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In-depth performance analysis of an EEG based neonatal seizure detection algorithm

Mathieson, S; Rennie, J; Livingstone, V; Temko, A; Low, E; Pressler, RM; Boylan, GB; (2016) In-depth performance analysis of an EEG based neonatal seizure detection algorithm. Clinical Neurophysiology , 127 (5) pp. 2246-2256. 10.1016/j.clinph.2016.01.026. Green open access

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Abstract

OBJECTIVE: To describe a novel neurophysiology based performance analysis of automated seizure detection algorithms for neonatal EEG to characterize features of detected and non-detected seizures and causes of false detections to identify areas for algorithmic improvement. METHODS: EEGs of 20 term neonates were recorded (10 seizure, 10 non-seizure). Seizures were annotated by an expert and characterized using a novel set of 10 criteria. ANSeR seizure detection algorithm (SDA) seizure annotations were compared to the expert to derive detected and non-detected seizures at three SDA sensitivity thresholds. Differences in seizure characteristics between groups were compared using univariate and multivariate analysis. False detections were characterized. RESULTS: The expert detected 421 seizures. The SDA at thresholds 0.4, 0.5, 0.6 detected 60%, 54% and 45% of seizures. At all thresholds, multivariate analyses demonstrated that the odds of detecting seizure increased with 4 criteria: seizure amplitude, duration, rhythmicity and number of EEG channels involved at seizure peak. Major causes of false detections included respiration and sweat artefacts or a highly rhythmic background, often during intermediate sleep. CONCLUSION: This rigorous analysis allows estimation of how key seizure features are exploited by SDAs. SIGNIFICANCE: This study resulted in a beta version of ANSeR with significantly improved performance.

Type: Article
Title: In-depth performance analysis of an EEG based neonatal seizure detection algorithm
Location: Netherlands
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.clinph.2016.01.026
Publisher version: http://dx.doi.org/10.1016/j.clinph.2016.01.026
Language: English
Additional information: Copyright © 2016 International Federation of Clinical Neurophysiology. Elsevier Ireland Ltd. All rights reserved. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). This document was posted here by permission of the publisher. At the time of the deposit, it included all changes made during peer review, copy editing, and publishing. The U. S. National Library of Medicine is responsible for all links within the document and for incorporating any publisher-supplied amendments or retractions issued subsequently. The published journal article, guaranteed to be such by Elsevier, is available for free, on ScienceDirect, at: http://dx.doi.org/10.1016/j.clinph.2016.01.026
Keywords: Automated seizure detection, Neonatal seizures
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Developmental Neurosciences Dept
URI: https://discovery.ucl.ac.uk/id/eprint/1501046
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