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

Development and Calibration of a PATCH Device for Monitoring Children's Heart Rate and Acceleration

INTRODUCTION

Current wearables that collect heart rate and acceleration were not designed for children and/or do not allow access to raw signals, making them fundamentally unverifiable. This study describes the creation and calibration of an open-source multichannel platform (PATCH) designed to measure heart rate and acceleration in children ages 3-8 yr.

METHODS

Children (N = 63; mean age, 6.3 yr) participated in a 45-min protocol ranging in intensities from sedentary to vigorous activity. Actiheart-5 was used as a comparison measure. We calculated mean bias, mean absolute error (MAE) mean absolute percent error (MA%E), Pearson correlations, and Lin's concordance correlation coefficient (CCC).

RESULTS

Mean bias between PATCH and Actiheart heart rate was 2.26 bpm, MAE was 6.67 bpm, and M%E was 5.99%. The correlation between PATCH and Actiheart heart rate was 0.89, and CCC was 0.88. For acceleration, mean bias was 1.16 mg and MAE was 12.24 mg. The correlation between PATCH and Actiheart was 0.96, and CCC was 0.95.

CONCLUSIONS

The PATCH demonstrated clinically acceptable accuracies to measure heart rate and acceleration compared with a research-grade device.

The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom-built wearable device

OBJECTIVE

To develop and validate a method for long-term (24-h) objective quantification of absence seizures in the EEG of patients with childhood absence epilepsy (CAE) in their real home environment using a wearable device (waEEG), comparing automatic detection methods with auditory recognition after seizure sonification.

METHODS

The waEEG recording was acquired with two scalp electrodes. Automatic analysis was performed using previously validated software (Persyst® 14) and then fully reviewed by an experienced clinical neurophysiologist. The EEG data were converted into an audio file in waveform format with a 60-fold time compression factor. The sonified EEG was listened to by three inexperienced observers and the number of seizures and the processing time required for each data set were recorded blind to other data. Quantification of seizures from the patient diary was also assessed.

RESULTS

Eleven waEEG recordings from seven CAE patients with an average age of 8.18 ± 1.60 years were included. No differences in the number of seizures were found between the recordings using automated methods and expert audio assessment, with significant correlations between methods (ρ > .89, p < .001) and between observers (ρ > .96, p < .001). For the entire data set, the audio assessment yielded a sensitivity of .830 and a precision of .841, resulting in an F1 score of .835.

SIGNIFICANCE

Auditory waEEG seizure detection by lay medical personnel provided similar accuracy to post-processed automatic detection by an experienced clinical neurophysiologist, but in a less time-consuming procedure and without the need for specialized resources. Sonification of long-term EEG recordings in CAE provides a user-friendly and cost-effective clinical workflow for quantifying seizures in clinical practice, minimizing human and technical constraints.

Current state of the epilepsy drug and device pipeline

The field of epilepsy has undergone substantial advances as we develop novel drugs and devices. Yet considerable challenges remain in developing broadly effective, well-tolerated treatments, but also precision treatments for rare epilepsies and seizure-monitoring devices. We summarize major recent and ongoing innovations in diagnostic and therapeutic products presented at the seventeenth Epilepsy Therapies & Diagnostics Development (ETDD) conference, which occurred May 31 to June 2, 2023, in Aventura, Florida. Therapeutics under development are targeting genetics, ion channels and other neurotransmitters, and many other potentially first-in-class interventions such as stem cells, glycogen metabolism, cholesterol, the gut microbiome, and novel modalities for delivering electrical neuromodulation.

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.

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.

Data quality evaluation in wearable monitoring

Wearable recordings of neurophysiological signals captured from the wrist offer enormous potential for seizure monitoring. Yet, data quality remains one of the most challenging factors that impact data reliability. We suggest a combined data quality assessment tool for the evaluation of multimodal wearable data. We analyzed data from patients with epilepsy from four epilepsy centers. Patients wore wristbands recording accelerometry, electrodermal activity, blood volume pulse, and skin temperature. We calculated data completeness and assessed the time the device was worn (on-body), and modality-specific signal quality scores. We included 37,166 h from 632 patients in the inpatient and 90,776 h from 39 patients in the outpatient setting. All modalities were affected by artifacts. Data loss was higher when using data streaming (up to 49% among inpatient cohorts, averaged across respective recordings) as compared to onboard device recording and storage (up to 9%). On-body scores, estimating the percentage of time a device was worn on the body, were consistently high across cohorts (more than 80%). Signal quality of some modalities, based on established indices, was higher at night than during the day. A uniformly reported data quality and multimodal signal quality index is feasible, makes study results more comparable, and contributes to the development of devices and evaluation routines necessary for seizure monitoring.

Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence

Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.

Seizure remission rates remain low in a resource-limited country, a multicentre comparison study in Malaysia

Seizure remission rates of 60% with antiseizure medications were reported in developed countries, but might be lower in resource-limited countries. The challenges in epilepsy care in resource-limited regions were highlighted 10 years ago, and still remain an ongoing issue. This study aimed to determine the seizure freedom rates in level-2 epilepsy care centres (centres with general neurologists) compared to level-3/4 centres (centres with epileptologists providing epilepsy surgery evaluation) in Malaysia. This is a retrospective study of 1,347 adult epilepsy patients from two level-2 (n = 290) and two level-3/4 epilepsy care centres (n = 1,057). The seizure remission rates were significantly lower in level-2 centres (42.5%) compared to the level 3/4 centres (61.9%, p < 0.05). Level-2 centres had significantly more patients with undetermined seizure types compared to level-3/4 centres (6.6% vs 3.1%, p < 0.05). Level-3/4 centres had significantly more patients with epilepsy of structural and genetic origins, whereas more patients in level-2 centres had unknown aetiology (46.2% vs. 34.0% in level-3/4, p < 0.05). Level-2 centres had a lower neurologist-to-patient ratio (1:97 vs. 1:50 in level-3/4 centres, p < 0.05). Level-2 centres also had fewer patients, who underwent investigations such as EEG (74.1% vs. 89.6%) and brain MRI (54.1% vs. 72.4%, p < 0.05) in comparison with level-3/4 centres. Our study emphasized the existing challenges in epilepsy care in a resource-limited country to achieve the ideal 60% seizure remission rate.

Preferences and User Experiences of Wearable Devices in Epilepsy: A Systematic Review and Mixed-Methods Synthesis

BACKGROUND AND OBJECTIVES

To examine the preferences and user experiences of people with epilepsy and caregivers regarding automated wearable seizure detection devices.

METHODS

We performed a mixed-methods systematic review. We searched electronic databases for original peer-reviewed publications between January 1, 2000, and May 26, 2021. Key search terms included "epilepsy", "seizure", "wearable", and "non-invasive". We performed a descriptive and a qualitative thematic analysis of the studies included according to the technology acceptance model. Full texts of the discussion sections were further analyzed to identify word frequency and word mapping.

RESULTS

Twenty-two observational studies were identified. Collectively, they comprised responses from 3299 participants including patients with epilepsy, caregivers and healthcare workers. Sixteen studies examined user preferences, five examined user experiences, and one examined both experiences and preferences. Important preferences for wearables included improving care, cost, accuracy, and design. Patients desired real-time detection with a latency of ≤15 minutes from seizure occurrence, along with high sensitivity (≥90%) and low false-alarm rates. Device related costs were a major factor for device acceptance, where device costs of <$300 USD and a monthly subscription fee of <$20 USD were preferred. Despite being a major driver of wearable-based technologies, sudden unexpected death in epilepsy (SUDEP) was rarely discussed. Among studies evaluating user experiences, there was a greater acceptance towards wristwatches. Thematic coding analysis showed that attitudes towards device use, and perceived usefulness were reported consistently. Word mapping identified 'specificity', 'cost', and 'battery' as key single terms, and 'battery life', 'insurance coverage', 'prediction/detection quality', and the effect of devices on 'daily life' as key bigrams.

DISCUSSION

User acceptance of wearable technology for seizure detection was strongly influenced by accuracy, design, comfort, and cost. Our findings emphasise the need for standardised and validated tools to comprehensively examine preferences and user experiences of wearable devices in this population, using the themes identified in this study. Greater efforts to incorporate perspectives and user experiences in developing wearables for seizure detection, particularly in community-based settings are needed.

PROSPERO REGISTRATION

CRD42020193565.

Intra- and Inter-Subject Perspectives on the Detection of Focal Onset Motor Seizures in Epilepsy Patients

Focal onset epileptic seizures are highly heterogeneous in their clinical manifestations, and a robust seizure detection across patient cohorts has to date not been achieved. Here, we assess and discuss the potential of supervised machine learning models for the detection of focal onset motor seizures by means of a wrist-worn wearable device, both in a personalized context as well as across patients. Wearable data were recorded in-hospital from patients with epilepsy at two epilepsy centers. Accelerometry, electrodermal activity, and blood volume pulse data were processed and features for each of the biosignal modalities were calculated. Following a leave-one-out approach, a gradient tree boosting machine learning model was optimized and tested in an intra-subject and inter-subject evaluation. In total, 20 seizures from 9 patients were included and we report sensitivities of 67% to 100% and false alarm rates of down to 0.85 per 24 h in the individualized assessment. Conversely, for an inter-subject seizure detection methodology tested on an out-of-sample data set, an optimized model could only achieve a sensitivity of 75% at a false alarm rate of 13.4 per 24 h. We demonstrate that robustly detecting focal onset motor seizures with tonic or clonic movements from wearable data may be possible for individuals, depending on specific seizure manifestations.

A Systematic Review of the Scope of Study of mHealth Interventions for Wellness and Related Challenges in Pediatric and Young Adult Populations

Background

Despite the purported advantages and potential efficacy of mHealth interventions to promote wellness in children, adolescents, and young adults, it is not clear what areas have been explored and the challenges reported in the biomedical literature.

Methods

We conducted a scoping review of publications between 2015 and 2019.

Results

We identified 54 papers that met our inclusion criteria. Studies were conducted in 21 countries and ranged in size from six to 9851 participants (median: 184). A total of 41% of studies enrolled adolescents only (n = 19). Of the seven types of mHealth interventions identified, apps were the most common intervention (59%; n = 32) evaluated and 44% of the studies evaluated two or more interventions. The most common topic of the studies reviewed was sexual and reproductive health (24%; n = 13).

Conclusion

Most pediatric mHealth intervention studies are conducted in adolescents in large part, and sexual and reproductive health is the most commonly studied topic. With the easy and widespread accessibility to smartphone technology, the use of mobile apps for wellness interventions will likely continue to expand to other wellness topics.

Delivering epilepsy care in low-resource settings: the role of technology

INTRODUCTION

The implementation of technology in the field of epileptology has traditionally focused on its use for diagnosis and treatment and has, unsurprisingly, been capital-intensive, making it therefore mainly implementable in advanced high-income countries. Because of technological innovations over the past 20 years there has been almost a paradigm shift, particularly in access to and the potential for implementing relevant technology in lesser developed environments. Nearly 80% of people living with epilepsy live in low and middle-income countries.

AREAS COVERED

The challenge and the purpose of this paper is to discuss how technology can be implemented into lesser-resourced contexts not only cost-effectively but in a cost-saving way while also building capacity and thus sustainability.

EXPERT OPINION

The rate of technological advancement presents the risk of progressive widening of the technology and care gaps between advanced and lesser developed regions. Implementing technology is both about finding relevant appropriate technologies for the individual contexts of a diverse range of countries but also about repurposing low-tech technologies for application in epilepsy care in these areas. Finally exciting advances such as autonomous driving, digital twinning and robotic surgery will likely transform epilepsy care in several lower-resourced settings in the next 5-10 years.

Detecting Tonic-Clonic Seizures in Multimodal Biosignal Data From Wearables: Methodology Design and Validation

BACKGROUND

Video electroencephalography recordings, routinely used in epilepsy monitoring units, are the gold standard for monitoring epileptic seizures. However, monitoring is also needed in the day-to-day lives of people with epilepsy, where video electroencephalography is not feasible. Wearables could fill this gap by providing patients with an accurate log of their seizures.

OBJECTIVE

Although there are already systems available that provide promising results for the detection of tonic-clonic seizures (TCSs), research in this area is often limited to detection from 1 biosignal modality or only during the night when the patient is in bed. The aim of this study is to provide evidence that supervised machine learning can detect TCSs from multimodal data in a new data set during daytime and nighttime.

METHODS

An extensive data set of biosignals from a multimodal watch worn by people with epilepsy was recorded during their stay in the epilepsy monitoring unit at 2 European clinical sites. From a larger data set of 243 enrolled participants, those who had data recorded during TCSs were selected, amounting to 10 participants with 21 TCSs. Accelerometry and electrodermal activity recorded by the wearable device were used for analysis, and seizure manifestation was annotated in detail by clinical experts. Ten accelerometry and 3 electrodermal activity features were calculated for sliding windows of variable size across the data. A gradient tree boosting algorithm was used for seizure detection, and the optimal parameter combination was determined in a leave-one-participant-out cross-validation on a training set of 10 seizures from 8 participants. The model was then evaluated on an out-of-sample test set of 11 seizures from the remaining 2 participants. To assess specificity, we additionally analyzed data from up to 29 participants without TCSs during the model evaluation.

RESULTS

In the leave-one-participant-out cross-validation, the model optimized for sensitivity could detect all 10 seizures with a false alarm rate of 0.46 per day in 17.3 days of data. In a test set of 11 out-of-sample TCSs, amounting to 8.3 days of data, the model could detect 10 seizures and produced no false positives. Increasing the test set to include data from 28 more participants without additional TCSs resulted in a false alarm rate of 0.19 per day in 78 days of wearable data.

CONCLUSIONS

We show that a gradient tree boosting machine can robustly detect TCSs from multimodal wearable data in an original data set and that even with very limited training data, supervised machine learning can achieve a high sensitivity and low false-positive rate. This methodology may offer a promising way to approach wearable-based nonconvulsive seizure detection.

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.