Two-Step analysis of the fetal heart rate signal as a predictor of distress

Authors:
Robert Czabanski;Janusz Wrobel;Janusz Jezewski;Michal Jezewski
Affiliations:
Institute of Electronics, Silesian University of Technology, Gliwice, Poland;Biomedical Signal Processing Dept., Institute of Medical Technology and Equipment, Zabrze, Poland;Biomedical Signal Processing Dept., Institute of Medical Technology and Equipment, Zabrze, Poland;Institute of Electronics, Silesian University of Technology, Gliwice, Poland
Venue:
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2012

Citing 3
Cited 0

Support-Vector Networks

Machine Learning
Lagrangian support vector machines

The Journal of Machine Learning Research
Predicting the risk of low-fetal birth weight from cardiotocographic signals using ANBLIR system with deterministic annealing and ε-insensitive learning

IEEE Transactions on Information Technology in Biomedicine

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Abstract

Cardiotocography is a biophysical method of fetal state assessment based on analysis of fetal heart rate signal (FHR). The computerized fetal monitoring systems provide a quantitative evaluation of FHR signals, however the effective methods for fetal outcome prediction are still needed. The paper proposes a two-step analysis of fetal heart rate recordings that allows for prediction of the fetal distress. The first step consists in classification of FHR signals with Weighted Fuzzy Scoring System. The fuzzy inference that corresponds to the clinical interpretation of signals based on the FIGO guidelines enables to designate recordings indicating the fetal wellbeing. In the second step, the remained recordings are classified using Lagrangian Support Vector Machines (LSVM). The evaluation of the proposed procedure using data collected with computerized fetal surveillance system confirms its efficacy in predicting the fetal distress.