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

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.

First Demonstration of Field-Deployable Low Latency Hollow-core Cable Capable of Supporting >1000km, 400Gb/s WDM Transmission

We report order-of-magnitude improvements in performance of field-deployable hollow-core fiber cables evidenced by a 38.4Tb/s (800Gb/s-x-48WDM-channels) 20.5km lab-trial using commercial terminal equipment and the demonstration of 1128km/126km reach in full-fill 400/800Gb/s WDM recirculating-loop experiments.