Key result
Three-feature LS-SVM classifier outperforms baseline linear models for detecting fetal acidosis with ~68% sensitivity.
An LS-SVM classifier using pH values for fetal heart rate classification outperforms baseline methods, establishing a benchmark for the CTU-UHB CTG database.
May aid intrapartum acidosis detection; hypothesis-generating and requires prospective validation before clinical use.
Cardiotocography (CTG) is a standard tool for the assessment of fetal well-being during pregnancy and delivery. However, its interpretation is associated with high inter- and intra-observer variability. Since its introduction there have been numerous attempts to develop computerized systems assisting the evaluation of the CTG recording. Nevertheless these systems are still hardly used in a delivery ward. Two main approaches to computerized evaluation are encountered in the literature; the first one emulates existing guidelines, while the second one is more of a data-driven approach using signal processing and computational methods. The latter employs preprocessing, feature extraction/selection and a classifier that discriminates between two or more classes/conditions. These classes are often formed using the umbilical cord artery pH value measured after delivery. In this work an approach to Fetal Heart Rate (FHR) classification using pH is presented that could serve as a benchmark for reporting results on the unique open-access CTU-UHB CTG database, the largest and the only freely available database of this kind. The overall results using a very small number of features and a Least Squares Support Vector Machine (LS-SVM) classifier, are in accordance to the ones encountered in the literature and outperform the results of a baseline classification scheme proving the utility of using advanced data processing methods. Therefore the achieved results can be used as a benchmark for future research involving more informative features and/or better classification algorithms.
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Georgoulas et al. (2017) studied Fetal distress (fetal acidosis) (n=552). Least Squares Support Vector Machine (LS-SVM) classifier using 3 features vs. Minimum Mahalanobis Distance Classifier (MMDC) was evaluated on Classification sensitivity for fetal acidosis (pH ≤ 7.05). A Least Squares Support Vector Machine classifier using only three fetal heart rate features achieved 68.48% sensitivity and 77.68% specificity for detecting fetal acidosis, outperforming a baseline linear classifier.
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