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

Cardiotocography (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...

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Bibliographic Details
Published in:	Biomedical engineering online Vol. 10; no. 1; p. 6
Main Authors:	Krupa, Niranjana, Ali, Mohd, Zahedi, Edmond, Ahmed, Shuhaila, Hassan, Fauziah M
Format:	Journal Article
Language:	English
Published:	England BioMed Central Ltd 19-01-2011 BioMed Central BMC
Subjects:	Cardiotocography - methods Clinical medicine Decomposition Female Fetal heart rate Fetal heart rate monitoring Fetus - physiology Fetuses Fourier transforms Gynecology Heart Rate, Fetal - physiology Humans Hypoxia Methods Nonlinear Dynamics Pattern recognition Physiological aspects Pregnancy Signal processing Signal Processing, Computer-Assisted - instrumentation Studies Wavelet transforms Iran Malaysia
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Summary:	Cardiotocography (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'. The 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. For 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. Based 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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Undefined-2
ISSN:	1475-925X 1475-925X
DOI:	10.1186/1475-925X-10-6