Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Cardiotocography is a biophysical method of fetal state evaluation involving the recording and analysis of the fetal heart rate (FHR). Since a proper interpretation of the signal is relatively difficult, an automatic classification is often based on computational intelligence methods. The quality of classifiers based on supervised learning algorithms depends on a proper selection of learning data. In case of the fetal state evaluation, the learning is usually based on a set of quantitative parameters of FHR signal and the corresponding reference information determined on the basis of the retrospective analysis of newborn attributes. Values of the single attribute have been used so far as a reference. As a result, a part of information on the actual neonatal outcome has always been lost. The following paper presents a method of the fuzzy reasoning leading to an evaluation of neonatal outcome as a function of three newborn attributes. The fuzzy system was used in the process of a qualitative evaluation of the fetal state based on quantitative analysis of FHR signal using a support vector machine (SVM). In order to improve computational effectiveness, the learning algorithm was implemented in Compute Unified Device Architecture (CUDA). The results of these studies confirm the effectiveness of the proposed method and indicate the possibility of practical usage of the fuzzy system in supervised learning algorithms for the qualitative evaluation of the fetal state.
Rocznik
Tom
Strony
125--133
Opis fizyczny
Bibliogr. 19 poz., tab., wykr.
Twórcy
autor
- Silesian University of Technology, Institute of Electronics, Akademicka Str. 16, 44-100 Gliwice, Poland.
autor
- Institute of Medical Technology and Equipment, Roosevelt Str. 118, 41-800 Zabrze, Poland
autor
- Institute of Medical Technology and Equipment, Roosevelt Str. 118, 41-800 Zabrze, Poland
Bibliografia
- [1] BYUN H., LEE S. W., Applications of support vector machines for pattern recognition: A survey. In Proceedings of the 1st International Workshop on Pattern Recognition with Support Vector Machines SVM’02, London: Springer-Verlag, 2002, pp. 213-236.
- [2] CHUDACEK V., SPILKA J., JANKU P., KOUCKY M., LHOTSKA L., HUPTYCH M., Automatic evaluation of intrapartum fetal heart rate recordings: a comprehensive analysis of useful features. Physiol Meas, 2011, Vol. 32, pp. 1347-1360.
- [3] CHUDACEK V., SPILKA J., HUPTYCH M., GEORGOULAS G., LHOTSKA L., STYLIOS C.D., KOUCKY M., JANKU P., Linear and Non-Linear Features for Intrapartum Cardiotocography Evaluation, Computers in Cardiology, 2010, Vol. 37, pp. 999-1002.
- [4] CZABAŃSKI R., JEŻZEWSKI M., WRÓBEL J., HOROBA K., JEŻEWSKI J., A neuro-fuzzy approach to the classification of fetal cardiotocograms. Proc. Vol. 20, 14th International Conference NBC2008, Latvia, 2008, pp. 446-449.
- [5] CZABAŃSKI R., JEŻEWSKI M., WRÓBEL J., JEŻEWSKI J., HOROBA K., Predicting the risk of low-fetal birth weight from cardiotocographic signals using ANBLIR system with deterministic annealing and -insensitive learning. IEEE Trans. on Information Technology in Biomedicine, 2010, Vol. 14, pp. 1062-1074.
- [6] CZABAŃSKI R., JEŻEWSKI J., MATONIA A. JEŻEWSKI M., Computerized Analysis of Fetal Heart Rate Signals as the Predictor of Neonatal Acidemia, Expert Systems with Applications, 2012, Vol. 39, pp. 11846-11860.
- [7] CZABAŃSKI R., ROJ D., JEŻEWSKI J., HOROBA K., JEŻEWSKI M., Fuzzy prediction of fetal acidemia, Journal Of Medical Informatics & Technologies, 2011, Vol. 17, pp. 81-87.
- [8] FRIZE M., IBRAHIM D., SEKER H.,WALKER R., ODETAYO M., PETROVIC D., NAGUIB R., Predicting clinical outcomes for newborns using two artificial intelligence approaches. Proc. Vol. 2, IEEE IEMBS’04, USA, 2004, pp. 3202-3205.
- [9] GEORGOULAS G., STYLIOS C., CHUDACEK V., MACAS M., BERNARDES J., LHOTSKA L., Classification of Fetal Heart Rate Signals Based on Features Selected Using the Binary Particle Swarm Algorithm, 2007, Proc. Vol. 14, IFMBE’06, pp. 1156-1159.
- [10] GEORGOULAS G., STYLIOS C., GROUMPOS P., Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines. IEEE Trans. on Biomedical Engineering, 2006, Vol. 53, pp. 875-884.
- [11] HUANG Y. P., HUANG Y. H., SANDNES F. E., A fuzzy inference method-based fetal distress monitoring system, 2006, Proc. Vol. 1, IEEE ISIE’06, Canada, pp. 55-60.
- [12] JEŻEWSKI M., CZABAŃSKI R., WBÓBEL J., HOROBA K., Analysis of extracted cardiotocographic signal features to improve automated prediction of fetal outcome. Biocybernetics and Biomedical Engineering, 2010, Vol. 30, pp. 39-47.
- [13] JEŻEWSKI J., WRÓBEL J., HOROBA K., KUPKA T., MATONIA A., Centralised fetal monitoring system with hardware-based data flow control. Proc. IET 3rd MEDSIP06, United Kingdom, 2006, pp. 1-4.
- [14] KRUPA N., MA M., ZAHEDI E., AHMED S., HASSAN F., Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine. BioMedical Engineering OnLine, 2011, Vol. 10, pp. 1-15.
- [15] MANGASARIAN O. L., MUSICANT D. R., Lagrangian support vector machines. Journal of Machine Learning Research, 2001, Vol. 1, pp. 161-177.
- [16] NOGUCHI Y., MATSUMOTO F., MAEDA K., NAGASAWA T., Neural network analysis and evaluation of the fetal heart rate. Algorithms, 2009, Vol. 2, pp. 19-30.
- [17] SKINNER J., GARIBALDI J., IFEACHOR E., A fuzzy system for fetal heart rate assessment, LNCS, 1999, Vol. 1625, pp. 20-29.
- [18] SPILKA J., CHUDACEK V., KOUCKY M., LHOTSKA L., HUPTYCH M., JANKU P., GEORGOULAS G., STYLIOS C. D., Using nonlinear features for fetal heart rate classification, Biomedical Signal Processing and Control, 2011.
- [19] VAPNIK V., Statistical Learning Theory. New York: Wiley, 1998.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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