Classification of fetal compromise during labour: signal processing and feature engineering of the cardiotocograph
This work addresses the challenge of unreliable fetal monitoring for clinicians, though it is incremental as it builds on existing machine learning approaches with new features.
The study tackled the problem of objective fetal compromise detection during labor by developing novel cardiotocography features based on clinical expertise and system control theory, resulting in ARMA features ranking among the top for detection and improved classifier performance through clinical factor inclusion and data pruning.
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.