A database of fetal heart rate (FHR) time series measured from 7 221 patients during labor is analyzed with the aim of learning the types of features of these recordings that are informative of low cord pH. Our highly comparative analysis involves extracting over 9 000 time-series analysis features from each FHR time series, including measures of autocorrelation, entropy, distribution, and various model fits. This diverse collection of features was developed in previous work . We describe five features that most accurately classify a balanced training set of 59 low pH and 59 normal pH FHR recordings. We then describe five of the features with the strongest linear correlation to cord pH across the full dataset of FHR time series. The features identified in this work may be used as part of a system for guiding intervention during labor in future. This work successfully demonstrates the utility of comparing across a large, interdisciplinary literature on time-series analysis to automatically contribute new scientific results for specific biomedical signal processing challenges. © 2012 IEEE.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
3135 - 3138