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Cardiotocography (CTG) is the main tool used for fetal monitoring duringlabour. Interpretation of CTG requires dynamic pattern recognition in realtime. It is recognised as a difficult task with high inter- and intra-observerdisagreement. Machine learning has provided a viable path towards objective andreliable CTG assessment. In this study, novel CTG features are developed basedon clinical expertise and system control theory using an autoregressivemoving-average (ARMA) model to characterise the response of the fetal heartrate to contractions. The features are evaluated in a machine learning model toassess their efficacy in identifying fetal compromise. ARMA features rankedamongst the top features for detecting fetal compromise. Additionally,including clinical factors in the machine learning model and pruning data basedon a signal quality measure improved the performance of the classifier.

Classification of fetal compromise during labour: signal processing and feature engineering of the cardiotocograph