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William P. Marnane

William P. Marnane

D-Index & Metrics

Computer Science

D-Index
42
Citations
5956
World Ranking
8511
National Ranking
29

Overview

William P. Marnane is affiliated with University College Cork in Ireland, where their research primarily focuses on Medicine and Neuroscience. Their work spans several subfields including Pediatrics, Perinatology and Child Health, Cognitive Neuroscience, Endocrine and Autonomic Systems, Pulmonary and Respiratory Medicine, and Biomedical Engineering.

The scientist's main topics of research include Neonatal and fetal brain pathology, EEG and Brain-Computer Interfaces, Neuroscience of respiration and sleep, Neonatal Respiratory Health Research, Non-Invasive Vital Sign Monitoring, Cardiac Arrest and Resuscitation, and Atomic and Subatomic Physics Research.

Some of their recent papers are:

  • A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial, 2020, The Lancet Child & Adolescent Health
  • Neonatal Seizure Management: Is the Timing of Treatment Critical?, 2021, The Journal of Pediatrics
  • Grading hypoxic-ischemic encephalopathy in neonatal EEG with convolutional neural networks and quadratic time-frequency distributions, 2021, Journal of Neural Engineering
  • Machine learning for the early prediction of infants with electrographic seizures in neonatal hypoxic-ischemic encephalopathy, 2022, Epilepsia
  • Challenges of Developing Robust AI for Intrapartum Fetal Heart Rate Monitoring, 2021, Frontiers in Artificial Intelligence

Frequent co-authors in their research collaborations include Geraldine B. Boylan, Sean Mathieson, Gordon Lightbody, John M. O'Toole, and Andreea Pavel.

William P. Marnane has published multiple papers in venues such as arXiv (Cornell University), Zenodo (CERN European Organization for Nuclear Research), The Lancet Child & Adolescent Health, the 2021 29th European Signal Processing Conference (EUSIPCO), and The Journal of Pediatrics.

Best Publications

  • EEG-based neonatal seizure detection with Support Vector Machines.

    A. Temko;E. Thomas;W. Marnane;G. Lightbody

  • Energy-Efficient Low Duty Cycle MAC Protocol for Wireless Body Area Networks

    Stevan Jovica Marinkovic;Emanuel Mihai Popovici;Christian Spagnol;Stephen Faul

  • A comparison of quantitative EEG features for neonatal seizure detection.

    B.R. Greene;S. Faul;W.P. Marnane;G. Lightbody

  • Hardware Implementation of GF(2 m ) LDPC Decoders.

    Christian Spagnol;Emanuel M. Popovici;William P. Marnane

  • Efficient architectures for implementing montgomery modular multiplication and RSA modular exponentiation on reconfigurable logic

    Alan Daly;William Marnane

  • Optimisation of the SHA-2 family of hash functions on FPGAs

    R.P. McEvoy;F.M. Crowe;C.C. Murphy;W.P. Marnane

  • Efficient hardware for the tate pairing calculation in characteristic three

    T. Kerins;W. P. Marnane;E. M. Popovici;P. S. L. M. Barreto

  • Novel Real-Time Low-Complexity QRS Complex Detector Based on Adaptive Thresholding

    Raquel Gutierrez-Rivas;Juan Jesus Garcia;William P. Marnane;Alvaro Hernandez

  • FPGA Implementations of the Round Two SHA-3 Candidates

    Brian Baldwin;Andrew Byrne;Liang Lu;Mark Hamilton

  • A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial.

    Andreea M Pavel;Janet M Rennie;Linda S de Vries;Mats Blennow;Mats Blennow

  • Performance assessment for EEG-based neonatal seizure detectors

    A. Temko;E. Thomas;W. Marnane;G. Lightbody

  • Hardware Implementation of ${ m GF}(2^{m})$ LDPC Decoders

    C. Spagnol;E.M. Popovici;W.P. Marnane

  • Hardware Implementation of LDPC Decoders

    Christian Spagnol;Emanuel Mihai Popovici;William Peter Marnane

  • Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals

    Simon O’Regan;Stephen Faul;William Marnane

  • An FPGA implementation of a GF(p) ALU for encryption processors

    Alan Daly;William P. Marnane;Tim Kerins;Emanuel M. Popovici

  • Gaussian Process Modeling of EEG for the Detection of Neonatal Seizures

    S. Faul;G. Gregorcic;G. Boylan;W. Marnane

  • Differential power analysis of HMAC based on SHA-2, and countermeasures

    Robert McEvoy;Michael Tunstall;Colin C. Murphy;William P. Marnane

  • An Automated System for Grading EEG Abnormality in Term Neonates with Hypoxic-Ischaemic Encephalopathy

    N. J. Stevenson;I. Korotchikova;A. Temko;G. Lightbody

  • Robust neonatal EEG seizure detection through adaptive background modeling.

    Andrey Temko;Geraldine B. Boylan;William P. Marnane;Gordon Lightbody

  • Automatic detection of EEG artefacts arising from head movements

    Simon O' Regan;Stephen Faul;William Marnane

  • Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2010

    Orla Doyle;A Temko;D M Murray;G Lightbody

  • Identity- Based Cryptography

    C. Whelan;A. Byrne;Daniel Page;Frederik R G Vercauteren

Frequent Co-Authors

Gordon Lightbody
Gordon Lightbody University College Cork
Emanuel Popovici
Emanuel Popovici University College Cork
Edward Jones
Edward Jones University of Galway
Martin Glavin
Martin Glavin University of Galway
Maire O'Neill
Maire O'Neill Queen's University Belfast
Alexander C. van Huffelen
Alexander C. van Huffelen Utrecht University
Jonathan O'b. Hourihane
Jonathan O'b. Hourihane Royal College of Surgeons in Ireland
Richard B. Reilly
Richard B. Reilly Trinity College Dublin
Izzat Darwazeh
Izzat Darwazeh University College London

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