S
Stephen Faul
Researcher at University College Cork
Publications - 27
Citations - 1140
Stephen Faul is an academic researcher from University College Cork. The author has contributed to research in topics: Wireless sensor network & Neonatal seizure. The author has an hindex of 15, co-authored 27 publications receiving 1083 citations. Previous affiliations of Stephen Faul include National University of Ireland.
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Journal ArticleDOI
Energy-Efficient Low Duty Cycle MAC Protocol for Wireless Body Area Networks
TL;DR: The results show that the protocol is energy efficient for streaming communication as well as sending short bursts of data, and thus can be used for different types of physiological signals with different sample rates.
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A comparison of quantitative EEG features for neonatal seizure detection.
Barry R. Greene,Stephen Faul,William P. Marnane,Gordon Lightbody,Irina Korotchikova,Geraldine B. Boylan +5 more
TL;DR: The results documented in this study may provide a reference for the optimum quantitative EEG features to use in developing and enhancing neonatal seizure detection algorithms.
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An evaluation of automated neonatal seizure detection methods
TL;DR: It is clear from this study that while each algorithm does produce some meaningful information, the information would only be usable in a reliable neonatal seizure detection process when accompanied by more complex analysis, and more advanced classifiers.
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Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals
TL;DR: Categorizing head-movement artefacts as one distinct class and using support vector machines to automatically detect their presence are presented, and the combination of features extracted from EEG and gyroscope signals is explored in order to design an algorithm which incorporates both physical and physiological signals in accurately detecting artefacts arising from head movements.
Journal ArticleDOI
Gaussian Process Modeling of EEG for the Detection of Neonatal Seizures
Stephen Faul,Gregor Gregorcic,Geraldine B. Boylan,William P. Marnane,Gordon Lightbody,S. Connolly +5 more
TL;DR: The GP measures, in particular, the prediction certainty approach, produce a high level of performance compared to other modeling methods and methods currently in clinical use for EEG analysis, indicating that they are an important and useful tool for the real-time detection of neonatal seizures.