Seizure detection using wearable sensors and machine learning: Setting a benchmark

Epilepsy is a common cause of morbidity and mortality, especially among children, despite advances in management regimens.1, 2 Accurate monitoring and tracking of seizures are important to evaluate seizure burden, recurrence risk, and response to treatment. Outside the hospital, seizure tracking relies on patients' and families' self-reporting, which is often unreliable due to underreporting, seizures missed by caregivers, and patients' difficulties recalling seizures.3-6 Although long-term video-electroencephalography (EEG) in the epilepsy monitoring unit (EMU) is the gold standard for accurately diagnosing and evaluating epilepsy,7 it is also time-consuming and costly, can be perceived as stigmatizing, and places a greater burden on patients and caregivers than seizure monitoring with wearable devices. Based on prior studies, there exists a large clinical gap and urgent medical need to detect a broad range of seizures, beyond focal to bilateral tonic–clonic seizures (FBTCSs) and generalized tonic–clonic seizures (GTCSs), with wearable devices.3, Recent advances in the use and development of non-EEG-based seizure detection devices utilizing a variety of sensors and modalities provided innovative opportunities to fill this gap and to monitor patients continuously in the outpatient setting.