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
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.