The influence of cardiotocogram signal feature selection method on fetal state assessment efficacy

• Autorzy: Jeżewski M. , Czabański R. , Łęski J.
• Języki publikacji: EN

Abstrakty

EN

Cardiotocographic (CTG) monitoring is a method of assessing fetal state. Since visual analysis of CTG signal is difficult, methods of automated qualitative fetal state evaluation on the basis of the quantitative description of the signal are applied. The appropriate selection of learning data influences the quality of the fetal state assessment with computational intelligence methods. In the presented work we examined three different feature selection procedures based on: principal components analysis, receiver operating characteristics and guidelines of International Federation of Gynecology and Obstetrics. To investigate their influence on the fetal state assessment quality the benchmark SisPorto® dataset and the Lagrangian support vector machine were used.

Słowa kluczowe

EN

cardiotocography; classification; feature selection

PL

kardiotokografia; klasyfikacja; selekcja cech

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2014
• Tom: Vol. 23
• Strony: 51--58
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

Jeżewski M.

• Silesian University of Technology, Institute of Electronics, Akademicka Str. 16, 44-100 Gliwice, Poland

autor

Czabański R.

• Silesian University of Technology, Institute of Electronics, Akademicka Str. 16, 44-100 Gliwice, Poland

autor

Łęski J.

• Silesian University of Technology, Institute of Electronics, Akademicka Str. 16, 44-100 Gliwice, Poland

Bibliografia

• [1] AYRES-DE-CAMPOS D., BERNARDES J., GARRIDO A., et al., SisPorto 2.0: A program for automated analysis of cardiotocograms. Journal of Maternal-Fetal Medicine, 2000, Vol. 9, pp. 311–318.
• [2] BACHE K., LICHMAN M., UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science, 2013.
• [3] CHUDACEK V., SPILKA J., RUBACKOVA B., et al., Evaluation of feature subsets for classification of cardiotocographic recordings. Computers in Cardiology, 2008, Vol. 35, pp. 845-848.
• [4] CZABAŃSKI R., JEŻEWSKI J., MATONIA A., et al., Computerized analysis of fetal heart rate signals as the predictor of neonatal acidemia. Expert Systems with Applications, 2012, Vol. 39, pp. 11846-11860.
• [5] CZABAŃSKI R., JEŻEWSKI M., ROJ D., et al., Evaluation of predictive capabilities of quantitative cardiotocographic signal features. Journal of Medical Informatics and Technologies, 2010, Vol. 16, pp. 11-17.
• [6] CZABAŃSKI R., JEŻEWSKI M., WRÓBEL J., et al., Neuro-fuzzy approach to the classification of fetal cardiotocograms, IFMBE Proc. of the 14th Nordic Baltic Conference on Biomedical Engineering and Medical Physics, 2008, Vol. 20, pp. 446-449.
• [7] CZABAŃSKI R., JEŻEWSKI M., WRÓBEL J., et al., Predicting the risk of low-fetal birth weight from cardiotocographic signals using ANBLIR system with deterministic annealing and "-insensitive learning. IEEE Transactions on Information Technology in Biomedicine, 2010, Vol. 14, No. 4, pp. 1062-1074.
• [8] ESFANDIARI N., BABAVALIAN M. R., MOGHADAM A.-M.E., et al., Knowledge discovery in medicine: Current issue and future trend. Expert Systems with Applications, 2014, Vol. 41, pp. 4434-4463.
• [9] FAWCETT T., An introduction to ROC analysis. Pattern Recognition Letters, 2006, Vol. 27, pp. 861-874.
• [10] GEORGIEVA A., PAYNE S. J., MOULDEN M., et al., Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications, 2011, Vol. 22, pp. 85-93.
• [11] JEŻEWSKI J., WRÓBEL J., HOROBA K., et al., Fetal heart rate variability: clinical experts versus computerized system interpretation. Proc. of the 24th Annual International Conference of the IEEE EMBS, 2002, pp. 1617-1618.
• [12] JEŻEWSKI J., WRÓBEL J., HOROBA K., et al., Computerized perinatal database for retrospective qualitative assessment of cardiotocographic traces. Proc. of the Healthcare Computing Conference HC96, in Current Perspectives in Healthcare Computing, Editor: Richards B., BJHC Ltd, Great Britain, 1996, pp. 187-196.
• [13] JEŻEWSKI M., WRÓBEL J., ŁABAJ P., et al., Some practical remarks on neural networks approach to fetal cardiotocograms classification. Proc. of the 29th Annual International Conference of the IEEE EMBS, 2007, pp. 5170-5173.
• [14] LABATUT V., CHERIFI H., Accuracy measures for the comparison of classifiers. Proc. of the 5th International Conference on Information Technology, 2011.
• [15] MANGASARIAN O.L., MUSICANT D.R., Lagrangian support vector machines. Journal of Machine Learning Research, 2001, Vol. 1, pp. 161-177.
• [16] MATLAB Documentation, The MathWorks, Inc.
• [17] MENAI M.E.B., MOHDER F.J., AL-MUTAIRI F., Influence of feature selection on naïve Bayes classifier for recognizing patterns in cardiotocograms. Journal of Medical and Bioengineering, 2013, Vol. 2, No. 1, pp. 66-70.
• [18] NOGUCHI Y., MATSUMOTO F., MAEDA K., et al., Neural network analysis and evaluation of the fetal heart rate. Algorithms, 2009, Vol. 2, pp. 19-30.
• [19] OCAK H., A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being. Journal of Medical Systems, 2013, Vol. 37, pp. 9913.
• [20] OCAK H., ERTUNC H.M., Prediction of fetal state from the cardiotocogram recordings using adaptive neuro-fuzzy inference systems. Neural Computing and Applications, 2012, Vol. 23, pp. 1583-1589.
• [21] ROOTH G., Guidelines for the use of fetal monitoring. International Journal of Obstetrics & Gyneacology, 1987, Vol. 25, pp. 159–167.
• [22] SIGNORINI M.G., DE ANGELIS A., MAGENES G., et al., Classification of fetal pathologies through fuzzy inference systems based on a multiparametric analysis of fetal heart rate. Computers in Cardiology, 2000, Vol. 27, pp. 435-438.
• [23] SOKOLOVA M., LAPALME G., A systematic analysis of performance measures for classification tasks. Information Processing and Management, 2009, Vol. 45, pp. 427–437.
• [24] XU L., GEORGIEVA A., REDMAN C.W.G., et al., Feature selection for computerized fetal heart rate analysis using genetic algorithms. Proc. of the 35th Annual International Conference of the IEEE EMBS, 2013, pp. 445-448.
• [25] YILMAZ E., KILIKCIER C., Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree. Computational and Mathematical Methods in Medicine, 2013, Vol. 2013, pp. 1-8, Article ID 487179.