Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine

Abstract

BackgroundCardiotocography (CTG) is the most widely used tool for fetal surveillance. The visual analysis of fetal heart rate (FHR) traces largely depends on the expertise and experience of the clinician involved. Several approaches have been proposed for the effective interpretation of FHR. In this paper, a new approach for FHR feature extraction based on empirical mode decomposition (EMD) is proposed, which was used along with support vector machine (SVM) for the classification of FHR recordings as 'normal' or 'at risk'.MethodsThe FHR were recorded from 15 subjects at a sampling rate of 4 Hz and a dataset consisting of 90 randomly selected records of 20 minutes duration was formed from these. All records were labelled as 'normal' or 'at risk' by two experienced obstetricians. A training set was formed by 60 records, the remaining 30 left as the testing set. The standard deviations of the EMD components are input as features to a support vector machine (SVM) to classify FHR samples.ResultsFor the training set, a five-fold cross validation test resulted in an accuracy of 86% whereas the overall geometric mean of sensitivity and specificity was 94.8%. The Kappa value for the training set was .923. Application of the proposed method to the testing set (30 records) resulted in a geometric mean of 81.5%. The Kappa value for the testing set was .684.ConclusionsBased on the overall performance of the system it can be stated that the proposed methodology is a promising new approach for the feature extraction and classification of FHR signals.

Analysis of Cardiotocography Findings in Pregnancy with Less Fetal Movement and Its Association with Perinatal Outcome

“…A predictive fetal heart rate record is the principle indication that led to intervention and delivery. A prediction of fetal asphyxia that leads to intervention and delivery may prevent or modify moderate or severe newborn morbidity as the result of fetal asphyxia 13 .…”

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

“…Huang [42] analyzed the CTG data by three machine learning methods to create the classification models to predict fetal distress. Krupa et al [43] suggested using statistical features extracted from empirical mode decomposition (EMD). The extracted features from the decomposed components classified as normal and at risk.…”

Automated evaluation of fetal cardiotocograms using neural network

“…Some have combined empirical mode decomposition (EMD) and support vector machines (SVM). This actually resulted in a test accuracy of 86% [22].…”