For many years, there has been a concerted effort to automate the analysis of fetal heart rate (FHR) rhythms. However, despite significant advances in biomedical signal analysis, there has not been any significant improvement in automated decision support systems. FHR monitoring is now ubiquitous throughout delivery rooms, especially using the non-invasive Doppler monitor, but also using the fetal scalp electrode. Physician classification of fetal heart rate patterns is known to be a non-trivial problem because of significant inter and intra-observer variability of diagnosis. This has led to a marked increase in the number of caesarean deliveries, thereby increasing risk to the fetus and mother in many cases. This has further motivated the machine learning community to automate the classification procedure in the interest of accuracy and consistency as well as robustness with respect to noise. Usual approaches to this involve some type of supervised classification procedure, where the algorithm output on training data is compared with a gold-standard physician classification, followed by testing and validation on new datasets. However, since physician classification can be unreliable in the presence of the aforementioned diagnostic variability, as well as significant tracing noise, we propose the use of unsupervised algorithms to cluster FHR data records into clinically useful categories. We use nonparametric Bayes theory and Markov-time-dependence models for the evolution of feature sequences to propose methods that will achieve improved accuracy. The methods involve extraction of feature sequences from FHR time series data, which are modeled as samples from finite or infinite Dirichlet mixture models. We then use Gibbs sampling to obtain the cluster probabilities for each dataset. Clustering outcomes are compared against direct physician diagnosis and our current results are seen to be in broad agreement with them, while still giving new information on the character of different sub-groups of FHR records. With the proposed research, further gains in classification performance will be made.

Public Health Relevance

Fetal heart rate monitoring is now commonly used during childbirth and, at present, physicians read and interpret these data to classify fetal heart rate patterns and make sure that the baby is not in distress during the course of labor. However, there is great variability in how individual doctors interpret the tracings and this has led increases in the number of caesarean deliveries, thereby potentially increasing risk to both mothers and babies. Thus there has been a concerted effort from the machine learning community to develop an accurate automatic reading and classification procedure so that correct interpretation of fetal heart rates during labor is more diagnostic and consistent.

National Institute of Health (NIH)
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Exploratory/Developmental Grants (R21)
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Special Emphasis Panel (ZRG1)
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Raju, Tonse N
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State University New York Stony Brook
Obstetrics & Gynecology
Schools of Medicine
Stony Brook
United States
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Yu, Kezi; Quirk, J Gerald; Djuri?, Petar M (2017) Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models. PLoS One 12:e0185417