Sparse Support Vector Machine for Intrapartum Fetal Heart Rate Classification

Abstract

Fetal heart rate (FHR) monitoring is routinely used in clinical practice to help obstetricians assess fetal health status during delivery. However, early detection of fetal acidosis that allows relevant decisions for operative delivery remains a challenging task, receiving considerable attention. This contribution promotes sparse support vector machine classification that permits to select a small number of relevant features and to achieve efficient fetal acidosis detection. A comprehensive set of features is used for FHR description, including enhanced and computerized clinical features, frequency domain, and scaling and multifractal features, all computed on a large (1288 subjects) and well-documented database. The individual performance obtained for each feature independently is discussed first. Then, it is shown that the automatic selection of a sparse subset of features achieves satisfactory classification performance (sensitivity 0.73 and specificity 0.75, outperforming clinical practice). The subset of selected features (average depth of decelerations MAD, baseline level β , and variability H) receives simple interpretation in clinical practice. Intrapartum fetal acidosis detection is improved in several respects: A comprehensive set of features combining clinical, spectral, and scale-free dynamics is used; an original multivariate classification targeting both sparse feature selection and high performance is devised; state-of-the-art performance is obtained on a much larger database than that generally studied with description of common pitfalls in supervised classification performance assessments.

Information Theory to Probe Intrapartum Fetal Heart Rate Dynamics

“…e.g. [35,24]. Whether or not temporal dynamics associated to each stage are different has not been intensively explored yet (see a contrario [36,37]).…”

“…There has also been several attempts to combine features different in nature by doing multivariate classification using supervised machine learning strategies (cf. e.g., [21,6,22,23,24]). Measures from Complexity Theory or Information Theory remain however amongst the most used tools to construct HRV characterization.…”

Comparison of Machine Learning Techniques for Fetal Heart Rate Classification

“…Support vector machine SVM is a kernel mapping technique that can be applied to separable and non-separable data for regression, classification, and other learning tasks. Kernel-based algorithms have good generalization performance, and they work well in practice [15]. The similarity or dissimilarity of data objects is measured by kernels used to store data.…”

Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

“…Formula (6) means that the information entropy of the ith particle is less than the information entropy of whole particle swarm, which is to say that the optimization process at this time is turning bad, and the population diversity is reducing gradually. In formula (7), F g is the global optimal fitness, F m is the average fitness of all particles at the current iteration; if the absolute value of the difference between these two is less than À F m N , the movement trending to the most optimal position of the particles should be enhanced at this point.…”

“…The results demonstrated that the somatic mutation information was useful for prediction of primary tumor sites with machine learning modeling. Spilka et al [6] promoted Sparse SVM classification that permitted to select a small number of relevant features and to achieve efficient fetal acidosis detection.…”