A Survey of Electronic Fetal Monitoring: A Computational Perspective

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 486)

Abstract

Electronic Fetal Monitoring (EFM) records fetal heart rate in order to assess fetal well being in labor. Since its suggestion in clinical practise by de Kergeradee in the nineteenth century, it has been adopted as standard medical practise in many delivery scenarios across the globe. The extent of its use has augmented from its original purpose and is now used to not only reduce prenatal mortality, but also neonatal encephalopathy and cerebral palsy. One of the difficulties with EFM is interpreting the data, which is especially difficult if it is acquired in a continuous fashion. A grading system has been developed (utilised for developing a guideline for Clinical Practise Algorithm) which consists of grading fetal heart rate (FHR) into fairly rough categories (three). These categories are defined by values associated with a set of four features. The values for this set of features are potentially influenced by the particular collection equipment and/or operating conditions. These factors, in conjunction with a stressful condition such as a complicated delivery scenario may render rapid and unequivocal reporting of the neonatal status sometimes difficult. This chapter examines the development of automated approaches to classifying FHR into one of three clinically defined categories. The ultimate goal is to produce a reliable automated system that can be deployed in real-time within a clinical setting and can therefore be considered as an adjunctive tool that will provide continuous on-line assistance to medical staff.

Keywords

Biomedical datasets Cardiotocogram Decision support systems Electronic fetal monitoring Reducts Rough sets 

Notes

Acknowledgments

The authors would like to acknowledge the source of the dataset: the UCI KDD data repository, into which this dataset was deposited by faculty members at the University of Porto, Portugal.

The research of Barna Iantovics was supported by the project “Transnational Network for Integrated Management of Postdoctoral Research in Communicating Sciences.” Institutional building (postdoctoral school) and fellowships program (CommScie)-POSDRU/89/1.5/S/63663, Financed under the Sectorial Operational Programme Human Resources Development 2007–2013.

References

  1. 1.
    Alfirevic, Z, Devane, D., Gyte, GML.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour (Review) 1, Copyright © 2007 The Cochrane Collaboration, Wiley (2007)Google Scholar
  2. 2.
    Ito, T., Maeda, K., Takahashi, H., Nagata, N., Nakajima, K., Terakawa, N.: Differentiation between physiologic and pathologic sinusoidal FHR pattern by fetal actocardiogram. J. Perinat. Med. 22(1), 39–43 (1994)CrossRefGoogle Scholar
  3. 3.
    American College of Obstetricians and Gynecologists Task force on Neonatal Encephalopathy and Cerebral Palsy. Neonatal Encephalopathy and Cerebral Palsy: Defining the Pathogenesis and Pathophysiology, Jan (2003)Google Scholar
  4. 4.
  5. 5.
    Tsuzaki, T., Sekijima, A., Morishita, K., Takeuchi, Y., Mizuta, M., Minagawa, Y., Nakajima, K., Maeda, K.: Survey on the perinatal variables and the incidence of cerebral palsy for 12 years before and after the application of the fetal monitoring system. Nippon Sanka Fujinka Gakkai Zasshi, 42, 99–105 (1990)Google Scholar
  6. 6.
    Ayres-de-Campos, D., Bernardes, J., Garrido, A., De Sa, J.P.M., Pereira-Leite, L.: SisPorto 2.0: a program for automated analysis of cardiotocograms. J. Matern. Fetal Med. 9, 311–318 (2000)Google Scholar
  7. 7.
    Georgoulas, G., Gavrilis, D., Tsoulos, I.G., Stylios, C., Bernardes, J., Groumpos, P.P.: Novel approach for fetal heart rate classification introducing grammatical evolution. Biomed. Signal Process. Control 2, 69–79 (2007)CrossRefGoogle Scholar
  8. 8.
    Warrick, P., Hamilton, E., Macieszczak, M.: Neural network based detection of fetal heart rate patterns. IEEE Trans. Biomed. Eng. 57(4), 771–779 (2010)CrossRefGoogle Scholar
  9. 9.
    Fontenla-Romero, O., Guijarro-Berdinas, B., Alonso-Betanzos, A.: Symbolic neural and neuro-fuzzy approaches to pattern recognition in cardiotocograms. Adv. Comput. Intell. Learn. Int. Ser. Intell. Technol. 18, 489–500 (2002)CrossRefGoogle Scholar
  10. 10.
    Ulbricht, C., Dorffner, G., Lee, A.: Neural networks for recognizing patterns in cardiotocograms. Artif. Intell. Med. 12, 271–284 (1998)CrossRefGoogle Scholar
  11. 11.
    Georgoulas, G., Stylios, C., Groumpos, P.P.: Integrated approach for classification of cardiotocograms based on independent component analysis and neural networks. In: Proceedings of 11th IEEE Mediterranean conference on Control and Automation, 2003, Rodos, Greece, 18–20 June (2003)Google Scholar
  12. 12.
    Jezewski, M., Wrobel, J., Labaj, P., Leski, J., Henzel, N., Horoba, K., Jezewski, J.: Some practical remarks on neural networks approach to fetal cardiotocograms classification. In: Proceedings of IEEE Engineering in Medicine and Biology Society, pp. 5170–5173 (2007)Google Scholar
  13. 13.
    Revett, K.: A rough sets based approach to analysing CTG datasets. Int. J. Bioinform. Healthc. (in press)Google Scholar
  14. 14.
    Komorowski J., Pawlak Z., Polkowski L., Skowron A.: Theory, knowledge engineering and problem solving. Dordrecht: Kluwer, 1991. Rough sets: A Tutorial, In: Pal, S.K., Skowron, A. (eds.) Rough Fuzzy Hybridization: A New Trend in Decision Making, Springer, Singapore, pp. 3–98, (1999)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Informatics and Computer ScienceThe British University in EgyptCairoEgypt
  2. 2.Faculty of Sciences and LettersPetru Maior UniversityTargu MuresRomania

Personalised recommendations