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

  • Authors:

  • Robert Czabanski;Michal Jezewski;Janusz Wrobel;Janusz Jezewski;Krzysztof Horoba

  • Affiliations:

  • Institute of Electronics, Division of Biomedical Electronics, Silesian University of Technology, Gliwice, Poland;Institute of Electronics, Division of Biomedical Electronics, Silesian University of Technology, Gliwice, Poland;Department of Biomedical Informatics, Institute of Medical Technology and Equipment, Zabrze, Poland;Department of Biomedical Informatics, Institute of Medical Technology and Equipment, Zabrze, Poland;Department of Biomedical Informatics, Institute of Medical Technology and Equipment, Zabrze, Poland

  • Venue:
  • IEEE Transactions on Information Technology in Biomedicine
  • Year:
  • 2010

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Abstract

Cardiotocography (CTG) is a biophysical method of fetal condition assessment based mainly on recording and automated analysis of fetal heart activity. The computerized fetal monitoring systems provide the quantitative description of the CTG signals, but the effective conclusion generation methods for decision process support are still needed. Assessment of the fetal state can be verified only after delivery using the fetal (newborn) outcome data. One of the most important features defining the abnormal fetal outcome is low birth weight. This paper describes an application of the artificial neural network based on logical interpretation of fuzzy if-then rules neurofuzzy system to evaluate the risk of low-fetal birth weight using the quantitative description of CTG signals. We applied different learning procedures integrating least squares method, deterministic annealing (DA) algorithm, and Ɛ:-insensitive learning, as well as various methods of input dataset modification. The performance was evaluated with the number of correctly classified cases (CC) expressed as the percentage of the testing set size, and with overall index (OI) being the function of predictive indexes. The best classification efficiency (CC = 97.5% and OI = 82.7%), was achieved for integrated DA with Ɛ:-insensitive learning and dataset comprising of the CTG traces recorded as earliest for a given patient. The obtained results confirm efficiency for supporting the fetal outcome prediction using the proposed methods.