Paediatrics and Child Health - Conference Items

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    Neonatal hypoxic-ischemic encephalopathy grading from multi-channel EEG time-series data using a fully convolutional neural network
    (Institute of Electrical and Electronics Engineers (IEEE), 2023-10-18) Yu, Shuwen; Marnane, William P.; Boylan, Geraldine B.; Lightbody, Gordon; Science Foundation Ireland; Wellcome Trust
    A deep learning classifier is proposed for hypoxic-ischemic encephalopathy (HIE) grading in neonates. Rather than using any features, this architecture can be fed with raw EEG. Fully convolutional layers were adopted both in the feature extraction and classification blocks, which makes this architecture simpler, and deeper, but with fewer parameters. Here two large (335h and 338h respectively) multi-center neonatal continuous EEG datasets were used for training and test. The model was trained based on weak labels and channel independence. A majority vote method was used for the post-processing of the classifier results (across time and channels) to increase the robustness of the prediction. The proposed system achieved an accuracy of 86.09% (95% confidence interval: 82.41% ∼89.78%), an MCC of 0.7691, and an AUC of 86.23% on the large unseen test set. Two convolutional neural network architectures which utilized time-frequency distribution features were selected as the baseline as they had been developed or tested on the same datasets. A relative improvement of 23.65% in test accuracy was obtained as compared with the best baseline.
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    Sparse-denoising methods for extracting desaturation transients in cerebral oxygenation signals of preterm Infants
    (IEEE, 2021-11) Ashoori, Minoo; Dempsey, Eugene M.; McDonald, Fiona B.; O'Toole, John M.; Science Foundation Ireland
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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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    Heart rate variability during periods of low blood pressure as a predictor of short-term outcome in preterms
    (Institute of Electrical and Electronics Engineers (IEEE), 2018-10-29) Semenova, Oksana; Carra, Giorgia; Lightbody, Gordon; Boylan, Geraldine B.; Dempsey, Eugene M.; Temko, Andriy; Science Foundation Ireland
    Efficient management of low blood pressure (BP) in preterm neonates remains challenging with a considerable variability in clinical practice. The ability to assess preterm wellbeing during episodes of low BP will help to decide when and whether hypotension treatment should be initiated. This work aims to investigate the relationship between heart rate variability (HRV), BP and the short-term neurological outcome in preterm infants less than 32 weeks gestational age (GA). The predictive power of common HRV features with respect to the outcome is assessed and shown to improve when HRV is observed during episodes of low mean arterial pressure (MAP) - with a single best feature leading to an AUC of 0.87. Combining multiple features with a boosted decision tree classifier achieves an AUC of 0.97. The work presents a promising step towards the use of multimodal data in building an objective decision support tool for clinical prediction of short-term outcome in preterms who suffer episodes of low BP.
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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.