Clustering with Pairs of Prototypes to Support Automated Assessment of the Fetal State

References

• Ayres-de-Campos, D., J. Bernardes, A. Garrido, J. Marques-de-Sa, and L. Pereira-Leite. 2000. SisPorto 2.0: A program for automated analysis of cardiotocograms. Journal of Maternal-Fetal Medicine 9 (5):311–318.
   PubMedGoogle Scholar
• Bezdek, J. C. 1981. Pattern recognition with fuzzy objective function algorithms. New York, NY: Plenum Press.
   Google Scholar
• Chudacek, V., J. Spilka, M. Bursa, P. Janku, L. Hruban, M. Huptych, and L. Lhotska. 2014. Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14 (16):1–12. doi:10.1186/1471-2393-14-16.
   PubMed Web of Science ®Google Scholar
• Czabanski, R. 2006. Deterministic annealing integrated with ε-insensitive learning in neuro-fuzzy systems. In Proceedings of 8th international conference on artificial intelligence and soft computing (ICAISC 2006), ed. L. Rutkowski, R. Tadeusiewicz, L. A. Zadeh, and J. Zurada, 220–229, Lecture Notes in Artificial Intelligence 4029. Berlin Heidelberg: Springer-Verlag.
   Google Scholar
• Czabanski, R., J. Jezewski, K. Horoba, and M. Jezewski. 2013. Fetal state assessment using fuzzy analysis of fetal heart rate signals - Agreement with the neonatal outcome. Biocybernetics and Biomedical Engineering 33:145–155. doi:10.1016/j.bbe.2013.07.003.
   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., 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, ed. A. Katashev, Y. Dekhtyar, and J. Spigulis, 446–449, IFMBE Proceedings 20. Berlin Heidelberg: Springer-Verlag.
   Google Scholar
• Das, S., K. Roy, and C. K. Saha (2013). Application of FURIA in the classification of cardiotocograph. In IEEE-international conference on research and development prospects on engineering and technology (ICRDPET 2013) Vol. 4, 120–124. IEEE.
   Google Scholar
• Georgieva, A., S. J. Payne, M. Moulden, and C. W. G. Redman. 2013. Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22:85–93. doi:10.1007/s00521-011-0743-y.
   Web of Science ®Google Scholar
• Georgoulas, G., C. Stylios, and P. P. Groumpos 2005. Classification of FHR during the intrapartum period using wavelet neural networks trained by PSO. In The 3rd European medical and biological engineering conference (EMBEC’05), 1–5. IFMBE Proceedings 11 (1)CD-ROM edition (ISSN: 1727-1983).
   Google Scholar
• Georgoulas, G., C. D. Stylios, and P. 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 (5):875–884. doi:10.1109/TBME.2006.872814.
   PubMed Web of Science ®Google Scholar
• Goldberger, A. L., L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, et al. 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101 (23):e215–e220. doi:10.1161/01.CIR.101.23.e215.
   PubMed Web of Science ®Google Scholar
• Jezewski, M., R. Czabanski, and J. Leski. 2015. An attempt to optimize the cardiotocographic signal feature set for fetal state assessment. Journal of Medical Imaging and Health Informatics 5 (6):1364–1373. doi:10.1166/jmihi.2015.1540.
   Web of Science ®Google Scholar
• Jezewski, M., R. Czabanski, J. Leski, and K. Horoba. 2015. A new approach for the clustering using pairs of prototypes. Journal of Medical Informatics and Technologies 24:113–121.
   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 (4):29–47.
   Web of Science ®Google Scholar
• Jezewski, M., J. M. Leski, and R. Czabanski. 2016. Classification based on incremental fuzzy (1+p)-means clustering. In Man-machine interactions 4, Advances in intelligent systems and computing, ed. A. Gruca, A. Brachman, S. Kozielski, and T. Czachorski, vol. 391, 563–572. Switzerland Cham: Springer International Publishing.
   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):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, Advances in soft computing, ed. M. Kurzynski, E. Puchala, M. Wozniak, and A. Zolnierek, vol. 45, 532–541. 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, pp. 5170–5173. IEEE.
   Google Scholar
• Krupa, N., M. A. MA, E. Zahedi, S. Ahmed, and F. M. Hassan. 2011. Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine. BioMedical Engineering OnLine 10 (1):1–15. doi:10.1186/1475-925X-10-6.
   PubMed Web of Science ®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 Proceedings of the 26th annual international conference of the IEEE engineering in medicine and biology society, 337–340. IEEE.
   Google Scholar
• Leski, J. M. 2015. Fuzzy (c+p)-means clustering and its application to a fuzzy rule-based classifier: Toward good generalization and good interpretability. IEEE Transactions on Fuzzy Systems 23 (4):802–812. doi:10.1109/TFUZZ.2014.2327995.
   Web of Science ®Google Scholar
• Lichman, M. 2013. UCI Machine learning repository. Irvine, CA: University of California, School of Information and Computer Science. http://archive.ics.uci.edu/ml
   Google Scholar
• Maeda, K., Y. Noguchi, M. Utsu, and T. Nagassawa. 2015. Algorithms for computerized fetal heart rate diagnosis with direct reporting. Algorithms 8:395–406. doi:10.3390/a8030395.
   Web of Science ®Google Scholar
• Magenes, G., R. Bellazzi, A. Fanelli, and M. G. Signorini. 2014. Multivariate analysis based on linear and non-linear FHR parameters for the identification of IUGR fetuses. In 36th Annual international conference of the IEEE engineering in medicine and biology society – EMBC 2014, 1868–1871. IEEE.
   Google Scholar
• Magenes, G., M. G. Signorini, R. Sassi, and D. Arduini. 2001. Multiparametric analysis of fetal heart rate: Comparison of neural and statistical classifiers. In 9th Mediterranean conference on medical and biological engineering and computing (MEDICON 2001), eds. R. Magjarevic et al., 360–363, IFMBE Proceedings 1, Zagreb, Croatia: University of Zagreb, Faculty of Electrical Engineering and Computing.
   Google Scholar
• Mangasarian, O. L., and D. R. Musicant. 2001. Lagrangian support vector machines. Journal of Machine Learning Research 1:161–177.
   Web of Science ®Google Scholar
• Menai, M. E. B., F. J. Mohder, and F. Al-Mutairi. 2013. Influence of feature selection on naïve Bayes classifier for recognizing patterns in cardiotocograms. Journal of Medical and Bioengineering 2 (1):66–70.
   Google Scholar
• Ocak, H., and H. M. Ertunc. 2013. Prediction of fetal state from the cardiotocogram recordings using adaptive neuro-fuzzy inference systems. Neural Computing and Applications 23:1583–1589. doi:10.1007/s00521-012-1110-3.
   Web of Science ®Google Scholar
• Pedrinazzi, L., G. Magenes, and M. G. Signorini. 2004. Support vector machine for cardiotocographic clustering. In Proceedings of the MEDICON conference ( IFMBE 2004), Milano, Italy: GHEDIMEDIA, 1–4.
   Google Scholar
• Rotariu, C., A. Pasarica, G. Andruseac, H. Costin, and D. Nemescu. 2014. Automatic analysis of the fetal heart rate variability and uterine contractions. In 2014 International conference and exposition on electrical and power engineering (EPE 2014), eds. M. Gavrilas et al., New York, USA: IEEE, 553–556.
   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
• Signorini, M. G., A. de Angelis, G. Magenes, R. Sassi, D. Arduini, and S. Cerutti. 2000. Classification of fetal pathologies through fuzzy inference systems based on a multiparametric analysis of fetal heart rate. Computers in Cardiology 27:435–438.
   Google Scholar
• Spilka, J., V. Chudacek, P. Janku, L. Hruban, M. Bursa, M. Huptych, L. Zach, and L. Lhotska. 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., G. Georgoulas, P. Karvelis, V. P. Oikonomou, V. Chudacek, C. Stylios, L. Lhotska, and P. Janku. 2013. Automatic evaluation of FHR recordings from CTU-UHB CTG database. In 4th International conference on information technology in bio- and medical informatics – (ITBAM 2013), ed. M. Bursa, S. Khuri, and M. E. Renda, 47–61, Lecture Notes in Computer Science 8060. Berlin Heidelberg: Springer-Verlag.
   Google Scholar
• Sundar, C., M. Chitradevi, and G. Geetharamani. 2012. Classification of cardiotocogram data using neural network based machine learning technique. International Journal of Computer Applications 47 (14):19–25. doi:10.5120/7256-0279.
   Google Scholar
• Tomas, P., J. Krohova, P. Dohnalek, and P. Gajdos. 2013. Classification of cardiotocography records by random forest. In 2013 36th International conference on telecommunications and signal processing (TSP), eds. N. Herencsar, K.Molnar, Brno, Czech Republic: Faculty of Electrical Engineering and Communication, Brno University of Technology. 620–623. Papers from this conference are available in IEEE Xplore Digital Library.
   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 (6):1295–1301. doi:10.1166/jmihi.2015.1532.
   Web of Science ®Google Scholar
• Wrobel, J., A. Matonia, K. Horoba, J. Jezewski, R. Czabanski, A. Pawlak, and P. Porwik. 2015. Pregnancy telemonitoring with smart control of algorithms for signal analysis. Journal of Medical Imaging and Health Informatics 5 (6):1302–1310. doi:10.1166/jmihi.2015.1533.
   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.
   Google Scholar