Multimodal nocturnal seizure detection in a residential care setting: A long-term prospective trial

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.

Commercially available seizure detection devices: A systematic review

IMPORTANCE

Epilepsy can be associated with significant morbidity and mortality. Seizure detection devices could be invaluable tools for both people with epilepsy, their caregivers, and clinicians as they could alert caretakers about seizures, reduce the risk of sudden unexpected death in epilepsy, and provide objective and more reliable seizure tracking to guide treatment decisions or monitor outcomes in clinical trials.

OBJECTIVE

To synthesize the characteristics of commercial seizure detection tools/devices currently available.

METHODS

We performed a systematic search utilizing a diverse set of resources to identify commercially available seizure detection products for consumer use. Performance data was obtained through a systematic review on commercially available products.

OBSERVATIONS

We identified 23 products marketed for seizure detection/alerting. Devices utilize a variety of mechanisms to detect seizures, including movement detectors, autonomic change detectors, electroencephalogram (EEG) based detectors, and other mechanisms (audio). The optimal device for a person with epilepsy depends on a variety of factors including the main purpose of the device, their age, seizure type and personal preferences. Only 8 devices have published peer-reviewed performance data and the majority for tonic-clonic seizures. An informed conversation between the clinician and the patient can help guide if a seizure detection device is appropriate.

CONCLUSIONS AND RELEVANCE

Seizure detection devices have a potential to reduce morbidity and mortality for certain people with epilepsy. Clinicians should be familiar with the characteristics of commercially available devices to best counsel their patients on whether a seizure detection device may be beneficial and what the optimal devices may be.

Identification of Ictal Tachycardia in Focal Motor- and Non-Motor Seizures by Means of a Wearable PPG Sensor

Photoplethysmography (PPG) as an additional biosignal for a seizure detector has been underutilized so far, which is possibly due to its susceptibility to motion artifacts. We investigated 62 focal seizures from 28 patients with electrocardiography-based evidence of ictal tachycardia (IT). Seizures were divided into subgroups: those without epileptic movements and those with epileptic movements not affecting and affecting the extremities. PPG-based heart rate (HR) derived from a wrist-worn device was calculated for sections with high signal quality, which were identified using spectral entropy. Overall, IT based on PPG was identified in 37 of 62 (60%) seizures (9/19, 7/8, and 21/35 in the three groups, respectively) and could be found prior to the onset of epileptic movements affecting the extremities in 14/21 seizures. In 30/37 seizures, PPG-based IT was in good temporal agreement (<10 s) with ECG-based IT, with an average delay of 5.0 s relative to EEG onset. In summary, we observed that the identification of IT by means of a wearable PPG sensor is possible not only for non-motor seizures but also in motor seizures, which is due to the early manifestation of IT in a relevant subset of focal seizures. However, both spontaneous and epileptic movements can impair PPG-based seizure detection.

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.

Wearable devices for seizure detection: Practical experiences and recommendations from the Wearables for Epilepsy And Research (WEAR) International Study Group

The Wearables for Epilepsy And Research (WEAR) International Study Group identified a set of methodology standards to guide research on wearable devices for seizure detection. We formed an international consortium of experts from clinical research, engineering, computer science, and data analytics at the beginning of 2020. The study protocols and practical experience acquired during the development of wearable research studies were discussed and analyzed during bi-weekly virtual meetings to highlight commonalities, strengths, and weaknesses, and to formulate recommendations. Seven major essential components of the experimental design were identified, and recommendations were formulated about: (1) description of study aims, (2) policies and agreements, (3) study population, (4) data collection and technical infrastructure, (5) devices, (6) reporting results, and (7) data sharing. Introducing a framework of methodology standards promotes optimal, accurate, and consistent data collection. It also guarantees that studies are generalizable and comparable, and that results can be replicated, validated, and shared.

Performance of ECG-based seizure detection algorithms strongly depends on training and test conditions

Objective

To identify non‐EEG‐based signals and algorithms for detection of motor and non‐motor seizures in people lying in bed during video‐EEG (VEEG) monitoring and to test whether these algorithms work in freely moving people during mobile EEG recordings.

Methods

Data of three groups of adult people with epilepsy (PwE) were analyzed. Group 1 underwent VEEG with additional devices (accelerometry, ECG, electrodermal activity); group 2 underwent VEEG; and group 3 underwent mobile EEG recordings both including one‐lead ECG. All seizure types were analyzed. Feature extraction and machine‐learning techniques were applied to develop seizure detection algorithms. Performance was expressed as sensitivity, precision, F₁ score, and false positives per 24 hours.

Results

The algorithms were developed in group 1 (35 PwE, 33 seizures) and achieved best results (F₁ score 56%, sensitivity 67%, precision 45%, false positives 0.7/24 hours) when ECG features alone were used, with no improvement by including accelerometry and electrodermal activity. In group 2 (97 PwE, 255 seizures), this ECG‐based algorithm largely achieved the same performance (F₁ score 51%, sensitivity 39%, precision 73%, false positives 0.4/24 hours). In group 3 (30 PwE, 51 seizures), the same ECG‐based algorithm failed to meet up with the performance in groups 1 and 2 (F₁ score 27%, sensitivity 31%, precision 23%, false positives 1.2/24 hours). ECG‐based algorithms were also separately trained on data of groups 2 and 3 and tested on the data of the other groups, yielding maximal F₁ scores between 8% and 26%.

Significance

Our results suggest that algorithms based on ECG features alone can provide clinically meaningful performance for automatic detection of all seizure types. Our study also underscores that the circumstances under which such algorithms were developed, and the selection of the training and test data sets need to be considered and limit the application of such systems to unseen patient groups behaving in different conditions.

Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy, on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found moderate level of evidence for seizure types without GTCs or FBTCs. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

Wearables bei Epilepsien

Epileptische Anfälle führen zu verschiedensten körperlichen Symptomen, die je nach Art und Ausprägung mit geeigneten Geräten gemessen werden und als Surrogatmarker epileptischer Anfälle dienen können. Dominierende motorische Symptome können mit Beschleunigungssensoren oder elektromyografisch erfasst werden. Bei fokalen Anfällen mit fehlender oder geringer motorischer Beteiligung können autonome Phänomene wie Änderungen der Herzrate, Atmung und des elektrischen Hautwiderstandes per Elektrokardiografie, Photopletysmografie und Hautsensoren gemessen werden. Die in den heutigen Wearables integrierten Sensoren können diese Körpersignale messen und zur automatisierten Anfallserkennung nutzbar machen. In dieser Übersichtsarbeit werden verschiedene Sensortechnologien, Wearables und deren Anwendung zur automatisierten Erkennung epileptischer Anfälle vorgestellt, Gütekriterien zur Einschätzung mobiler Gesundheitstechnologien diskutiert und klinisch geprüfte Systeme zusammengefasst.

Wearable seizure detection devices in refractory epilepsy

Epilepsy affects 50 million patients and their caregivers worldwide. Devices that facilitate the detection of seizures can have a large influence on a patient's quality of life, therapeutic decisions and the conduct of clinical trials with anti-epileptic drugs. This article provides an up-to-date overview and comparison between wearable seizure detection devices (WSDDs), taking into account the newly proposed standards for testing and clinical validation of devices. 16 devices were included in our comparison. The F1-score, combining the device's accurate recall and precision, was calculated for each of these devices and used to evaluate their performance. The devices were separated by development phase and ranked by F1-score from highest to lowest. We describe 16 WSDDs: 6 of which were accelerometry (ACM)-based, 3 surface electromyography-based, 1 was a wearable application of EEG, 4 had multimodal sensors and 2 other types of sensors. We observed a significant inconsistency in the description of performance measures. The devices in the most advanced development phase with the highest F1-scores incorporated ACM- and sEMG-based sensors to detect tonic-clonic seizures. This review highlights the importance of implementing standards for an optimal comparison and, therefore, improving the research and development of WSDDs. WSDDs can improve the patient's care and quality of life, decrease seizure underreporting and they could potentially prevent sudden-unexpected-death in epilepsy. We discuss the central role of the neurologist in the use of WSDDs, and why a business to business to consumer model is better than the current business to consumer model of most WSDDs.

[Decision support systems, assistance systems and telemedicine in epileptology]

Hintergrund

Die wissenschaftlichen Erkenntnisse über Epilepsien und deren klinische Implikationen nehmen rasant zu. Für Nichtexperten stellt sich die zunehmende Herausforderung, den Überblick hierüber zu bewahren. Hier setzen Clinical-decision-support-Systeme (CDSS) an, indem sie standard- und expertengetriggertes Wissen zur Diagnostik und Therapie individualisiert und automatisiert liefern. Zudem sind Medizin-Apps und telemedizinische Verfahren zur Diagnostik und Therapie sowie Assistenzsysteme zur Anfallsdetektion bei Epilepsien verfügbar.

Ziel der Arbeit

Es soll ein Überblick über die aktuellen Entwicklungen und Anwendungsmöglichkeiten verfügbarer tele-epileptologischer Methoden gegeben werden.

Material und Methoden

Auf der Basis persönlicher Kenntnis und eines Literaturreviews werden epilepsiespezifische CDSS, Medizin-Apps, Assistenzsysteme sowie telemedizinische Anwendungen charakterisiert und deren klinische Einsatzmöglichkeiten dargestellt.

Ergebnisse und Diskussion

Personen mit Epilepsie könnten aufgrund des chronischen Verlaufs und der Komplexität der Erkrankung und ihrer Folgen von CDSS profitieren. Es erscheint wünschenswert, dass epilepsiespezifische CDSS sowohl für die Behandelnden als auch für Patienten nutzbar werden. Apps für Menschen mit Epilepsie dienen derzeit meist der Verlaufsdokumentation von Anfallsfrequenz, Medikamentencompliance und Nebenwirkungen. Gegenwärtige Anfallsdetektionssysteme erkennen vor allem generalisiert tonisch-klonische Anfälle (GTKA). Ein klinischer Nutzen ist noch nicht hinreichend belegt, erscheint aber wahrscheinlich, insbesondere da GTKA mit dem Risiko eines plötzlichen Todes von Epilepsiepatienten assoziiert sind und Interventionen als wirksam gelten.

Robust Epileptic Seizure Detection on Wearable Systems with Reduced False-Alarm Rate

Epilepsy affects more than 50 million people and ranks among the most common neurological diseases worldwide. Despite advances in treatment, one-third of patients still suffer from refractory epilepsy. Wearable devices for real-time patient monitoring can potentially improve the quality of life for such patients and reduce the mortality rate due to seizure-related accidents and sudden death in epilepsy. However, the majority of employed seizure detection techniques and devices suffer from unacceptable false-alarm rate. In this paper, we propose a robust seizure detection methodology for a wearable platform and validate it on the Physionet.org CHB-MIT Scalp EEG database. It reaches sensitivity of 0.966 and specificity of 0.925, and reducing the false-alarm rate by 34.7%. We also evaluate the battery lifetime of the wearable system including our proposed methodology and demonstrate the feasibility of using it in real time for up to 40.87 hours on a single battery charge.

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

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

Machine learning and wearable devices of the future

Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non-EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.

Photoplethysmographic evaluation of generalized tonic-clonic seizures

OBJECTIVE

Photoplethysmography (PPG) is an optical technique measuring variations of blood perfusion in peripheral tissues. We evaluated alterations in PPG signals in relationship to the occurrence of generalized tonic-clonic seizures (GTCSs) in patients with epilepsy to evaluate the feasibility of seizure detection.

METHODS

During electroencephalographic (EEG) long-term monitoring, patients wore portable wristband sensor(s) on their wrists or ankles recording PPG signals. We analyzed PPG signals during three time periods, which were defined with respect to seizures detected on EEG: (1) baseline (>30 minutes prior to seizure), (2) preseizure period, and (3) postseizure period. Furthermore, we selected five random control segments during seizure-free periods. PPG features, including frequency, amplitude, duration, slope, smoothness, and area under the curve, were automatically calculated. We used a linear mixed-effect model to evaluate changes in PPG features between different time periods in an attempt to identify signal changes that detect seizures.

RESULTS

We prospectively enrolled 174 patients from the epilepsy monitoring unit at Boston Children's Hospital. Twenty-five GTCSs were recorded from 13 patients. Data from the first recorded GTCS of each patient were included in the analysis. We observed an increase in PPG frequency during pre- and postseizure periods that was higher than the changes during seizure-free periods (frequency increase: preseizure = 0.22 Hz, postseizure = 0.58 Hz vs changes during seizure-free period = 0.05 Hz). The PPG slope decreased significantly by 56.71 nW/s during preseizure periods compared to seizure-free periods. Additionally, the smoothness increased significantly by 0.22 nW/s during the postseizure period compared to seizure-free periods.

SIGNIFICANCE

Monitoring of PPG signals may assist in the detection of GTCSs in patients with epilepsy. PPG may serve as a promising biomarker for future seizure detection systems and may contribute to future seizure prediction systems.

Evaluation of Wearable Electronics for Epilepsy: A Systematic Review

Epilepsy is a neurological disorder that affects 50 million people worldwide. It is characterised by seizures that can vary in presentation, from short absences to protracted convulsions. Wearable electronic devices that detect seizures have the potential to hail timely assistance for individuals, inform their treatment, and assist care and self-management. This systematic review encompasses the literature relevant to the evaluation of wearable electronics for epilepsy. Devices and performance metrics are identified, and the evaluations, both quantitative and qualitative, are presented. Twelve primary studies comprising quantitative evaluations from 510 patients and participants were collated according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Two studies (with 104 patients/participants) comprised both qualitative and quantitative evaluation components. Despite many works in the literature proposing and evaluating novel and incremental approaches to seizure detection, there is a lack of studies evaluating the devices available to consumers and researchers, and there is much scope for more complete evaluation data in quantitative studies. There is also scope for further qualitative evaluations amongst individuals, carers, and healthcare professionals regarding their use, experiences, and opinions of these devices.

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.

Automated video-based detection of nocturnal motor seizures in children

Seizure detection devices can improve epilepsy care, but wearables are not always tolerated. We previously demonstrated good performance of a real‐time video‐based algorithm for detection of nocturnal convulsive seizures in adults with learning disabilities. The algorithm calculates the relative frequency content based on the group velocity reconstruction from video‐sequence optical flow. We aim to validate the video algorithm on nocturnal motor seizures in a pediatric population. We retrospectively analyzed the algorithm performance on a database including 1661 full recorded nights of 22 children (age = 3‐17 years) with refractory epilepsy at home or in a residential care setting. The algorithm detected 118 of 125 convulsions (median sensitivity per participant = 100%, overall sensitivity = 94%, 95% confidence interval = 61%‐100%) and identified all 135 hyperkinetic seizures. Most children had no false alarms; 81 false alarms occurred in six children (median false alarm rate [FAR] per participant per night = 0 [range = 0‐0.47], overall FAR = 0.05 per night). Most false alarms (62%) were behavior‐related (eg, awake and playing in bed). Our noncontact detection algorithm reliably detects nocturnal epileptic events with only a limited number of false alarms and is suitable for real‐time use.

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.

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.

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.

The role of devices in managing risk

Over the last few years, there has been significant expansion of wearable technologies and devices into the health sector, including for conditions such as epilepsy. Although there is significant potential to benefit patients, there is a paucity of well-conducted scientific research in order to inform patients and healthcare providers of the most appropriate technology. In addition to either directly or indirectly identifying seizure activity, the ideal device should improve quality of life and reduce the risk of sudden unexpected death in epilepsy (SUDEP). Devices typically monitor a number of parameters including electroencephalographic (EEG), cardiac, and respiratory patterns and can detect movement, changes in skin conductance, and muscle activity. Multimodal devices are emerging with improved seizure detection rates and reduced false positive alarms. While convulsive seizures are reliably identified by most unimodal and multimodal devices, seizures associated with no, or minimal, movement are frequently undetected. The vast majority of current devices detect but do not actively intervene. At best, therefore, they indicate the presence of seizure activity in order to accurately ascertain true seizure frequency or facilitate intervention by others, which may, nevertheless, impact the rate of SUDEP. Future devices are likely to both detect and intervene within an autonomous closed-loop system tailored to the individual and by self-learning from the analysis of patient-specific parameters. The formulation of standards for regulatory bodies to validate seizure detection devices is also of paramount importance in order to confidently ascertain the performance of a device; and this will be facilitated by the creation of a large, open database containing multimodal annotated data in order to test device algorithms. This paper is for the Special Issue: Prevent 21: SUDEP Summit - Time to Listen.

Seizure detection based on heart rate variability using a wearable electrocardiography device

OBJECTIVE

To assess the feasibility and accuracy of seizure detection based on heart rate variability (HRV) using a wearable electrocardiography (ECG) device. Noninvasive devices for detection of convulsive seizures (generalized tonic-clonic and focal to bilateral tonic-clonic seizures) have been validated in phase 2 and 3 studies. However, detection of nonconvulsive seizures still needs further research, since currently available methods have either low sensitivity or an extremely high false alarm rate (FAR).

METHODS

In this phase 2 study, we prospectively recruited patients admitted to long-term video-EEG monitoring (LTM). ECG was recorded using a dedicated wearable device. Seizures were automatically detected using HRV parameters computed off-line, blinded to all other data. We compared the performance of 26 automated algorithms with the seizure time-points marked by experts who reviewed the LTM recording. Patients were classified as responders if >66% of their seizures were detected.

RESULTS

We recruited 100 consecutive patients and analyzed 126 seizures (108 nonconvulsive and 18 convulsive) from 43 patients who had seizures during monitoring. The best-performing HRV algorithm combined a measure of sympathetic activity with a measure of how quickly HR changes occurred. The algorithm identified 53.5% of the patients with seizures as responders. Among responders, detection sensitivity was 93.1% (95% CI: 86.6%-99.6%) for all seizures and 90.5% (95% CI: 77.4%-97.3%) for nonconvulsive seizures. FAR was 1.0/24 h (0.11/night). Median seizure detection latency was 30 s. Typically, patients with prominent autonomic nervous system changes were responders: An ictal change of >50 heartbeats per minute predicted who would be responder with a positive predictive value of 87% and a negative predictive value of 90%.

SIGNIFICANCE

The automated HRV algorithm, using ECG recorded with a wearable device, has high sensitivity for detecting seizures, including the nonconvulsive ones. FAR was low during the night. This approach is feasible in patients with prominent ictal autonomic changes.

Accelerometry for remote monitoring of physical activity in amyotrophic lateral sclerosis: a longitudinal cohort study

Background

The extensive heterogeneity between patients with amyotrophic lateral sclerosis (ALS) complicates the quantification of disease progression. In this study, we determine the value of remote, accelerometer-based monitoring of physical activity in patients with ALS.

Methods

This longitudinal cohort study was conducted in a home-based setting; all study materials were sent by mail. Patients wore the ActiGraph during waking hours for 7 days every 2–3 months and provided information regarding their daily functioning (ALSFRS-R). We defined four accelerometer-based endpoints that either reflect the average daily activity or quantify the patient’s physical capacity.

Results

A total of 42 patients participated; the total valid monitoring period was 9288 h with a 93.0% adherence rate. At baseline, patients were active 27.9% (range 11.6–52.4%) of their time; this declined by 0.64% (95% 0.43–0.86, p < 0.001) per month. Accelerometer-based endpoints were strongly associated with the ALSFRS-R (r 0.78, 95% CI 0.63–0.92, p < 0.001), but showed less variability over time than the ALSFRS-R (coefficient of variation 0.64–0.81 vs. 1.06, respectively). Accelerometer-based endpoints could reduce sample size by 30.3% for 12-month trials and 44.6% for 18-month trials; for trials lasting less than 9 months, the ALSFRS-R resulted in smaller sample sizes.

Conclusion

Accelerometry is an objective method for quantifying disease progression, which could obtain real-world insights in the patient’s physical functioning and may personalize the delivery of care. In addition, remote monitoring provides patients with the opportunity to participate in clinical trials from home, paving the way to a patient-centric clinical trial model.