Large Neural Network Based Detection of Apnea, Bradycardia and Desaturation Events
This addresses a critical problem for newborn care by enabling early detection of life-threatening events, though it appears incremental as it compares to existing methods.
The paper tackled the detection of apnea, bradycardia, and desaturation events in newborn babies using a large neural network for binary classification, achieving a detection performance level feasible for clinical care with limited and unbalanced training data.
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.