State Space Clustering for Interpretable Fetal Heart Rate Characterization

Computerized cardiotocography (CTG) often relies on fetal heart rate (FHR) features with weak correlations to umbilical cord blood pH, the gold standard for neonatal acidosis, whereas deep learning features lack interpretability. In this work, we propose a novel family of interpretable FHR features based on clustering state vectors from shadow manifolds reconstructed from FHR signals. This approach partitions recordings into low- and high-volatility regimes and derives features that quantify both cluster compactness and separation. Experiments on an open-access CTG database demonstrate that these features achieve substantially stronger correlations with cord blood pH than conventional variability and nonlinear measures, which indicates their potential to improve the diagnostic power of computerized FHR analysis.