NDLI: Identification of fetal sufferance antepartum through a multiparametric analysis and a support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Magenes, G. Pedrinazzi, L. Signorini, M.G.
Copyright Year	2004
Description	Author affiliation: Dipartimento di Informatica e Sistemistica, Pavia Univ., Italy (Magenes, G.; Pedrinazzi, L.)
Abstract	The present work is concerned with the automatic identification of fetal sufferance in intrauterine growth retarded (IUGR) fetuses, based on a multiparametric analysis of cardiotocographic recordings feeding a neural classifier. As classification tool, we propose a SVM (support vector machine), which receives the set of linear and nonlinear parameters extracted from the fetal heart rate signal (FHR) as input and gives the indication of fetal distress as output. SVM is a powerful supervised learning algorithm belonging to the statistical learning theory. It minimizes the structural risk performance in various classification problems. Three SVMs are built with different kernels. Their training set includes 70 cases: 35 normal and 35 IUGR suffering fetuses. Classification results obtained with a 2nd order polynomial kernel, on a test set of 30 unknown cases, show good values of accuracy, specificity and sensitivity. The SVM performance is very similar to that obtained with multilayer perceptron and neurofuzzy classifiers proposed in previous works. The introduction of a hybrid unsupervised/supervised learning scheme integrating independent component analysis (ICA) with SVM will be the natural development of this work with a further improvement of the diagnostic ability of the system.
Starting Page	462
Ending Page	465
File Size	1445963
Page Count	4
File Format	PDF
ISBN	0780384393
DOI	10.1109/IEMBS.2004.1403194
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-09-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Fetal heart rate Supervised learning Kernel Independent component analysis Cardiography Statistical learning Polynomials Testing Support Vector Machines Fetal Monitoring Multiparametric Analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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