Wearable detection of tonic seizures in childhood epilepsy: An exploratory cohort study

Abstract

AbstractObjectiveTo investigate the performance of a multimodal wearable device for the offline detection of tonic seizures (TS) in a pediatric childhood epilepsy cohort, with a focus on patients with Lennox–Gastaut syndrome.MethodsParallel with prolonged video‐electroencephalography (EEG), the Plug 'n Patch system, a multimodal wearable device using the Sensor Dot and replaceable electrode adhesives, was used to detect TS. Multiple biosignals were recorded: behind‐the‐ear EEG, surface electromyography, electrocardiography, and accelerometer/gyroscope. Biosignals were annotated blindly by a neurologist. Seizure characteristics were described, and performance was assessed by sensitivity, positive predictive value (PPV), F1 score, and false alarm rate (FAR) per hour. Performance was compared to seizure diaries kept by the caretaker.ResultsNinety‐nine TS were detected in 13 patients. Seven patients (54%) had Lennox–Gastaut syndrome and six patients (46%) had other forms of (developmental) epileptic encephalopathies or drug‐resistant epilepsy. All but one patient had intellectual disability. Overall sensitivity was 41%, with a PPV of 9%, an F1 score of 14%, and a median FAR per hour of 0.75. Performance increased to an F1 score of 66% for nightly seizures lasting at least 10 s (sensitivity 66%, PPV 66%) and 71% for nightly seizures lasting at least 20 s (sensitivity 62%, PPV 82%). For these seizures there were no false alarms in 10 of 13 patients. Sensitivity of seizure diaries reached a maximum of 52% for prolonged (≥20 s) nightly seizures, even though caretakers slept in the same room.SignificanceWe showed that it is feasible to use a multimodal wearable device with multiple adhesive sites in children with epilepsy and intellectual disability. For prolonged nightly seizures, offline manual detection of TS outperformed seizure diaries. The recognition of seizure‐specific signatures using multiple modalities can help in the development of automated TS detection algorithms.