Ictal autonomic changes as a tool for seizure detection: a systematic review

Anouk Van Westrhenen,Sabine Van Huffel,Thomas De Cooman,Roland D Thijs,Richard H C Lazeron

doi:10.1007/s10286-018-0568-1

Anouk Van Westrhenen, Sabine Van Huffel + Show 3 more

Open Access

https://doi.org/10.1007/s10286-018-0568-1

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Abstract

PurposeAdequate epileptic seizure detection may have the potential to minimize seizure-related complications and improve treatment evaluation. Autonomic changes often precede ictal electroencephalographic discharges and therefore provide a promising tool for timely seizure detection. We reviewed the literature for seizure detection algorithms using autonomic nervous system parameters.MethodsThe PubMed and Embase databases were systematically searched for original human studies that validate an algorithm for automatic seizure detection based on autonomic function alterations. Studies on neonates only and pilot studies without performance data were excluded. Algorithm performance was compared for studies with a similar design (retrospective vs. prospective) reporting both sensitivity and false alarm rate (FAR). Quality assessment was performed using QUADAS-2 and recently reported quality standards on reporting seizure detection algorithms.ResultsTwenty-one out of 638 studies were included in the analysis. Fifteen studies presented a single-modality algorithm based on heart rate variability (n = 10), heart rate (n = 4), or QRS morphology (n = 1), while six studies assessed multimodal algorithms using various combinations of HR, corrected QT interval, oxygen saturation, electrodermal activity, and accelerometry. Most studies had small sample sizes and a short follow-up period. Only two studies performed a prospective validation. A tendency for a lower FAR was found for retrospectively validated algorithms using multimodal autonomic parameters compared to those using single modalities (mean sensitivity per participant 71–100% vs. 64–96%, and mean FAR per participant 0.0–2.4/h vs. 0.7–5.4/h).ConclusionsThe overall quality of studies on seizure detection using autonomic parameters is low. Unimodal autonomic algorithms cannot reach acceptable performance as false alarm rates are still too high. Larger prospective studies are needed to validate multimodal automatic seizure detection.

Highlights

Epileptic seizures are potentially dangerous as they can lead to complications, including injury, status epilepticus, and sudden unexpected death in epilepsy (SUDEP) [1]
The PubMed and Embase databases were systematically searched through May 2018 for original studies validating an algorithm for automatic seizure detection based on heart rate (HR), heart rate variability (HRV), oxygen saturation (SpO2), electrodermal activity (EDA, reflecting changes in transpiration), or a combination of the aforementioned
Out of the 638 articles identified, 86 studies were selected on the basis of title and abstract

Summary

Introduction

Epileptic seizures are potentially dangerous as they can lead to complications, including injury, status epilepticus, and sudden unexpected death in epilepsy (SUDEP) [1]. Adequate seizure detection may have the potential to minimize these complications and to ameliorate treatment evaluation, as seizures— those at night—are often underreported [2,3,4,5]. Several parameters, including movement, sound, and autonomic nervous system changes, can be used to detect seizures. Changes in autonomic function can precede ictal electroencephalographic (EEG) discharges by several seconds [10,11,12]. Autonomic alterations may provide an adequate tool for early seizure detection and facilitate timely interventions. SUDEP usually occurs several minutes after a convulsive seizure (mean 10 min, range 2–17 min) [18]. Raising an alarm at seizure onset may be sufficient to allow timely intervention

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