Apnea, bradycardia and desaturation (ABD) events often precede life-threatening events including sepsis in newborn babies. Here, we explore machine learning for detection of ABD events as a binary classification problem. We investigate the use of a large neural network to achieve a good detection performance. To be user friendly, the chosen neural network does not require a high level of parameter tuning. Furthermore, a limited amount of training data is available and the training dataset is unbalanced. Comparing with two widely used state-of-the-art machine learning algorithms, the large neural network is found to be efficient. Even with a limited and unbalanced training data, the large neural network provides a detection performance level that is feasible to use in clinical care.
@article{arxiv.1711.06484,
title = {Large Neural Network Based Detection of Apnea, Bradycardia and Desaturation Events},
author = {Antoine Honoré and Veronica Siljehav and Saikat Chatterjee and Eric Herlenius},
journal= {arXiv preprint arXiv:1711.06484},
year = {2017}
}