INFANT Research Centre - Conference Items

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    Investigation of lung volume measurements in neonates using gas in scattering media absorption spectroscopy
    (Optica Publishing Group, 2022-04) Pacheco, Andrea; Jayet, Bapiste; Grygoryev, Konstantin; Messina, Walter; Dehghani, Hamid; Krite Svanberh, Emilie; Dempsey, Eugene M.; Andersson-Engels, Stefan; Science Foundation Ireland
    We perform phantom and numerical studies of the changes in molecular oxygen and water vapor spectroscopic signals, showing the potential of measuring pulmonary volume changes with GASMAS technique in neonates.
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    Non-invasive lung oxygen monitoring in term infants: a pilot trial
    (Optica Publishing Group, 2022-04) Panaviene, Jurate; Grygoryev, Konstantin; Pacheco, Andrea; Dempsey, Eugene M.; Andersson-Engels, Stefan; Science Foundation Ireland
    Employing non-invasive GASMAS based system, lung oxygen measurements were performed on 25 healthy term infants on various chest positions. Oxygen and water vapor absorption signal was detected on most occasions.
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    Periconceptional and antenatal nutritional supplement use in Irish women: data from the IMPROvED Study
    (Nutrition Society, 2021-12) Kelliher, Lisa; Hennessy, Áine; McCarthy, Fergus P.; Kiely, Mairead E.
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    Investigating the impact of CNN depth on neonatal seizure detection performance
    (Institute of Electrical and Electronics Engineers (IEEE), 2018-10-29) O'Shea, Alison; Lightbody, Gordon; Boylan, Geraldine B.; Temko, Andriy; Wellcome Trust; Science Foundation Ireland
    This study presents a novel, deep, fully convolutional architecture which is optimized for the task of EEG-based neonatal seizure detection. Architectures of different depths were designed and tested; varying network depth impacts convolutional receptive fields and the corresponding learned feature complexity. Two deep convolutional networks are compared with a shallow SVMbased neonatal seizure detector, which relies on the extraction of hand-crafted features. On a large clinical dataset, of over 800 hours of multichannel unedited EEG, containing 1389 seizure events, the deep 11-layer architecture significantly outperforms the shallower architectures, improving the AUC90 from 82.6% to 86.8%. Combining the end-to-end deep architecture with the feature-based shallow SVM further improves the AUC90 to 87.6%. The fusion of classifiers of different depths gives greatly improved performance and reduced variability, making the combined classifier more clinically reliable.
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