Classification of Fetal State from the Cardiotocogram Recordings using ANN and Simple Logistic

In this study, we present a comparison of machine learning technics using antepartum cardiotocographs performed by SisPorto 2.0 in predicting newborn outcome. CTG is widely used in pregnancy as a technique of measuring fetal well-being, mainly in pregnancies with increased risk of complications. It is a non-invasive way for checking the fetal conditions in the antepartum period. CTG is a continuous electronic record of the baby’s heart rate acquired via an ultrasound transducer placed on the mother’s abdomen. The information efficiently took out from these recordings can be used to envisage pathological state of the fetus and makes an early intervention possible before there is a permanent damage to the fetus. Using features extracted from the FHR and UC signals, the techniques ANN and Simple Logistic was trained to predict the normal and the pathological state. The dataset which consist of 1831 instances with 21 attributes was tested by using the methods which is mentioned above. The CTG recordings were also categorized 1655 of them as normal and 176 of them as pathological by three expert obstetricians’ consensus. They were showed that ANN and Simple Logistic based methods were able to classify the data as normal and pathological with 98.5% and 98.7% accuracy, respectively. Keywords: Cardiotocogram, CTG, SisPorto, Artificial Neural Network (ANN), Simple Logistic, feotus

[1]  Alan Agresti,et al.  Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.

[2]  Maria Romano,et al.  Comparison of short term variability indexes in cardiotocographic foetal monitoring , 2009, Comput. Biol. Medicine.

[3]  A. Asuncion,et al.  UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences , 2007 .

[4]  G Bortolan,et al.  Assessment and comparison of different methods for heartbeat classification. , 2008, Medical engineering & physics.

[5]  J. Chimka.  Categorical Data Analysis, Second Edition , 2003 .

[6]  B Stigsby,et al.  Intra- and inter-observer variability in the assessment of intrapartum cardiotocograms. , 1988, Acta obstetricia et gynecologica Scandinavica.

[7]  A Hasman,et al.  Interobserver variation in the assessment of fetal heart rate recordings. , 1993, European journal of obstetrics, gynecology, and reproductive biology.

[8]  A. Lalonde,et al.  The reproducibility of intrapartum cardiotocogram assessments. , 1982, Canadian Medical Association journal.

[9]  H. Geijn.  2 Developments in CTG analysis , 1996 .

[10]  P. Nielsen,et al.  Intra‐ and inter‐observer variability in the assessment of intrapartum cardiotocograms , 1987 .

[11]  Tiina Luukkaala,et al.  Intrapartum cardiotocography – the dilemma of interpretational variation , 2006, Journal of Perinatal Medicine.

[12]  Jacob Cohen,et al.  Applied multiple regression/correlation analysis for the behavioral sciences , 1979 .

[13]  J. Waller,et al.  A comparison of visual analyses of intrapartum fetal heart rate tracings according to the new national institute of child health and human development guidelines with computer analyses by an automated fetal heart rate monitoring system. , 2000, American journal of obstetrics and gynecology.

[14]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[15]  D. Devane,et al.  Midwives' visual interpretation of intrapartum cardiotocographs: intra- and inter-observer agreement. , 2005, Journal of advanced nursing.

[16]  G. Magenes,et al.  Identification of fetal sufferance antepartum through a multiparametric analysis and a support vector machine , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Everett F. Magann,et al.  Intrapartum nonreassuring fetal heart rate tracing and prediction of adverse outcomes: interobserver variability. , 2008, American journal of obstetrics and gynecology.