Twenty‐four‐hour patterns in electrodermal activity recordings of patients with and without epileptic seizures

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.

Association between semiologic, autonomic, and electrographic seizure characteristics in children with generalized tonic-clonic seizures

“…We also subtracted the ictal EDA from a baseline to avoid overestimating ictal EDA. Yet, we cannot completely rule out the EDA contaminants related to stress [33], circadian fluctuation [25], or sleep storms [31] that may lead to higher surges. In our study, patients wore the wristband on either wrist or ankle left or right side.…”

“…EDA can be measured contin-uously outside the hospital setting, using non-invasive and less stigmatizing wearable devices, such as wristbands, making them well-tolerated and accepted by users [20,40,41]. EDA monitoring provides helpful information on GTCS-related sympathetic changes relevant to SUDEP risk assessment and has shown utility seizure detection and prediction, especially when combined with other physiologic modalities [25,[42][43][44][45]. EDA monitoring may be a supplementary and necessary tool for evaluating seizure and SUDEP risk circumstances, particularly when other methods are not available.…”

“…This increase is a measure of seizure-related sympathetic activation, and correlates with the duration of PGES [23,24]. EDA can be measured peripherally using wearable devices such as wristbands and is a reliable method to detect GTCS and evaluate the resulting sympathetic surge and possibly assess SUDEP risk [20,[23][24][25].…”

Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic

“…Billeci et al found that heart rate variability could be used to predict seizures up to 15 min before onset with >80% sensitivity, albeit with a relatively high average false positive rate of 0.41 per hour (almost 10 per day) (128). Signals that show some well-defined ictal changes, such as EDA (129)(130)(131), heart rate, or EMG (121), also show predictive changes prior to seizure onset (32). For instance, a recent study found predictive value in wearable sensor recordings EDA, blood volume pulse, accelerometry, and skin temperature (102).…”

The Challenging Path to Developing a Mobile Health Device for Epilepsy: The Current Landscape and Where We Go From Here

“…Finally, a clear-cut surge in EDA is observed at onset of GTCS (67)(68)(69), while imbalance in this autonomic biomarker can also be observed in the pre-ictal state (70). A combination of signals can be used for seizure monitoring in the real-world setting, yet the pitfall of this approach is that signal quality, and thus reliability of the approach, is influenced by daily activities and FIGURE 1 | (A,B) A wrist worn commercially available seizure detector with integrated ACC and EDA sensor, (B) displays three electrodes for EDA measure that are in contact with the skin.…”