Fuzzy Analysis of Delivery Outcome Attributes for Improving the Automated Fetal State Assessment

References

• Alamedine, D., M. Khalil, and C. Marque. 2013. Comparison of different EHG feature selection methods for the detection of preterm labor. Computational and Mathematical Methods in Medicine 2013:1–9. doi:10.1155/2013/485684.
   Web of Science ®Google Scholar
• Catley, C., M. Frize, C. Walker, and D. Petriu. 2006. Predicting high-risk preterm birth using artificial neural networks. IEEE Transactions on Information Technology in Biomedicine 10:540–549. doi:10.1109/TITB.2006.872069.
   PubMedGoogle Scholar
• Chudacek, V., J. Spilka, P. Janku, M. Koucky, L. Lhotska, and M. Huptych. 2011. Automatic evaluation of intrapartum fetal heart rate recordings: A comprehensive analysis of useful features. Physiological Measurement 32:1347–1360. doi:10.1088/0967-3334/32/8/022.
   PubMed Web of Science ®Google Scholar
• Czabanski, R., J. Jezewski, A. Matonia, and M. Jezewski. 2012. Computerized analysis of fetal heart rate signals as the predictor of neonatal acidemia. Expert Systems with Applications 39:11846–11860. doi:10.1016/j.eswa.2012.01.196.
   Web of Science ®Google Scholar
• Czabanski, R., J. Jezewski, J. Wrobel, J. Sikora, and M. Jezewski. 2013. Application of fuzzy inference systems for classification of fetal heart rate tracings in relation to neonatal outcome. Ginekologia Polska 84:38–43.
   PubMed Web of Science ®Google Scholar
• Czabanski, R., M. Jezewski, J. Wrobel, K. Horoba, and J. Jezewski. 2008. A neuro-fuzzy approach to the classification of fetal cardiotocograms. In 14th Nordic-Baltic conference on biomedical engineering and medical physics, vol 20 of IFMBE proceedings, ed. A. Katashev, Y. Dekhtyar, and J. Spigulis, 446–449. Berlin, Heidelberg: Springer.
   Google Scholar
• Czabanski, R., M. Jezewski, J. Wrobel, J. Jezewski, and K. Horoba. 2010. 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 14:1062–1074. doi:10.1109/TITB.2009.2039644.
   PubMedGoogle Scholar
• Czabanski, R., J. Wrobel, J. Jezewski, J. Leski, and M. Jezewski. 2015. Efficient evaluation of fetal wellbeing during pregnancy using methods based on statistical learning principles. Journal of Medical Imaging and Health Informatics 5:1327–1336. doi:10.1166/jmihi.2015.1536.
   Web of Science ®Google Scholar
• Dash, S., J. Muscat, J. Quirk, and P. Djuric. 2012, March. Classification of fetal heart rate series. In IEEE international conference on acoustics, speech and signal processing (ICASSP), 629–632. IEEE.
   Google Scholar
• Dash, S., J. Quirk, and P. Djuric. 2014. Fetal heart rate classification using generative models. IEEE Transactions on Biomedical Engineering 61:2796–2805. doi:10.1109/TBME.2014.2330556.
   PubMed Web of Science ®Google Scholar
• Esfandiari, N., M. Babavalian, A.-M. Moghadam, and V. Tabar. 2014. Knowledge discovery in medicine: Current issue and future trend. Expert Systems with Applications 41:4434–4463. doi:10.1016/j.eswa.2014.01.011.
   Web of Science ®Google Scholar
• Georgoulas, G., C. Stylios, and P. Grumpos. 2005. Investigation and comparison of different scale dependent features for fetal heart rate classification. Paper presented at Proceedings of the 16th International Federation of Automatic Control World Congress, Prague, Czech Republic, July 4–8, (CD-ROM).
   Google Scholar
• Georgoulas, G., D. Stylios, and P. Groumpos. 2006. Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines. IEEE Transactions on Biomedical Engineering 53:875–884. doi:10.1109/TBME.2006.872814.
   PubMed Web of Science ®Google Scholar
• Guidi, G., G. Adembri, S. Vannuccini, and E. Iadanza. 2014. Predictability of some pregnancy outcomes based on svm and dichotomous regression techniques. In Ambient assisted living and daily activities, ed. L. Pecchia, L. L. Chen, C. Nugent, and J. Bravo, 163–166. Lecture Notes in Computer Science 8868. Cham, Switzerland: Springer International.
   Google Scholar
• Hannah Inbarani, H., P. Nizar Banu, and A. Azar. 2014. Feature selection using swarm-based relative reduct technique for fetal heart rate. Neural Computing and Applications 25:793–806. doi:10.1007/s00521-014-1552-x.
   Web of Science ®Google Scholar
• Jadhav, S., S. Nalbalwar, and S. Ghatol. 2011. Modular neural network model based foetal state classification. Paper presented at Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshops, November 12–15, 915–917, Atlanta, Georgia, United States.
   Google Scholar
• Jezewski, M., R. Czabanski, J. Wrobel, and K. Horoba. 2010. Analysis of extracted cardiotocographic signal features to improve automated prediction of fetal outcome. Biocybernetics and Biomedical Engineering 30:29–47.
   Web of Science ®Google Scholar
• Jezewski, J., D. Roj, J. Wrobel, and K. Horoba. 2011. A novel technique for fetal heart rate estimation from Doppler ultrasound signal. BioMedical Engineering OnLine 10:1–17. doi:10.1186/1475-925X-10-92.
   PubMed Web of Science ®Google Scholar
• Jezewski, M., J. Wrobel, K. Horoba, A. Gacek, N. Henzel, and J. Leski. 2007. The prediction of fetal outcome by applying neural network for evaluation of ctg records. In Computer recognition systems 2, ed. M. Kurzynski, E. Puchala, M. Wozniak, and A. Zolnierek, 532–541. Advances in Soft Computing 45. Berlin, Heidelberg: Springer-Verlag.
   Google Scholar
• Jezewski, M., J. Wrobel, P. Labaj, J. Leski, N. Henzel, K. Horoba, and J. Jezewski. 2007. Some practical remarks on neural networks approach to fetal cardiotocograms classification. In Proceedings of the 29th annual international conference of the IEEE engineering in medicine and biology society, 5170–5173. IEEE.
   Google Scholar
• Kupka, T., J. Jezewski, A. Matonia, K. Horoba, and J. Wrobel. 2004. Timing events in Doppler ultrasound signal of fetal heart activity. In Engineering in medicine and biology society, (IEMBS ’04) 26th annual international conference of the IEEE, vol. 1, 337–340. IEEE.
   Google Scholar
• Maeda, K., M. Utsu, Y. Noguchi, F. Matsumoto, and T. Nagasawa. 2012. Central computerized automatic fetal heart rate diagnosis with a rapid and direct alarm system. The Open Medical Devices Journal 4:28–33. doi:10.2174/1875181401204010028.
   Google Scholar
• Mangasarian, O., and D. Musicant. 2001. Lagrangian support vector machines. Journal of Machine Learning Research 1:161–177.
   Web of Science ®Google Scholar
• Noguchi, Y., F. Matsumoto, K. Maeda, and T. Nagasawa. 2009. Neural network analysis and evaluation of the fetal heart rate. Algorithms 2:19–30. doi:10.3390/a2010019.
   Google Scholar
• Rooth, G. 1987. Guidelines for the use of fetal monitoring. International Journal of Gynecology & Obstetrics 25:159–167.
   Google Scholar
• Sahin, H., and A. Subasi. 2015. Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques. Applied Soft Computing 33:231–238. doi:10.1016/j.asoc.2015.04.038.
   Web of Science ®Google Scholar
• Spilka, J., V. Chudacek, P. Janku, L. Hruban, M. Bursa, L. Huptych, M. Zach, and L. Lhotská. 2014. Analysis of obstetricians decision making on ctg recordings. Journal of Biomedical Informatics 51:72–79. doi:10.1016/j.jbi.2014.04.010.
   PubMed Web of Science ®Google Scholar
• Spilka, J., V. Chudacek, M. Koucky, L. Lhotska, M. Huptych, P. Janku, G. Georgoulas, and C. Stylios. 2012. Using nonlinear features for fetal heart rate classification. Biomedical Signal Processing and Control 7:350–357. doi:10.1016/j.bspc.2011.06.008.
   Web of Science ®Google Scholar
• Stylios, I., V. Vlachos, and I. Androulidakis. 2014. Performance comparison of machine learning algorithms for diagnosis of cardiotocograms with class inequality. In 22nd Telecommunications forum telfor (TELFOR), 951–954. IEEE.
   Google Scholar
• Takagi, T., and M. Sugeno. 1985. Fuzzy identification of systems and its application to modeling and control. IEEE Transactions System Manual and Cybernetics 15:116–132. doi:10.1109/TSMC.1985.6313399.
   Web of Science ®Google Scholar
• Tomas, P., J. Krohova, P. Dohnalek, and P. Gajdos. 2013. Classification of cardiotocography records by random forest. In Proceedings of the 36th international conference on telecommunications and signal processing (TSP), 620–923. IEEE.
   Google Scholar
• Vapnik, V. 1999. An overview of statistical learning theory. IEEE Transactions on Neural Networks 10:988–999. doi:10.1109/72.788640.
   PubMed Web of Science ®Google Scholar
• Warrick, P., E. Hamilton, D. Precup, and R. Kearney. 2010. Classification of normal and hypoxic fetuses from systems modeling of intrapartum cardiotocography. IEEE Transactions on Biomedical Engineering 57:771–779. doi:10.1109/TBME.2009.2035818.
   PubMed Web of Science ®Google Scholar
• Wrobel, J., K. Horoba, T. Pander, J. Jezewski, and R. Czabanski. 2013. Improving the fetal heart rate signal interpretation by application of myriad filtering. Biocybernetics and Biomedical Engineering 33:211–221. doi:10.1016/j.bbe.2013.09.004.
   Web of Science ®Google Scholar
• Wrobel, J., J. Jezewski, K. Horoba, A. Pawlak, R. Czabanski, M. Jezewski, and P. Porwik. 2015. Medical cyber-physical system for home telecare of high-risk pregnancy - design challenges and requirements. Journal of Medical Imaging and Health Informatics 5:1295–1301. doi:10.1166/jmihi.2015.1532.
   Web of Science ®Google Scholar
• Xu, L., C. Redman, S. Payne, and A. Georgieva. 2014. Feature selection using genetic algorithms for fetal heart rate analysis. Physiological Measurement 35:1357–1371. doi:10.1088/0967-3334/35/7/1357.
   PubMed Web of Science ®Google Scholar
• Yilmaz, E., and C. Kilikcier. 2013. Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree. Computational and Mathematical Methods in Medicine 2013:1–8.
   Web of Science ®Google Scholar