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Clin Auton Res. 2019 Apr;29(2):161-181. doi: 10.1007/s10286-018-0568-1. Epub 2018 Oct 30.

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

Author information

1
Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, P.O. Box 540, 2130 AM, Hoofddorp, The Netherlands.
2
Department of Neurology, Leiden University Medical Center (LUMC), Leiden, The Netherlands.
3
Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Louvain, Belgium.
4
IMEC, Louvain, Belgium.
5
Academic Center of Epileptology Kempenhaeghe, Heeze, The Netherlands.
6
Faculty of Electrical Engineering, Technical University Eindhoven, Eindhoven, The Netherlands.
7
Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, P.O. Box 540, 2130 AM, Hoofddorp, The Netherlands. rthijs@sein.nl.
8
Department of Neurology, Leiden University Medical Center (LUMC), Leiden, The Netherlands. rthijs@sein.nl.

Abstract

PURPOSE:

Adequate 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.

METHODS:

The 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.

RESULTS:

Twenty-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).

CONCLUSIONS:

The 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.

KEYWORDS:

Algorithm(s); Automatic seizure detection; Autonomic function(s); Autonomic parameter(s); Epilepsy; SUDEP

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