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Clinical Autonomic Research

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Open Access 01-04-2019 | Review Article

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

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

Published in: Clinical Autonomic Research | Issue 2/2019

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.

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Title: Ictal autonomic changes as a tool for seizure detection: a systematic review
Authors: Anouk van Westrhenen
Thomas De Cooman
Richard H. C. Lazeron
Sabine Van Huffel
Roland D. Thijs
Publication date: 01-04-2019
Publisher: Springer Berlin Heidelberg
Published in: Clinical Autonomic Research / Issue 2/2019
Print ISSN: 0959-9851
Electronic ISSN: 1619-1560
DOI: https://doi.org/10.1007/s10286-018-0568-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Purpose

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

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