Automatic Burst Detection based on Line Length in the Premature EEG

Ninah Koolen, Katrien Jansen, Jan Vervisch, Vladimir Matic, Maarten De Vos, Gunnar Naulaers, Sabine Van Huffel

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 Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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