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

TL;DR

This paper develops novel signal processing features from cardiotocography data, using ARMA models and clinical factors, to improve machine learning detection of fetal compromise during labour.

Contribution

It introduces new ARMA-based features and integrates clinical factors to enhance machine learning classification of fetal compromise from CTG signals.

Findings

ARMA features are highly effective for detecting fetal compromise.

Including clinical factors improves classifier performance.

Signal quality pruning enhances detection accuracy.

Abstract

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.