Assessment of features for automatic CTG analysis based on expert annotation

• Main Authors: Chudacek, V., Spilka, J., Lhotska, Lenka, Janku, P., Koucky, M., Huptych, M., Bursa, M.
• Format: Conference Proceeding
• Language: English
• Subjects: Cardiography; Complexity theory; Correlation; Entropy; Feature extraction; Fetal heart rate; Guidelines
• ISSN: 1094-687X; 1558-4615; 2694-0604
• DOI: 10.1109/IEMBS.2011.6091495

Cardiotocography (CTG) is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO) since 1960's used routinely by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the ever-used features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and the features are assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. Annotation derived from the panel of experts instead of the commonly utilized pH values was used for evaluation of the features on a large data set (552 records). We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. Number of acceleration and deceleration, interval index, as well as Lempel-Ziv complexity and Higuchi's fractal dimension are among the top five features.