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

Bruno, Elisa; Böttcher, Sebastian; Viana, Pedro; Amengual‐Gual, Marta; Joseph, Boney; Epitashvili, Nino; Dümpelmann, Matthias; Glasstetter, Martin; Biondi, Andrea; Laerhoven, Kristof Van; Loddenkemper, Tobias; Richardson, Mark P.; Schulze‐Bonhage, Andreas; Brinkmann, Benjamin H.

doi:10.1111/epi.17044

Cited by 31 publications

(25 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most recently, the Wearables for Epilepsy and Research International Study Group proposed recommendations for reporting results from prospective wearable studies aimed at seizure detection. 36 These included obtaining usability challenges and user perspectives from people with epilepsy who wear the device(s). Few studies identified as part of our systematic review examined the preferences and experiences of users.…”

Section: Discussionmentioning

confidence: 99%

Preferences and User Experiences of Wearable Devices in Epilepsy

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Preferences and User Experiences of Wearable Devices in Epilepsy

et al. 2022

View full text Add to dashboard Cite

show abstract

“…reviewed all EEG recordings and annotated the timestamps of the seizures onset and offset. More details regarding the data acquisition can be found in Bruno et al [Bruno_2021].…”

Section: Methodsmentioning

confidence: 99%

Temporal evolution of multiday, epileptic functional networks prior to seizure occurrence

Laiou

Biondi

Bruno

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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 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 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 from EEG recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.

show abstract

“…Participants wore a variety of different wearable devices across the 2 sites; however, the only device worn by participants from both sites was a wrist-worn device (Empatica E4, Empatica Inc). The study and recording procedures were further described and discussed in the review by Bruno et al [ 29 ]. All recruited patients provided written informed consent, and the study procedures were approved by local ethics committees, the ethics committee at the University of Freiburg (538/16), and the London Fulham Research Ethics Committee (16/LO/2209; Integrated Research Application System project ID216316).…”

Section: Methodsmentioning

confidence: 99%

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

Böttcher

Bruno

Manyakov

et al. 2021

JMIR Mhealth Uhealth

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 31 publications

References 49 publications

Preferences and User Experiences of Wearable Devices in Epilepsy

Preferences and User Experiences of Wearable Devices in Epilepsy

Temporal evolution of multiday, epileptic functional networks prior to seizure occurrence

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

Contact Info

Product

Resources

About