Intrapartum Cardiotocography Feature Detection and Fetal State Estimation Using Signal Processing and Machine Learning

This chapter will focus on automated approaches to interpreting intrapartum cardiotocography (CTG). The goal is to assist clinical staff in making better obstetrical decisions from the information contained in the two acquired CTG signals: uterine pressure (UP) and fetal heart rate (FHR). The methods and rationale for acquiring these signals as well as the accumulated clinical consensus on their most informative aspects will be discussed. Key signal preprocessing steps will be introduced that account for such artifacts as missing signal and maternal heart rate interference. Two interpretation problems will be addressed within a machine learning context. The first is the automated detection of the key clinical features such as FHR baseline, acceleration, and deceleration. The second addresses the key rationale for intrapartum CTG and its most challenging aspect, that is, inference on the fetal state that is timely and accurate. Relevant machine learning challenges will be discussed including signal representation and learning strategies, with examples from specific approaches. Finally, we will assess the limitations of such interpretation and consider future directions.