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Authors: Ninah Koolen 1 ; Katrien Jansen 2 ; Jan Vervisch 2 ; Vladimir Matic 3 ; Maarten De Vos 4 ; Gunnar Naulaers 2 and Sabine Van Huffel 3

Affiliations: 1 iMinds-KU Leuven , Katholieke Universiteit Leuven and Leuven, Belgium ; 2 University Hospital Gasthuisberg, Belgium ; 3 iMinds-KU Leuven and Katholieke Universiteit Leuven, Belgium ; 4 University of Oldenburg, iMinds-KU Leuven and Katholieke Universiteit Leuven, Germany

ISBN: 978-989-8565-36-5

Keyword(s): Brain Monitoring, Premature EEG, Automatic Detection, Burst, Interburst Interval, Neonatal Intensive Care Unit.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Detection and Identification ; Medical Image Detection, Acquisition, Analysis and Processing ; Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics

Abstract: To extract useful information from preterm electroencephalogram (EEG) for diagnosis and long-term prognosis, automated processing of EEG is a crucial step to reduce the workload of neurologists. Important information is contained in the bursts, the interburst-intervals (IBIs) and the evolution of their duration over time. Therefore, an algorithm to automatically detect bursts and IBIs would be of significant value in the Neonatal Intensive Care Unit (NICU). The developed algorithm is based on calculation of the line length to segment EEG into bursts and IBIs. Validating burst detection of this algorithm with expert labelling and existing methods shows the robustness of this algorithm for the patients under test. Moreover, automation is within our grasp as calculated features mimic values obtained by scoring of experts. The outline for successful computer-aided detection of bursting processes is shown, thereby paving the way for improvement of the overall assessment in the NICU.

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Paper citation in several formats:
Koolen, N.; Jansen, K.; Vervisch, J.; Matic, V.; De Vos, M.; Naulaers, G. and Van Huffel, S. (2013). Automatic Burst Detection based on Line Length in the Premature EEG.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 105-111. DOI: 10.5220/0004186401050111

@conference{biosignals13,
author={Ninah Koolen. and Katrien Jansen. and Jan Vervisch. and Vladimir Matic. and Maarten De Vos. and Gunnar Naulaers. and Sabine Van Huffel.},
title={Automatic Burst Detection based on Line Length in the Premature EEG},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},
year={2013},
pages={105-111},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004186401050111},
isbn={978-989-8565-36-5},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)
TI - Automatic Burst Detection based on Line Length in the Premature EEG
SN - 978-989-8565-36-5
AU - Koolen, N.
AU - Jansen, K.
AU - Vervisch, J.
AU - Matic, V.
AU - De Vos, M.
AU - Naulaers, G.
AU - Van Huffel, S.
PY - 2013
SP - 105
EP - 111
DO - 10.5220/0004186401050111

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