PMID- 17271713
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20070517
LR  - 20200309
IS  - 1557-170X (Print)
IS  - 1557-170X (Linking)
VI  - 2006
DP  - 2004
TI  - Identification of fetal sufferance antepartum through a multiparametric analysis 
      and a support vector machine.
PG  - 462-5
AB  - 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.
FAU - Magenes, G
AU  - Magenes G
AD  - Dipartimento di Informatica e Sistemistica, Pavia Univ., Italy.
FAU - Pedrinazzi, L
AU  - Pedrinazzi L
FAU - Signorini, M G
AU  - Signorini MG
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Conf Proc IEEE Eng Med Biol Soc
JT  - Conference proceedings : ... Annual International Conference of the IEEE 
      Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and 
      Biology Society. Annual Conference
JID - 101243413
EDAT- 2007/02/03 09:00
MHDA- 2007/02/03 09:01
CRDT- 2007/02/03 09:00
PHST- 2007/02/03 09:00 [pubmed]
PHST- 2007/02/03 09:01 [medline]
PHST- 2007/02/03 09:00 [entrez]
AID - 10.1109/IEMBS.2004.1403194 [doi]
PST - ppublish
SO  - Conf Proc IEEE Eng Med Biol Soc. 2004;2006:462-5. doi: 
      10.1109/IEMBS.2004.1403194.