Abstract
Objective
Daytime and nighttime patterns affect the dynamic modulation of brain and body functions and influence the autonomic nervous system response to seizures. Therefore, we aimed to evaluate 24‐hour patterns of electrodermal activity (EDA) in patients with and without seizures.
Methods
We included pediatric patients with (a) seizures (SZ), including focal impaired awareness seizures (FIAS) or generalized tonic‐clonic seizures (GTCS), (b) no seizures and normal electroencephalography (NEEG), or (c) no seizures but epileptiform activity in the EEG (EA) during vEEG monitoring. Patients wore a device that continuously recorded EDA and temperature (TEMP). EDA levels, EDA spectral power, and TEMP levels were analyzed. To investigate 24‐hour patterns, we performed a nonlinear mixed‐effects model analysis. Relative mean pre‐ictal (−30 min to seizure onset) and post‐ictal (I: 30 min after seizure offset; II: 30 to 60 min after seizure offset) values were compared for SZ subgroups.
Results
We included 119 patients (40 SZ, 17 NEEG, 62 EA). EDA level and power group‐specific models (SZ, NEEG, EA) (h = 1; P < .01) were superior to the all‐patient cohort model. Fifty‐nine seizures were analyzed. Pre‐ictal EDA values were lower than respective 24‐hour modulated SZ group values. Post hoc comparisons following the period‐by‐seizure type interaction (EDA level: χ2 = 18.50; P < .001, and power: χ2 = 6.73; P = .035) revealed that EDA levels were higher in the post‐ictal period I for FIAS and GTCS and in post‐ictal period II for GTCS only compared to the pre‐ictal period.
Significance
Continuously monitored EDA shows a pattern of change over 24 hours. Curve amplitudes in patients with recorded seizures were lower as compared to patients who did not exhibit seizures during the recording period. Sympathetic skin responses were greater and more prolonged in GTCS compared to FIAS. EDA recordings from wearable devices offer a noninvasive tool to continuously monitor sympathetic activity with potential applications for seizure detection, prediction, and potentially sudden unexpected death in epilepsy (SUDEP) risk estimation.
FEB