  Cardiotocography (CTG) is the main tool used for fetal monitoring during
labour. Interpretation of CTG requires dynamic pattern recognition in real
time. It is recognised as a difficult task with high inter- and intra-observer
disagreement. Machine learning has provided a viable path towards objective and
reliable CTG assessment. In this study, novel CTG features are developed based
on clinical expertise and system control theory using an autoregressive
moving-average (ARMA) model to characterise the response of the fetal heart
rate to contractions. The features are evaluated in a machine learning model to
assess their efficacy in identifying fetal compromise. ARMA features ranked
amongst the top features for detecting fetal compromise. Additionally,
including clinical factors in the machine learning model and pruning data based
on a signal quality measure improved the performance of the classifier.
