Fetal Heart Rate Classification Using Generative Models

This paper presents novel methods for classification of fetal heart rate (FHR) signals into categories that are meaningful for clinical implementation. They are based on generative models (GMs) and Bayesian theory. Instead of using scalar features that summarize information obtained from long-duration data, the models allow for explicit use of feature sequences derived from local patterns of FHR evolution. We compare our methods with a deterministic expert system for classification and with a support vector machine approach that relies on system-identification and heart rate variability features. We tested the classifiers on 83 retrospectively collected FHR records, with the gold-standard true diagnosis defined using umbilical cord pH values. We found that our methods consistently performed as well as or better than these, suggesting that the use of GMs and the Bayesian paradigm can bring significant improvement to automatic FHR classification approaches.