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Technologies for automated detection of neonatal seizures are gradually moving towards cot-side implementation. The aim of this paper is to present different ways to visualize the output of a neonatal seizure detection system and analyse their influence on performance in a clinical environment. Three different ways to visualize the detector output are considered: As an alternative to visual aids, audified neonatal EEG is also considered. Additionally, a survey on the usefulness and accuracy of the presented methods has been performed among clinical personnel.
The main advantages and disadvantages of the presented methods are discussed. The connection between information visualization and different methods to compute conventional metrics is established.
The results of the visualization methods along with the system validation results indicate that the developed neonatal seizure detector with its current level of performance would unambiguously be of benefit to clinicians as a decision support system.
The results of the survey suggest that a suitable way to visualize the output of neonatal seizure detection systems in a clinical environment is a combination of a binary output and a probabilistic trace. The main healthcare benefits of the tool are outlined. The decision support system with the chosen visualization interface is currently undergoing pre-market European multi-centre clinical investigation to support its regulatory approval and clinical adoption.
His main research interests include kernel methods, signal processing, and multimodal interfaces. He has been involved in several EU and national government funded projects on speech and biomedical signal processing.
He is a senior member of IEEE. William Marnane received the B. Geraldine Boylan received the M. Much of her more recent work is of an interdisciplinary nature and aims to create a synergy between medicine and engineering by using the skills and techniques of engineering signal processing research to address important medical problems such as seizure detection in the neonate.
Gordon Lightbody graduated with the M. After completing a one year post-doctoral position funded by Du Pont, he was appointed by Queen's University as a lecturer in Modern Control Systems. In he was appointed as a lecturer in Control Engineering at University College Cork, and subsequently promoted to senior lecturer in Under a Creative Commons license.
Abstract Technologies for automated detection of neonatal seizures are gradually moving towards cot-side implementation. Keywords Neonatal seizure detection. Recommended articles Citing articles 0. Published by Elsevier B.