Conditional Contrastive Predictive Coding for Assessment of Fetal Health From the Cardiotocogram

Fetal well-being during labor is currently assessed by medical professionals through visual interpretation of the cardiotocogram, a simultaneous recording of Fetal Heart Rate and Uterine Activity. This method is disputed due to high inter- and intra-observer variability and a resulting high number of unnecessary interventions. Recently, an unsupervised deep learning model for automated anomaly detection in the cardiotocogram was presented. Anomalies were defined as out-of-distribution behaviour or deviations from subject-specific behaviour and the model was based on the WaveNet architecture, but required a two-step training. The current work improves this previous work by leveraging Contrastive Predictive Coding (CPC), which uses a contrastive loss to make latent predictions without requiring a decoder network. In this work, CPC was extended with a stochastic, recurrent, and conditioned (upon Uterine Activity) future predictor. We, moreover, introduce a new training objective that was found better suitable for the task of anomaly detection. Evaluated on annotations made by experienced gynecologists, all proposed extensions were shown to be beneficial, and the proposed method is shown to rival or outperform the WaveNet-based method on different annotation categories.