A study of artificial neural network training algorithms for classification of cardiotocography signals
Abstract
Cardiotocography (CTG) containing of fetal heart rate (FHR) and uterine contraction (UC) signals is a
monitoring technique. During the last decades, FHR signals have been classified as normal, suspicious,
and pathological using machine learning techniques. As a classifier, artificial neural network (ANN) is
notable due to its powerful capabilities. For this reason, behaviors and performances of neural network
training algorithms were investigated and compared on classification task of the CTG traces in this
study. Training algorithms of neural network were categorized in five group as Gradient Descent,
Resilient Backpropagation, Conjugate Gradient, Quasi-Newton, and Levenberg-Marquardt. Two
different experimental setups were performed during the training and test stages to achieve more
generalized results. Furthermore, several evaluation parameters, such as accuracy (ACC), sensitivity
(Se), specificity (Sp), and geometric mean (GM), were taken into account during performance
comparison of the algorithms. An open access CTG dataset containing 2126 instances with 21 features
and located under UCI Machine Learning Repository was used in this study. According to the results of
this study, all training algorithms produced rather satisfactory results. In addition, the best classification
performances were obtained with Levenberg-Marquardt backpropagation (LM) and Resilient
Backpropagation (RP) algorithms. The GM values of RP and LM were obtained as 89.69% and 86.14%,
respectively. Consequently, this study confirms that ANN is a useful machine learning tool to classify
FHR recordings.
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