  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.
