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Automated Neonatal Brain Monitoring
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Author(s): M. De Vos (University of Oldenburg, Germany & KU Leuven, Belgium), P. J. Cherian (Erasmus MC, The Netherlands), W. Deburchgraeve (KU Leuven, Belgium), R. M. Swarte (Erasmuc MC-Sophia, The Netherlands), P. Govaert (Erasmuc MC-Sophia, The Netherlands), S. Van Huffel (KU Leuven, Belgium)and G. H. Visser (Erasmus MC, The Netherlands)
Copyright: 2012
Pages: 18
Source title:
Neonatal Monitoring Technologies: Design for Integrated Solutions
Source Author(s)/Editor(s): Wei Chen (Eindhoven University of Technology, The Netherlands), Sidarto Bambang Oetomo (Máxima Medical Center, The Netherlands)and Loe Feijs (Eindhoven University of Technology, The Netherlands)
DOI: 10.4018/978-1-4666-0975-4.ch011
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Abstract
Monitoring the electroencephalogram (EEG) in sick newborn babies in the neonatal intensive care units (NICU) gives important information about brain function. Seizures are frequently seen in the EEG of the sick neonate, and usually denote serious underlying brain dysfunction. Current clinical practice assumes that neonatal seizures have to be treated to prevent further injury to the brain. Recording of amplitude integrated EEG (aEEG) or the full EEG supports treatment decisions as well as prognostication has become standard practice in many NICUs. aEEG has become popular in recent years due to its user friendliness. A full EEG offers a more reliable window to study the ongoing activity in the newborn brain with high temporal and relatively good spatial resolution. However, the expertise required to register and interpret EEG is not available around the clock in the NICUs. For this purpose, automated monitoring devices have been developed, to assist neonatologists at the bedside and neurophysiologists in reviewing large amounts of monitoring data. The main topic of this chapter is automated detection of neonatal seizures and its possible impact in clinical practice. Three different detection approaches are reviewed: model-based, heuristic and classifier-based. Also a futuristic view on automated EEG analysis systems will be given.
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