single-jc.php

JACIII Vol.20 No.1 pp. 33-40
doi: 10.20965/jaciii.2016.p0033
(2016)

Paper:

Fuzzified Evaluation of Cardiotocography Data for Real Medical Data

Yutaka Hatakeyama, Hiromi Kataoka, Noriaki Nakajima, Teruaki Watabe, and Yoshiyasu Okuhara

Center of Medical Information Science, Kochi University Medical School
Oko-cho Kohasu, Nankoku-shi, Kochi 783-8505, Japan

Received:
February 2, 2015
Accepted:
September 29, 2015
Online released:
January 19, 2016
Published:
January 20, 2016
Keywords:
cardiotocography (CTG), fuzzy inference, particle filter
Abstract

Level evaluation system in cardiotocography (CTG) taken in the real medical practice is constructed for the use of e-learning materials. The system consists of 3 parts, preprocessing for FHR by particle filter, extraction process of deceleration information, and level evaluation. The extraction and evaluation processes are executed by fuzzy inference. To check the effectiveness of the proposed system, level evaluation experiments are executed for the real CTG data extracted from records at the Kochi Medical School hospital. The experimental results show that the extracted CTG features are suitable for the material, and that the calculated level of the patients with abnormal pH value is evaluated as abnormal situations. The proposed system can provide the possible data for the materials for evaluation learning of real CTG.

References
  1. [1] Z. Alfirevic, D. Devane, and G. M. Gyte, “Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour,” Cochrane Database Syst Rev., Vol.5, CD006066, 2013.
  2. [2] K. Afors and E. Chandraharan, “Use of continuous electronic fetal monitoring in a preterm fetus: clinical dilemmas and recommendations for practice,” J. Pregnancy, ID 848794, 2011.
  3. [3] V. Chudávcek, J. Spilka, P. Jankru, M. Kouck’y, L. Lhotskáa, and M. Huptych, “Automatic evaluation of intrapartum fetal heart rate recordings: a comprehensive analysis of useful features,” Physiol Meas., Vol.32, No.8, pp. 1347-1360, 2011.
  4. [4] P. A. Warrick, E. F. Hamilton, D. Precup, and R. E. Kearney, “Classification of normal and hypoxic fetuses from systems modeling of intrapartum cardiotocography,” IEEE Trans Biomed Eng., Vol.57, No.4, pp. 771-779, 2010.
  5. [5] E. Yi lmaz and C. Kilikçier, “Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree,” Comput Math Methods Med., Vol.2013, ID 487179, 2013.
  6. [6] D. Ayres-de-Campos, A. Ugwumadu, P. Banfield, P. Lynch, P. Amin, D. Horwell, A. Costa, C. Santos, J. Bernardes, and K. Rosen, “A randomised clinical trial of intrapartum fetal monitoring with computer analysis and alerts versus previously available monitoring,” BMC Pregnancy Childbirth, Vol.10, No.71, 2010.
  7. [7] “Guidelines for obstetrical practice in Japan 2011 edition,” http://www.jsog.or.jp/activity/pdf/gl_obst_2011_en.pdf [Accessed Jun. 1, 2014]
  8. [8] G. Kitagawa, “Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models,” J. Computational and Graphical Statistics, Vol.5, No.1, pp. 1-25, 1996.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on May. 26, 2017