative low vs. high seizure frequency based on the population median).

Significance

Various attributes impact parental SDD preferences and may explain why preferences vary among users. Tailored approaches may help to meet the contrasting needs among SDD users.

The Individual Ictal Fingerprint: Combining Movement Measures With Ultra Long-Term Subcutaneous EEG in People With Epilepsy

Background: Epileptic seizures are caused by abnormal brain wave hypersynchronization leading to a range of signs and symptoms. Tools for detecting seizures in everyday life typically focus on cardiac rhythm, electrodermal activity, or movement (EMG, accelerometry); however, these modalities are not very effective for non-motor seizures. Ultra long-term subcutaneous EEG-devices can detect the electrographic changes that do not depend on clinical changes. Nonetheless, this also means that it is not possible to assess whether a seizure is clinical or subclinical based on an EEG signal alone. Therefore, we combine EEG and movement-related modalities in this work. We focus on whether it is possible to define an individual “multimodal ictal fingerprint” which can be exploited in different epilepsy management purposes.

Methods: This study used ultra long-term data from an outpatient monitoring trial of persons with temporal lobe epilepsy obtained with a subcutaneous EEG recording system. Subcutaneous EEG, an EMG estimate and chest-mounted accelerometry were extracted from four persons showing more than 10 well-defined electrographic seizures each. Numerous features were computed from all three modalities. Based on these, the Gini impurity measure of a Random Forest classifier was used to select the most discriminative features for the ictal fingerprint. A total of 74 electrographic seizures were analyzed.

Results: The optimal individual ictal fingerprints included features extracted from all three tested modalities: an acceleration component; the power of the estimated EMG activity; and the relative power in the delta (0.5–4 Hz), low theta (4–6 Hz), and high theta (6–8 Hz) bands of the subcutaneous EEG. Multimodal ictal fingerprints were established for all persons, clustering seizures within persons, while separating seizures across persons.

Conclusion: The existence of multimodal ictal fingerprints illustrates the benefits of combining multiple modalities such as EEG, EMG, and accelerometry in future epilepsy management. Multimodal ictal fingerprints could be used by doctors to get a better understanding of the individual seizure semiology of people with epilepsy. Furthermore, the multimodal ictal fingerprint gives a better understanding of how seizures manifest simultaneously in different modalities. A knowledge that could be used to improve seizure acknowledgment when reviewing EEG without video.

Wearable electroencephalography for ultra-long-term seizure monitoring: a systematic review and future prospects

INTRODUCTION

: Wearable electroencephalography (EEG) for objective seizure counting might transform the clinical management of epilepsy. Non-EEG modalities have been validated for the detection of convulsive seizures, but there is still an unmet need for the detection of non-convulsive seizures.

AREAS COVERED

: The main objective of this systematic review was to explore the current status on wearable surface- and subcutaneous EEG for long-term seizure monitoring in epilepsy. We included 17 studies and evaluated the progress on the field, including device specifications, intended populations, and main results on the published studies including diagnostic accuracy measures. Furthermore, we examine the hurdles for widespread clinical implementation. This systematic review and expert opinion both consults the PRISMA guidelines and reflects on the future perspectives of this emerging field.

EXPERT OPINION

: Wearable EEG for long-term seizure monitoring is an emerging field, with plenty of proposed devices and proof-of-concept clinical validation studies. The possible implications of these devices are immense including objective seizure counting and possibly forecasting. However, the true clinical value of the devices, including effects on patient important outcomes and clinical decision making is yet to be unveiled and large-scale clinical validation trials are called for.

Autonomic manifestations of epilepsy: emerging pathways to sudden death?

Epileptic networks are intimately connected with the autonomic nervous system, as exemplified by a plethora of ictal (during a seizure) autonomic manifestations, including epigastric sensations, palpitations, goosebumps and syncope (fainting). Ictal autonomic changes might serve as diagnostic clues, provide targets for seizure detection and help us to understand the mechanisms that underlie sudden unexpected death in epilepsy (SUDEP). Autonomic alterations are generally more prominent in focal seizures originating from the temporal lobe, demonstrating the importance of limbic structures to the autonomic nervous system, and are particularly pronounced in focal-to-bilateral and generalized tonic-clonic seizures. The presence, type and severity of autonomic features are determined by the seizure onset zone, propagation pathways, lateralization and timing of the seizures, and the presence of interictal autonomic dysfunction. Evidence is mounting that not all autonomic manifestations are linked to SUDEP. In addition, experimental and clinical data emphasize the heterogeneity of SUDEP and its infrequent overlap with sudden cardiac death. Here, we review the spectrum and diagnostic value of the mostly benign and self-limiting autonomic manifestations of epilepsy. In particular, we focus on presentations that are likely to contribute to SUDEP and discuss how wearable devices might help to prevent SUDEP.

The Challenging Path to Developing a Mobile Health Device for Epilepsy: The Current Landscape and Where We Go From Here

Seizure detection, and more recently seizure forecasting, represent important avenues of clinical development in epilepsy, promoted by progress in wearable devices and mobile health (mHealth), which might help optimizing seizure control and prevention of seizure-related mortality and morbidity in persons with epilepsy. Yet, very long-term continuous monitoring of seizure-sensitive biosignals in the ambulatory setting presents a number of challenges. We herein provide an overview of these challenges and current technological landscape of mHealth devices for seizure detection. Specifically, we display, which types of sensor modalities and analytical methods are available, and give insight into current clinical practice guidelines, main outcomes of clinical validation studies, and discuss how to evaluate device performance at point-of-care facilities. We then address pitfalls which may arise in patient compliance and the need to design solutions adapted to user experience.

Parental experiences and perspectives on the value of seizure detection while caring for a child with epilepsy: A qualitative study

INTRODUCTION

Caring for a child with epilepsy has a significant impact on parental quality of life. Seizure unpredictability and complications, including sudden unexpected death in epilepsy (SUDEP), may cause high parental stress and increased anxiety. Nocturnal supervision with seizure detection devices may lower SUDEP risk and decrease parental burden of seizure monitoring, but little is known about their added value in family homes.

METHODS

We conducted semi-structured in-depth interviews with parents of children with refractory epilepsy participating in the PROMISE trial (NCT03909984) to explore the value of seizure detection in the daily care of their child. Children were aged 4-16 years, treated at a tertiary epilepsy center, had at least one nocturnal major motor seizure per week, and used a wearable seizure detection device (NightWatch) for two months at home. Data were analyzed using inductive thematic analysis.

RESULTS

Twenty three parents of nineteen children with refractory epilepsy were interviewed. All parents expressed their fear of missing a large seizure and the possible consequences of not intervening in time. Some parents felt the threat of child loss during every seizure, while others thought about it from time to time. The fear could fluctuate over time, mainly associated with fluctuations of seizure frequency. Most parents described how they developed a protective behavior, driven by this fear. The way parents handled the care of their child and experienced the burden of care influenced their perceptions on the added value of NightWatch. The experienced value of NightWatch depended on the amount of assurance it could offer to reduce their fear and the associated protective behavior as well as their resilience to handle the potential extra burden of care, due to false alarms or technical problems.

CONCLUSION

Healthcare professionals and device companies should be aware of parental protective behavior and the high parental burden of care and develop tailored strategies to optimize seizure detection device care.

Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit

Background: Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs).

Methods: Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration (“Active mode”).

Results: Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6–20 years, and 67 adult aged 21–63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different (p > 0.05) from the adult population's Sensitivity (0.94, CI: [0.89–1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87–1.73]), higher (p < 0.001) than in the adult population (0.57, CI: [0.36–0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods (p < 0.001).

Conclusions: Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs.

Association between semiologic, autonomic, and electrographic seizure characteristics in children with generalized tonic-clonic seizures

INTRODUCTION

Generalized tonic-clonic seizures (GTCS) are associated with elevated electrodermal activity (EDA) and postictal generalized electroencephalographic suppression (PGES), markers that may indicate sudden unexpected death in epilepsy (SUDEP) risk. This study investigated the association of GTCS semiology, EDA, and PGES in children with epilepsy.

METHODS

Patients admitted to the Boston Children's Hospital long-term video-EEG monitoring unit wore a sensor that records EDA. We selected patients with at least one GTCS and reviewed video-EEGs for semiology, tonic and clonic phase duration, total clinical seizure duration, electrographic onset, offset, and PGES. We grouped patients into three semiology classes: GTCS 1: bilateral symmetric tonic arm extension, GTCS 2: no specific tonic arm extension or flexion, GTCS 3: unilateral or asymmetrical arm extension, tonic arm flexion or posturing that does not fit into GTCS 1 or 2. We analyzed the correlation between semiology, EDA, and PGES, and measured the area under the curve (AUC) of the ictal EDA (seizure onset to one hour after), subtracting baseline EDA (one-hour seizure-free before seizure onset). Using generalized estimating equation (GEE) and linear regression, we analyzed all seizures and single episodes per patient.

RESULTS

We included 30 patients (median age 13.8 ± 3.6 years, 46.7% females) and 53 seizures. With GEE, GTCS 1 was associated with longer PGES duration compared to GTCS 2 (Estimate (β) = -26.32 s, 95% Confidence Interval (CI): -36.46 to -16.18, p < 0.001), and the presence of PGES was associated with greater EDA change (β = 429604 μS, 95% CI: 3550.96 to 855657.04, p = 0.048). With single-episode analysis, GTCS 1 had greater EDA change than GTCS 2 ((β = -601339 μS, 95% CI: -1167016.56 to -35661.44, p = 0.047). EDA increased with PGES presence (β = 637500 μS, 95% CI: 183571.84 to 1091428.16, p = 0.01) and duration (β = 16794 μS, 95% CI: 5729.8 to 27858.2, p = 0.006). Patients with GTCS 1 had longer PGES duration compared to GTCS 2 (β = -30.53 s, 95% CI: -44.6 to -16.46, p < 0.001) and GTCS 3 (β = -22.07 s, 95% CI: -38.95 to -5.19, p = 0.016).

CONCLUSION

In children with epilepsy, PGES correlates with greater ictal EDA. GTCS 1 correlated with longer PGES duration and may indirectly correlate with greater ictal EDA. Our study suggests potential applications in monitoring and preventing SUDEP in these patients.

Syncope, Epilepsy and Ictal Asystole: A Case Series and Narrative Review

IMPORTANCE

Syncope is a common presentation to emergency departments, and cardiac and neurological aetiologies are the predominant causes. Ictal asystole is a rare cardio-neural phenomenon seen in epilepsy syndromes whereby a seizure causes asystole (≥3 s) leading to syncope.

OBSERVATIONS

We present three cases of ictal asystole, together with a narrative review of the literature to assess the prevalence of the condition and review the pathophysiology, diagnosis and management. Our review of the literature has shown that ictal asystole is an unlikely contributor to sudden unexplained death with epilepsy (SUDEP). Pacemaker insertion may limit morbidity from trauma related to syncopal episodes but does not impact mortality.

CONCLUSIONS AND RELEVANCE

Patients with ictal asystole should be diagnosed with concurrent electroencephalogram-electrocardiograph (EEG-ECG) monitoring, have their anti-epileptic drugs optimised and be considered for epilepsy surgery if feasible. The use of longer term ECG monitoring may be used as a diagnostic aid if ictal asystole is suspected. If there are ongoing syncopal episodes with associated ictal asystole ≥6 seconds, particularly despite medical therapy, a permanent pacemaker may be considered to reduce morbidity. Current guidelines should be updated to reflect the increasing knowledge of this condition.

[SUDEP in brief - knowledge and practice recommendations on sudden unexpected death in epilepsy]

Sudden unexpected death in epilepsy (SUDEP) is the sudden and unexpected death of an epilepsy patient, which occurs under benign circumstances without evidence of typical causes of death. SUDEP concerns all epilepsy patients. The individual risk depends on the characteristics of the epilepsy and seizures as well as on living conditions. Focal to bilateral and generalized tonic-clonic seizures (TCS), nocturnal seizures and lack of nocturnal supervision increase the risk. Most SUDEP cases are due to a fatal cascade of apnea, hypoxemia and asystole in the aftermath of a TCS. Two thirds of SUDEP cases in unsupervised epilepsy patients with TCS could probably be prevented. Wearables can detect TCS and alert caregivers. SUDEP information is desired by most patients and relatives, has a favorable impact on treatment adherence and behavior and has no negative effects on mood and quality of life.Recommendations of the committee on patient safety of the German Society of Epileptology: the ultimate treatment goal is seizure freedom. If this cannot be achieved, control of TCS should be sought. All epilepsy patients and their relatives should be informed about SUDEP and risk factors. Patients and relatives should be informed about measures to counteract the elevated risk and imminent SUDEP. The counselling should be performed during a face-to-face discussion, at the time of first diagnosis or during follow-up visits. The counselling should be documented. Wearables for TCS detection can be recommended. If TCS persist, therapeutic efforts should be continued. The bereaved should be contacted after a SUDEP.

Personalized Real-Time Federated Learning for Epileptic Seizure Detection

Epilepsy is one of the most prevalent paroxystic neurological disorders. It is characterized by the occurrence of spontaneous seizures. About 1 out of 3 patients have drug-resistant epilepsy, thus their seizures cannot be controlled by medication. Automatic detection of epileptic seizures can substantially improve the patient's quality of life. To achieve a high-quality model, we have to collect data from various patients in a central server. However, sending the patient's raw data to this central server puts patient privacy at risk and consumes a significant amount of energy. To address these challenges, in this work, we have designed and evaluated a standard federated learning framework in the context of epileptic seizure detection using a deep learning-based approach, which operates across a cluster of machines. We evaluated the accuracy and performance of our proposed approach on the NVIDIA Jetson Nano Developer Kit based on the EPILEPSIAE database, which is one of the largest public epilepsy datasets for seizure detection. Our proposed framework achieved a sensitivity of 81.25%, a specificity of 82.00%, and a geometric mean of 81.62%. It can be implemented on embedded platforms that complete the entire training process in 1.86 hours using 344.34 mAh energy on a single battery charge. We also studied a personalized variant of the federated learning, where each machine is responsible for training a deep neural network (DNN) to learn the discriminative electrocardiography (ECG) features of the epileptic seizures of the specific person monitored based on its local data. In this context, the DNN benefitted from a well-trained model without sharing the patient's raw data with a server or a central cloud repository. We observe in our results that personalized federated learning provides an increase in all the performance metric, with a sensitivity of 90.24%, a specificity of 91.58%, and a geometric mean of 90.90%.

Wearable Health Devices in Health Care: Narrative Systematic Review

BACKGROUND

With the rise of mobile medicine, the development of new technologies such as smart sensing, and the popularization of personalized health concepts, the field of smart wearable devices has developed rapidly in recent years. Among them, medical wearable devices have become one of the most promising fields. These intelligent devices not only assist people in pursuing a healthier lifestyle but also provide a constant stream of health care data for disease diagnosis and treatment by actively recording physiological parameters and tracking metabolic status. Therefore, wearable medical devices have the potential to become a mainstay of the future mobile medical market.

OBJECTIVE

Although previous reviews have discussed consumer trends in wearable electronics and the application of wearable technology in recreational and sporting activities, data on broad clinical usefulness are lacking. We aimed to review the current application of wearable devices in health care while highlighting shortcomings for further research. In addition to daily health and safety monitoring, the focus of our work was mainly on the use of wearable devices in clinical practice.

METHODS

We conducted a narrative review of the use of wearable devices in health care settings by searching papers in PubMed, EMBASE, Scopus, and the Cochrane Library published since October 2015. Potentially relevant papers were then compared to determine their relevance and reviewed independently for inclusion.

RESULTS

A total of 82 relevant papers drawn from 960 papers on the subject of wearable devices in health care settings were qualitatively analyzed, and the information was synthesized. Our review shows that the wearable medical devices developed so far have been designed for use on all parts of the human body, including the head, limbs, and torso. These devices can be classified into 4 application areas: (1) health and safety monitoring, (2) chronic disease management, (3) disease diagnosis and treatment, and (4) rehabilitation. However, the wearable medical device industry currently faces several important limitations that prevent further use of wearable technology in medical practice, such as difficulties in achieving user-friendly solutions, security and privacy concerns, the lack of industry standards, and various technical bottlenecks.

CONCLUSIONS

We predict that with the development of science and technology and the popularization of personalized health concepts, wearable devices will play a greater role in the field of health care and become better integrated into people's daily lives. However, more research is needed to explore further applications of wearable devices in the medical field. We hope that this review can provide a useful reference for the development of wearable medical devices.

Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning

Currently, the tracking of seizures is highly subjective, dependent on qualitative information provided by the patient and family instead of quantifiable seizure data. Usage of a seizure detection device to potentially detect seizure events in a population of epilepsy patients has been previously done. Therefore, we chose the Fitbit Charge 2 smart watch to determine if it could detect seizure events in patients when compared to continuous electroencephalographic (EEG) monitoring for those admitted to an epilepsy monitoring unit. A total of 40 patients were enrolled in the study that met the criteria between 2015 and 2016. All seizure types were recorded. Twelve patients had a total of 53 epileptic seizures. The patient-aggregated receiver operating characteristic curve had an area under the curve of 0.58 [0.56, 0.60], indicating that the neural network models were generally able to detect seizure events at an above-chance level. However, the overall low specificity implied a false alarm rate that would likely make the model unsuitable in practice. Overall, the use of the Fitbit Charge 2 activity tracker does not appear well suited in its current form to detect epileptic seizures in patients with seizure activity when compared to data recorded from the continuous EEG.

A clinical evaluation of a novel algorithm in the reliable detection of epileptic seizures

PURPOSE

Undetected and prolonged epileptic seizures can result in hypoxic brain damage or death and occur most often when the victim is in bed alone or unsupervised. Sudden unexpected death in epilepsy may not always be preventable but it is believed that timely assistance with rescue medication and body re-positioning may overcome respiratory compromise in some cases. A novel algorithm based on a real time moving 9 s epoch, calculating 25 % percentage heart rate change and/or an oxygen saturation trigger level of <85 % was developed using photoplethysmography and incorporated into a prototype data storage device.

METHODS

The algorithm was clinically evaluated in this multicentre trial in the detection of clinically significant epileptic seizures. A range of epileptic seizures and normal physiological events were recorded and classified by reference standard EEG Videotelemetry and time-synchronised event data recorded by the prototype device incorporating the pre-specified cut-off points prospectively and retrospective analysis of all events.

RESULTS

119 participants who were attending electroencephalographic (EEG) videotelemetry as part of their clinical management of their epilepsy consented to take part in the trial. 683 epileptic seizures (77 clinically significant seizures) and 2648 normal physiological events were captured. When using pre-specified cut-off point 25 % heart rate change and/or oxygen desaturation <85 % on the basis of one/other, the device showed a sensitivity of 87 % for detecting clinically significant seizures. False Alarm Rate 4.5 (24 h FAR), detection latency of 58 s using heart rate percentage change.

CONCLUSIONS

The results indicate that the novel algorithm can be used in detecting clinically significant seizures.

New methodologies in ageing research

Ageing is arguably the most complex phenotype that occurs in humans. To understand and treat ageing as well as associated diseases, highly specialised technologies are emerging that reveal critical insight into the underlying mechanisms and provide new hope for previously untreated diseases. Herein, we describe the latest developments in cutting edge technologies applied across the field of ageing research. We cover emerging model organisms, high-throughput methodologies and machine-driven approaches. In all, this review will give you a glimpse of what will be pushing the field onwards and upwards.

Biomarkers of seizure severity derived from wearable devices

Besides triggering alarms, wearable seizure detection devices record a variety of biosignals that represent biomarkers of seizure severity. There is a need for automated seizure characterization, to identify high-risk seizures. Wearable devices can automatically identify seizure types with the highest associated morbidity and mortality (generalized tonic-clonic seizures), quantify their duration and frequency, and provide data on postictal position and immobility, autonomic changes derived from electrocardiography/heart rate variability, electrodermal activity, respiration, and oxygen saturation. In this review, we summarize how these biosignals reflect seizure severity, and how they can be monitored in the ambulatory outpatient setting using wearable devices. Multimodal recording of these biosignals will provide valuable information for individual risk assessment, as well as insights into the mechanisms and prevention of sudden unexpected death in epilepsy.

Changing characteristics of epilepsy interventional clinical trials over the last decade: Clinicaltrials.Gov registry

INTRODUCTION

Epilepsy affects about 1% of the world's population (over 50 million). Of these, one-third have refractory or medication-resistant epilepsy. This group of people drives the development and testing of new interventions for epilepsy. To better address the needs of people with epilepsy, the characteristics of clinical trials, as well as the gaps in the population of interest, need to be evaluated.

METHODS

We searched the www.ClinicalTrials.gov database using the keywords "seizure" or "epilepsy" between 9/1/2008-9/1/2018 and filtering for Interventional Clinical trials. The data were categorized by three equal time intervals (tertiles), and evaluated by type of intervention (behavioral, diet, device, drug, other), primary purpose (treatment, diagnosis, prevention, or basic science), gender, age, phase (Phase1 to Phase 4 trials), length and status of the study, enrollment/recruitment/randomization, location, blinding status, assignment group (single/parallel/crossover/factorial/sequential), and funding. We focused on drugs and devices and used a binary logistic regression model to analyze the role of time, length of study, funding, location, randomization, and age.

RESULTS

We found 359 epilepsy clinical trials; of these, 245 (68.2%) clinical trials involved drugs, and 55 (15.3%) were device trials. Over the three tertiles, the percentage of device trials increased while medication trials decreased. Device:drug trial odds ratios increased six times by the third tertile. Also, the results showed that clinical trials for drugs and devices occurred more in adults than children. Industry funding decreased 20% over time. The US contribution to clinical research was stable, but device trials were more likely to occur outside of the US.

CONCLUSION

Drugs constitute the substantial fields of interventional trials in epilepsy but decreased in proportion over the last decade, while the presence of the device trials steadily increased. Device trials focused on treatment and diagnosis of seizures and have been more invested in non-US countries.

Ictal quantitative surface electromyography correlates with postictal EEG suppression

OBJECTIVE

To test the hypothesis that neurophysiologic biomarkers of muscle activation during convulsive seizures reveal seizure severity and to determine whether automatically computed surface EMG parameters during seizures can predict postictal generalized EEG suppression (PGES), indicating increased risk for sudden unexpected death in epilepsy. Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures. Our goal was to use quantitative EMG measurements for seizure characterization and risk assessment.

METHODS

Quantitative parameters were computed from surface EMGs recorded during convulsive seizures from deltoid and brachial biceps muscles in patients admitted to long-term video-EEG monitoring. Parameters evaluated were the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, and the dynamics of their evolution (slope). We compared them with the duration of the PGES.

RESULTS

We found significant correlations between quantitative surface EMG parameters and the duration of PGES (p < 0.001). Stepwise multiple regression analysis identified as independent predictors in deltoid muscle the duration of the clonic phase and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. The surface EMG-based algorithm identified seizures at increased risk (PGES ≥20 seconds) with an accuracy of 85%.

CONCLUSIONS

Ictal quantitative surface EMG parameters correlate with PGES and may identify seizures at high risk.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that during convulsive seizures, surface EMG parameters are associated with prolonged postictal generalized EEG suppression.

Seizure detection using heart rate variability: A prospective validation study

Although several validated seizure detection algorithms are available for convulsive seizures, detection of nonconvulsive seizures remains challenging. In this phase 2 study, we have validated a predefined seizure detection algorithm based on heart rate variability (HRV) using patient-specific cutoff values. The validation data set was independent from the previously published data set. Electrocardiography (ECG) was recorded using a wearable device (ePatch) in prospectively recruited patients. The diagnostic gold standard was inferred from video-EEG monitoring. Because HRV-based seizure detection is suitable only for patients with marked ictal autonomic changes, we defined responders as the patients who had a>50 beats/min ictal change in heart rate. Eleven of the 19 included patients with seizures (57.9%) fulfilled this criterion. In this group, the algorithm detected 20 of the 23 seizures (sensitivity: 87.0%). The algorithm detected all but one of the 10 recorded convulsive seizures and all of the 8 focal impaired awareness seizures, and it missed 2 of the 4 focal aware seizures. The median sensitivity per patient was 100% (in nine patients all seizures were detected). The false alarm rate was 0.9/24 h (0.22/night). Our results suggest that HRV-based seizure detection has high performance in patients with marked autonomic changes.

Wearable devices for seizure detection: Is it time to translate into our clinical practice?

With the exponential development of mobile health technologies over the past ten years, there has been a growing interest in the potential applications in the field of epilepsy, and specifically for seizure detection. Better detection of seizures is probably one of the best ways to improve patient safety. Overall, we are observing an exponential increase in the number of non-EEG based seizure detection systems and a progressive homogenization of their evaluation procedures. Most importantly, the properties of these devices for detection of tonic-clonic seizures are now very interesting, both in terms of sensitivity and in terms of false-alarm rates. Accordingly, we might expect that these be used in clinical practice in the near future, especially in patients at high risk of seizure-related injuries or sudden unexpected death in epilepsy (SUDEP).

Prevention of sudden unexpected death in epilepsy: current status and future perspectives

Introduction: Sudden unexpected death in epilepsy (SUDEP) affects about 1 in 1000 people with epilepsy, and even more in medically refractory epilepsy. As most people are between 20 and 40 years when dying suddenly, SUDEP leads to a considerable loss of potential life years. The most important risk factors are nocturnal and tonic-clonic seizures, underscoring that supervision and effective seizure control are key elements for SUDEP prevention. The question of whether specific antiepileptic drugs are linked to SUDEP is still controversially discussed. Knowledge and education about SUDEP among health-care professionals, patients, and relatives are of outstanding importance for preventive measures to be taken, but still poor and widely neglected.Areas covered: This article reviews epidemiology, pathophysiology, risk factors, assessment of individual SUDEP risk and available measures for SUDEP prevention. Literature search was done using Medline and Pubmed in October 2019.Expert opinion: Significant advances in the understanding of SUDEP were made in the last decade which allow testing of novel strategies to prevent SUDEP. Promising current strategies target neuronal mechanisms of brain stem dysfunction, cardiac susceptibility for fatal arrhythmias, and reliable detection of tonic-clonic seizures using mobile health technologies.Abbreviations: AED, antiepileptic drug; CBZ, carbamazepine; cLQTS, congenital long QT syndrome; EMU, epilepsy monitoring unit; FBTCS, focal to bilateral tonic-clonic seizures; GTCS, generalized tonic-clonic seizures; ICA, ictal central apnea; LTG, lamotrigine; PCCA, postconvulsive central apnea; PGES, postictal generalized EEG suppression; SRI, serotonin reuptake inhibitor; SUDEP, sudden unexpected death in epilepsy; TCS, tonic-clonic seizures.

Treatments for the prevention of Sudden Unexpected Death in Epilepsy (SUDEP)

BACKGROUND

This is an updated version of the original Cochrane Review, published in 2016, Issue 7. Sudden Unexpected Death in Epilepsy (SUDEP) is defined as sudden, unexpected, witnessed or unwitnessed, non-traumatic or non-drowning death of people with epilepsy, with or without evidence of a seizure, excluding documented status epilepticus and in whom postmortem examination does not reveal a structural or toxicological cause for death. SUDEP has a reported incidence of 1 to 2 per 1000 patient-years and represents the most common epilepsy-related cause of death. The presence and frequency of generalised tonic-clonic seizures (GTCS), male sex, early age of seizure onset, duration of epilepsy, and polytherapy are all predictors of risk of SUDEP. The exact pathophysiology of SUDEP is currently unknown, although GTCS-induced cardiac, respiratory, and brainstem dysfunction appears likely. Appropriately chosen antiepileptic drug treatment can render around 70% of patients free of all seizures. However, around one-third will remain drug-resistant despite polytherapy. Continuing seizures place patients at risk of SUDEP, depression, and reduced quality of life. Preventative strategies for SUDEP include reducing the occurrence of GTCS by timely referral for presurgical evaluation in people with lesional epilepsy and advice on lifestyle measures; detecting cardiorespiratory distress through clinical observation and seizure, respiratory, and heart rate monitoring devices; preventing airway obstruction through nocturnal supervision and safety pillows; reducing central hypoventilation through physical stimulation and enhancing serotonergic mechanisms of respiratory regulation using selective serotonin reuptake inhibitors (SSRIs); and reducing adenosine and endogenous opioid-induced brain and brainstem depression.

OBJECTIVES

To assess the effectiveness of interventions in preventing SUDEP in people with epilepsy by synthesising evidence from randomised controlled trials of interventions and cohort and case-control non-randomised studies.

SEARCH METHODS

For the latest update we searched the following databases without language restrictions: Cochrane Register of Studies (CRS Web, 4 February 2019); MEDLINE (Ovid, 1946 to 1 February 2019); SCOPUS (1823 to 4 February 2019); PsycINFO (EBSCOhost, 1887 to 4 January 2019); CINAHL Plus (EBSCOhost, 1937 to 4 February 2019); ClinicalTrials.gov (5 February 2019); and the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP, 5 February 2019). We checked the reference lists of retrieved studies for additional reports of relevant studies and contacted lead study authors for any relevant unpublished material. We identified any grey literature studies published in the last five years by searching: Zetoc database; ISI Proceedings; International Bureau for Epilepsy (IBE) congress proceedings database; International League Against Epilepsy (ILAE) congress proceedings database; abstract books of symposia and congresses, meeting abstracts, and research reports.

SELECTION CRITERIA

We aimed to include randomised controlled trials (RCTs), quasi-RCTs, and cluster-RCTs; prospective non-randomised cohort controlled and uncontrolled studies; and case-control studies of adults and children with epilepsy receiving an intervention for the prevention of SUDEP. Types of interventions included: early versus delayed pre-surgical evaluation for lesional epilepsy; educational programmes; seizure-monitoring devices; safety pillows; nocturnal supervision; selective serotonin reuptake inhibitors (SSRIs); opiate antagonists; and adenosine antagonists.

DATA COLLECTION AND ANALYSIS

We aimed to collect data on study design factors and participant demographics for included studies. The primary outcome of interest was the number of deaths from SUDEP. Secondary outcomes included: number of other deaths (unrelated to SUDEP); change in mean depression and anxiety scores (as defined within the study); clinically important change in quality of life, that is any change in quality of life score (average and endpoint) according to validated quality of life scales; and number of hospital attendances for seizures.

MAIN RESULTS

We identified 1277 records from the databases and search strategies. We found 10 further records by searching other resources (handsearching). We removed 469 duplicate records and screened 818 records (title and abstract) for inclusion in the review. We excluded 785 records based on the title and abstract and assessed 33 full-text articles. We excluded 29 studies: eight studies did not assess interventions to prevent SUDEP; eight studies were review articles, not clinical studies; five studies measured sensitivity of devices to detect GTCS but did not directly measure SUDEP; six studies assessed risk factors for SUDEP but not interventions for preventing SUDEP; and two studies did not have a control group. We included one cohort study and three case-control studies of serious to critical risk of bias. The 6-month prospective cohort study observed no significant effect of providing patients with SUDEP information on drug compliance and quality of life, anxiety and depression levels. The study was too short and with no deaths observed in either group to determine a protective effect. Two case control studies reported a protective effect for nocturnal supervision against SUDEP. However due to significant heterogeneity, the results could not be combined in meta-analysis. One study of 154 SUDEP cases and 616 controls reported an unadjusted odds ratio (OR) of 0.34 (95% CI 0.22 to 0.53; P < 0.0001). The same study demonstrated the protective effect was independent of seizure control, suggesting that nocturnal supervision is not just a surrogate marker of seizure control. The second case-control study of 48 SUDEP cases and 220 controls reported an unadjusted OR of 0.08 (95% CI 0.02 to 0.27; P < 0.0001). The third case-control study of residential care centre patients who were already receiving physical checks more than 15 minutes apart throughout the night did not report any protective effect for additional nocturnal supervision (physical checks < 15 minutes apart; use of listening devices; dormitory setting; and use of bed sensors). However the same study did ascertain a difference between centres: the residential centre with the lowest level of supervision had the highest incidence of SUDEP. The case-control studies did not report on quality of life or depression and anxiety scores.

AUTHORS' CONCLUSIONS

We found limited, very low-certainty evidence that supervision at night reduces the incidence of SUDEP. Further research is required to identify the effectiveness of other current interventions - for example seizure detection devices, safety pillows, SSRIs, early surgical evaluation, educational programmes, and opiate and adenosine antagonists - in preventing SUDEP in people with epilepsy.

Sounds of seizures

PURPOSE

A phase I feasibility study to determine the accuracy of identifying seizures based on audio recordings.

METHODS

We systematically generated 166 audio clips of 30 s duration from 83 patients admitted to an epilepsy monitoring unit between 1/2015 and 12/2016, with one clip during a seizure period and one clip during a non-seizure control period for each patient. Five epileptologists performed a blinded review of the audio clips and rated whether a seizure occurred or not, and indicated the confidence level (low or high) of their rating. The accuracy of individual and consensus ratings were calculated.

RESULTS

The overall performance of the consensus rating between the five epileptologists showed a positive predictive value (PPV) of 0.91 and a negative predictive value (NPV) of 0.66. The performance improved when confidence was high (PPV of 0.96, NPV of 0.70). The agreement between the epileptologists was moderate with a kappa of 0.584. Hyperkinetic (PPV 0.92, NPV 0.86) and tonic-clonic (PPV and NPV 1.00) seizures were most accurately identified. Seizures with automatisms only and non-motor seizures could not be accurately identified. Specific seizure-related sounds associated with accurate identification included disordered breathing (PPV and NPV 1.00), rhythmic sounds (PPV 0.93, NPV 0.80), and ictal vocalizations (PPV 1.00, NPV 0.97).

CONCLUSION

This phase I feasibility study shows that epileptologists are able to accurately identify certain seizure types from audio recordings when the seizures produce sounds. This provides guidance for the development of audio-based seizure detection devices and demonstrate which seizure types could potentially be detected.

Prone, lateral, or supine positioning at seizure onset determines the postictal body position: A multicenter video-EEG monitoring cohort study

PURPOSE

Most patients who die from sudden unexpected death in epilepsy (SUDEP) are found in the prone position. We evaluated whether changes in body position occur during generalized convulsive seizures (GCSs).

METHOD

GCSs in patients undergoing video-EEG-monitoring between 2007 and 2017 at epilepsy centers in Frankfurt and Marburg were analyzed in relation to changes in body position.

RESULTS

A total of 494 GCSs were analyzed among 327 patients. At seizure onset, positions included supine (48.2 %), right lateral (19.0 %), left lateral (15.6 %), sitting or standing (14.0 %), and prone (3.2 %). Between seizure onset and the start of generalization, 57.5 % of participants altered body positions. During four seizures, patients adopted a prone position, while, in five seizures, patients moved from a prone position. Patients who experienced GCS onset while in a nonprone position had a 2.1 % risk of entering the prone position by the end of their seizure. In contrast, 56.2 % of those in an initial prone position remained so at the end of the GCS, with an odds ratio for maintaining that position of 60.2 (95 % confidence interval: 29.1-124.3; p < 0.001). The likelihood of ending up in the prone position post-GCS did not vary among patients with different nonprone starting positions (p = 0.147).

CONCLUSIONS

Seizures in prone position occur during sleep and the highest risk for postictal prone positioning appears to be being in the prone position at GCS onset. Epilepsy patients should therefore be advised to go to sleep in a supine or lateral position to reduce their SUDEP risk.

Risks and predictive biomarkers of sudden unexpected death in epilepsy patient

PURPOSE OF REVIEW

The current review updates our knowledge regarding sudden unexpected death in epilepsy patient (SUDEP) risks, risk factors, and investigations of putative biomarkers based on suspected mechanisms of SUDEP.

RECENT FINDINGS

The overall incidence of SUDEP in adults with epilepsy is 1.2/1000 patient-years, with surprisingly comparable figures in children in recently published population-based studies. This risk was found to decrease over time in several cohorts at a rate of -7% per year, for unknown reasons. Well established risk factors include frequency of generalized tonic-clonic seizures, while adding antiepileptic treatment, nocturnal supervision and use of nocturnal listening device appear to be protective. In contrast, recent data failed to demonstrate the predictive value of heart rate variability, periictal cardiorespiratory dysfunction, and postictal generalized electroencephalography suppression. Preliminary findings suggest that brainstem and thalamic atrophy may be associated with a higher risk of SUDEP. Novel experimental and human data support the primary role of generalized tonic-clonic seizure-triggered respiratory dysfunction and the likely contribution of altered brainstem serotoninergic neurotransmission, in SUDEP pathophysiology.

SUMMARY

Although significant progress has been made during the past year in the understanding of SUDEP mechanisms and investigation of numerous potential biomarkers, we are still missing reliable predictors of SUDEP beyond the well established clinical risk factors.

Clinical risk factors in SUDEP: A nationwide population-based case-control study

Objective

We conducted a nationwide case-control study in Sweden to test the hypothesis that specific clinical characteristics are associated with increased risk of sudden unexpected death in epilepsy (SUDEP).

Methods

The study included 255 SUDEP cases (definite and probable) and 1,148 matched controls. Clinical information was obtained from medical records and the National Patient Register. The association between SUDEP and potential risk factors was assessed by odds ratios (ORs) and 95% confidence intervals (CIs) and interaction assessed by attributable proportion due to interaction (AP).

Results

Experiencing generalized tonic-clonic seizures (GTCS) during the preceding year was associated with a 27-fold increased risk (OR 26.81, 95% CI 14.86–48.38), whereas no excess risk was seen in those with exclusively non-GTCS seizures (OR 1.15, 95% CI 0.54–48.38). The presence of nocturnal GTCS during the last year of observation was associated with a 15-fold risk (OR 15.31, 95% CI 9.57–24.47). Living alone was associated with a 5-fold increased risk of SUDEP (OR 5.01, 95% CI 2.93–8.57) and interaction analysis showed that the combination of not sharing a bedroom and having GTCS conferred an OR of 67.10 (95% CI 29.66–151.88), with AP estimated at 0.69 (CI 0.53–0.85). Among comorbid diseases, a previous diagnosis of substance abuse or alcohol dependence was associated with excess risk of SUDEP.

Conclusions

Individuals with GTCS who sleep alone have a dramatically increased SUDEP risk. Our results indicate that 69% of SUDEP cases in patients who have GTCS and live alone could be prevented if the patients were not unattended at night or were free from GTCS.