Abstract
The Cardiotocograph (CTG) is being used by the obstetricians since 1960s as a means for recording (graphy) the heart beat (cardio) and the uterine contraction pressure (toco) of the mother, to evaluate the well being of the fetus. One of the major features of fetal heart rate (FHR) is its baseline,the accurate classification which is of utmost importance as all the other parameters of CTG rely on it. Inherent vagueness in the assessment given by the physicians can probably be modeled using fuzzy logic. It is one of the most trusted tools to handle uncertainty intrinsically present in the linguistic expression of human. The main challenge in designing a fuzzy logic based system is to design its membership function. In this paper we have presented a ANN based technique for the design of Fuzzy Membership Function (FMF) of FHR and used it in Fuzzy Unordered Rule Induction Algorithm (FURIA) in order to classify the CTG. The results obtained show significant improvement in classification over non FMF based technique.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yilmaz, E., Kilikçier, C.: Determination of Fetal State from Cardiotocogram using LS-SVM with Particle Swarm Optimization and Binary Decision Tree. J. Comp. Math. Methods in Med. 2013, 1–8 (2013)
Dawes, G.S., Visser, G.H., Goodman, J.D., Redman, C.W.: Numerical analysis of the human fetal heart rate: the quality of ultrasound records. Am. J. Obstet. Gynecol. 141(1), 43–52 (1981)
Magenes, G., et al.: Improving the fetal cardiotocographic monitoring by advanced signal processing. In: 25th IEEE Annual International Conference of Engineering in Medicine and Biology Society, vol. 3, pp. 2295–2298. IEEE Press, Italy (2003)
Alonso-Betanzos, A., Guijarro-Berdiñas, B., Moret-Bonillo, V., López-Gonzalez, S.: The NST-EXPERT project: the need to evolve. J. Artif. Intell. Med. 7(4), 297–313 (1995)
Guijarro-Berdiñas, B., Alonso-Betanzos, A.: Empirical Evaluation of a Hybrid Intelligent Monitoring System using Different Measures of Effectiveness. J. Artif. Intell. Med. 24(1), 1–96 (2002)
de Campos, A., Sousa, P., Costa, A., Bernardes, J.: Omniview-SisPorto® 3.5 - A central Fetal Monitoring Station With Online Alerts Based on Computerized Cardiotocogram+ST Event Analysis. J. Perinatal Medicine 36(3), 260–264 (2008)
Helgason, H., Abrey, P., Gharib, C., et al.: Adaptive Multiscale Complexity of Fetal Heat Rate. IEEE Transactions on Biomedical Engineering 58(8), 2186–2193 (2011)
Das, S., Roy, K., Saha, C.K.: A Novel Approach for Extraction and Analysis of Variability of Baseline. In: IEEE International Conference on Recent Trends in Information Systems, pp. 336–339. IEEE Press, Kolkata (2011)
Skinner, J.F., Garibaldi, J.M., Ifeachor, E.C.: A Fuzzy System for Fetal Heart Rate Assessment. In: Reusch, B. (ed.) Fuzzy Days 1999. LNCS, vol. 1625, pp. 20–29. Springer, Heidelberg (1999)
Chinnasamy, S., Muthasamy, C., et al.: An Outlier Based Bi-Level Neural Network Classification Systemfor Improved Classification of Cardiotocogram Data. J. Life Science 10(1), 244–251 (2013)
Macones, G.A., et al.: The 2008 National Institute of Child Health and Human Development Workshop Report on Electronic Fetal Monitoring: Update on Definitions, Interpretation, and Research Guidelines. J. Am. College of Obstet. & Gynecol. 112, 661–666 (2008)
Altiparmak, F., Dengiz, B., Smith, A.E.: A General Neural Network Model for Estimating Telecommunications Network for Reliability. IEEE Transactions on Reliability 58(1), 2–9 (2009)
Chen, C.Y., Chen, J.C., Yu, C., et al.: A Comparative Study of a New Cardiotocography Analysis Program. In: IEEE Annual Conference of Med. Biol. Soc., pp. 2567–2570. IEEE Eng., Taiwan (2009)
Langellė, R., Devoux, T.: Training Multilayer Perceptrons Layer Using an Objective Functions for Internal Representations. J. Neural Networks 58(8), 83–98 (1996)
UCI Irvine Data Repository, http://archive.ics.uci.edu/ml/datasets/Cardiotocography
Das, S., Roy, K., Saha, C.K.: Application of FURIA in the Classification of Cardiotocograph. In: IEEE- International Conference on Research and Development Prospects on Engineering and Technology, pp. 120–124. IEEE Press, Chennai (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Das, S., Roy, K., Saha, C.K. (2015). Fuzzy Membership Estimation Using ANN: A Case Study in CTG Analysis. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-11933-5_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11932-8
Online ISBN: 978-3-319-11933-5
eBook Packages: EngineeringEngineering (R0)