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.

Towards user-centered design of medical devices for SUDEP prediction and prevention: Insights from persons with epilepsy and caregivers.

OBJECTIVES: As epilepsy management medical devices emerge as potential technological solutions for prediction and prevention of sudden death in epilepsy (SUDEP), there is a gap in understanding the features and priorities that should be included in the design of these devices. This study aims to bridge the gap between current technology and emerging needs by leveraging insights from persons with epilepsy (PWE) and caregivers (CG) on current epilepsy management devices and understanding how SUDEP awareness influences preferences and design considerations for potential future solutions. METHODS: Two cross-sectional surveys were designed to survey PWE and CG on medical device design features, SUDEP awareness, and participation in medical device research. Data analysis included both qualitative thematic analysis and quantitative statistical analysis. RESULTS: The survey revealed that among 284 responses, CG were more aware of SUDEP than PWE. Comfort was identified as the primary concern regarding wearable medical devices for epilepsy management with significant differences between PWE and CG regarding acceptance and continuous use preferences. The thematic analysis identified integration with daily life, aesthetic and emotional resonance, adaptability to seizure characteristics, and user-centric design specifications as crucial factors to be considered for enhanced medical device adoption. The integration of a companion app is seen as an important tool to enhance communication and data sharing. DISCUSSION: This study reveals that while SUDEP awareness can promote the development of future SUDEP predictive and preventive medical devices, these should be designed to mitigate its impact on daily life and anxiety of both PWE and CG. Comfort and acceptance are seen as key priorities to support continuous use and are seen as a technical requirement of future medical devices for SUDEP prediction and prevention. Widespread adoption requires these technologies to be customizable to adapt to different lifestyles and social situations. A holistic approach should be used in the design of future medical devices to capture several dimensions of PWE and CG epilepsy management journey and uphold communication between healthcare professionals, PWE and CG. CONCLUSION: Data from this study highlight the importance of considering user preferences and experiences in the design of epilepsy management medical devices with potential applicability for SUDEP prediction and prevention. By employing user-centered design methods this research provides valuable insights to inform the development of future SUDEP prediction and prevention devices.

A systematic review of the literature reporting on remote monitoring epileptic seizure detection devices.

BACKGROUND: Early detection and alert notification of an impending seizure for people with epilepsy have the potential to reduce Sudden Unexpected Death in Epilepsy (SUDEP). Current remote monitoring seizure detection devices for people with epilepsy are designed to support real-time monitoring of their vital health parameters linked to seizure alert notification. An understanding of the rapidly growing literature on remote seizure detection devices is essential to address the needs of people with epilepsy and their carers. AIM: This review aims to examine the technical characteristics, device performance, user preference, and effectiveness of remote monitoring seizure detection devices. METHODOLOGY: A systematic review referenced to PRISMA guidelines was used. RESULTS: A total of 1095 papers were identified from the initial search with 30 papers included in the review. Sixteen non-invasive remote monitoring seizure detection devices are currently available. Such seizure detection devices were found to have inbuilt intelligent sensor functionality to monitor electroencephalography, muscle movement, and accelerometer-based motion movement for detecting seizures remotely. Current challenges of these devices for people with epilepsy include skin irritation due to the type of patch electrode used and false alarm notifications, particularly during physical activity. The tight-fitted accelerometer-type devices are reported as uncomfortable from a wearability perspective for long-term monitoring. Also, continuous recording of physiological signals and triggering alert notifications significantly reduce the battery life of the devices. The literature highlights that 3.2 out of 5 people with epilepsy are not using seizure detection devices because of the cost and appearance of the device. CONCLUSION: Seizure detection devices can potentially reduce morbidity and mortality for people with epilepsy. Therefore, further collaboration of clinicians, technical experts, and researchers is needed for the future development of these devices. Finally, it is important to always take into consideration the expectations and requirements of people with epilepsy and their carers to facilitate the next generation of remote monitoring seizure detection devices.

The cost-effectiveness of a real-time seizure detection application for people with epilepsy.

OBJECTIVES: Automated seizure detection modalities can increase safety among people with epilepsy (PWE) and reduce seizure-related anxiety. We evaluated the potential cost-effectiveness of a seizure detection mobile application for PWE in Singapore. METHODS: We used a Markov cohort model to estimate the expected changes to total costs and health outcomes from a decision to adopt the seizure detection application versus the current standard of care from the health provider perspective. The time horizon is ten years and cycle duration is one month. Parameter values were updated from national databases and published literature. As we do not know the application efficacy in reducing seizure-related injuries, a conservative estimate of 1% reduction was used. Probabilistic sensitivity analysis, scenario analyses, and value of information analysis were performed. RESULTS: At a willingness-to-pay of $45,000/ quality-adjusted life-years (QALY), the incremental cost-effectiveness ratio was $1,096/QALY, and the incremental net monetary benefit was $13,656. Probabilistic sensitivity analyses reported that the application had a 99.5% chance of being cost-effective. In a scenario analysis in which the reduction in risk of seizure-related injury was 20%, there was a 99.8% chance that the application was cost-effective. Value of information analysis revealed that health utilities was the most important parameter group contributing to model uncertainty. CONCLUSIONS: This early-stage modeling study reveals that the seizure detection application is likely to be cost-effective compared to current standard of care. Future prospective trials will be needed to demonstrate the real-world impact of the application. Changes in health-related quality of life should also be measured in future trials.

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.

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.

Tonic-clonic seizure detection using accelerometry-based wearable sensors: A prospective, video-EEG controlled study.

PURPOSE: The aim of this prospective, video-electroencephalography (video-EEG) controlled study was to evaluate the performance of an accelerometry-based wearable system to detect tonic-clonic seizures (TCSs) and to investigate the accuracy of different seizure detection algorithms using separate training and test data sets. METHODS: Seventy-five epilepsy surgery candidates undergoing video-EEG monitoring were included. The patients wore one three-axis accelerometer on each wrist during video-EEG. The accelerometer data was band-pass filtered and reduced using a movement threshold and mapped to a time-frequency feature space representation. Algorithms based on standard binary classifiers combined with a TCS specific event detection layer were developed and trained using the training set. Their performance was evaluated in terms of sensitivity and false positive (FP) rate using the test set. RESULTS: Thirty-seven available TCSs in 11 patients were recorded and the data was divided into disjoint training (27 TCSs, three patients) and test (10 TCSs, eight patients) data sets. The classification algorithms evaluated were K-nearest-neighbors (KNN), random forest (RF) and a linear kernel support vector machine (SVM). For the TCSs detection performance of the three algorithms in the test set, the highest sensitivity was obtained for KNN (100% sensitivity, 0.05 FP/h) and the lowest FP rate was obtained for RF (90% sensitivity, 0.01 FP/h). CONCLUSIONS: The low FP rate enhances the clinical utility of the detection system for long-term reliable seizure monitoring. It also allows a possible implementation of an automated TCS detection in free-living environment, which could contribute to ascertain seizure frequency and thereby better seizure management.

Sudden unexpected death in epilepsy: measures to reduce risk.

This review looks at the strategies that may help to reduce the risk of sudden unexpected death in epilepsy beyond that of trying to achieve seizure cessation, which is not possible for up to 30% of patients with epilepsy. These strategies include seizure safety checklists, mobile phone technology, telehealth and various devices currently available or in development. We highlight interventions where there is evidence of benefit, and draw attention for the need both to involve patients with epilepsy in risk reduction and to improve communication with those at risk.

Safe and sound? A systematic literature review of seizure detection methods for personal use.

PURPOSE: The study aims to review systematically the quality of evidence supporting seizure detection devices. The unpredictable nature of seizures is distressing and disabling for sufferers and carers. If a seizure can be reliably detected then the patient or carer could be alerted. It could help prevent injury and death. METHODS: A literature search was completed. Forty three of 120 studies found using relevant search terms were suitable for systematic review which was done applying pre-agreed criteria using PRISMA guidelines. The papers identified and reviewed were those that could have potential for everyday use of patients in a domestic setting. Studies involving long term use of scalp electrodes to record EEG were excluded on the grounds of unacceptable restriction of daily activities. RESULTS: Most of the devices focused on changes in movement and/or physiological signs and were dependent on an algorithm to determine cut off points. No device was able to detect all seizures and there was an issue with both false positives and missed seizures. Many of the studies involved relatively small numbers of cases or report on only a few seizures. Reports of seizure alert dogs are also considered. CONCLUSION: Seizure detection devices are at a relatively early stage of development and as yet there are no large scale studies or studies that compare the effectiveness of one device against others. The issue of false positive detection rates is important as they are disruptive for both the patient and the carer. Nevertheless, the development of seizure detection devices offers great potential in the management of epilepsy.

Electromyography-based seizure detector: Preliminary results comparing a generalized tonic-clonic seizure detection algorithm to video-EEG recordings.

OBJECTIVE: Automatic detection of generalized tonic-clonic seizures (GTCS) will facilitate patient monitoring and early intervention to prevent comorbidities, recurrent seizures, or death. Brain Sentinel (San Antonio, Texas, USA) developed a seizure-detection algorithm evaluating surface electromyography (sEMG) signals during GTCS. This study aims to validate the seizure-detection algorithm using inpatient video-electroencephalography (EEG) monitoring. METHODS: sEMG was recorded unilaterally from the biceps/triceps muscles in 33 patients (17white/16 male) with a mean age of 40 (range 14-64) years who were admitted for video-EEG monitoring. Maximum voluntary biceps contraction was measured in each patient to set up the baseline physiologic muscle threshold. The raw EMG signal was recorded using conventional amplifiers, sampling at 1,024 Hz and filtered with a 60 Hz noise detection algorithm before it was processed with three band-pass filters at pass frequencies of 3-40, 130-240, and 300-400 Hz. A seizure-detection algorithm utilizing Hotelling's T-squared power analysis of compound muscle action potentials was used to identify GTCS and correlated with video-EEG recordings. RESULTS: In 1,399 h of continuous recording, there were 196 epileptic seizures (21 GTCS, 96 myoclonic, 28 tonic, 12 absence, and 42 focal seizures with or without loss of awareness) and 4 nonepileptic spells. During retrospective, offline evaluation of sEMG from the biceps alone, the algorithm detected 20 GTCS (95%) in 11 patients, averaging within 20 s of electroclinical onset of generalized tonic activity, as identified by video-EEG monitoring. Only one false-positive detection occurred during the postictal period following a GTCS, but false alarms were not triggered by other seizure types or spells. SIGNIFICANCE: Brain Sentinel's seizure detection algorithm demonstrated excellent sensitivity and specificity for identifying GTCS recorded in an epilepsy monitoring unit. Further studies are needed in larger patient groups, including children, especially in the outpatient setting.