@inbook{2401c05eedf94381891dd80731fce993,
title = "Towards Deeper Neural Networks for Neonatal Seizure Detection",
abstract = "Machine learning and more recently deep learning have become valuable tools in clinical decision making for neonatal seizure detection. This work proposes a deep neural network architecture which is capable of extracting information from long segments of EEG. Residual connections as well as data augmentation and a more robust optimizer are efficiently exploited to train a deeper architecture with an increased receptive field and longer EEG input. The proposed system is tested on a large clinical dataset of 4,570 hours of duration and benchmarked on a publicly available Helsinki dataset of 112 hours duration. The performance has improved from an AUC of 95.41\% to an AUC of 97.73\% when compared to a deep learning baseline.",
author = "Aengus Daly and Alison O'Shea and Gordon Lightbody and Andriy Temko",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 ; Conference date: 01-11-2021 Through 05-11-2021",
year = "2021",
doi = "10.1109/EMBC46164.2021.9629485",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "920--923",
booktitle = "43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021",
address = "United States",
}