Abstract
The purpose of this work is to interpret cardiotocograph recordings by estimating kubli score in order to find out the presence of antepartum morbidity in cardiotocograph (CTG) recordings. The Kubli score is a tool used to evaluate a non-stress test (NST). It works very much like an Apgar Score in that there are 5 criteria to be assessed, baseline rate, amplitude of fluctuations, frequency of fluctuations, deceleration and acceleration pattern, and each will be assigned a score of 0, 1, or 2, for a maximum total of 10. The advantage of the Kubli score is that it is systematic and specific. This communication tool helps clinicians to have a visual image of the immediate fetal status. Using the Kubli score the NST can be interpreted as normal, equivocal, and ominous with a score ranging from 8–10, 5–7 and 1–4 respectively. In our study, all the patterns (baseline, amplitude, frequency, deceleration and acceleration) necessary for estimating the Kubli score are recognized using algorithms implemented using JAVA 1.5. The generated Kubli score is used to assess the CTG recordings. A set of cardiotocograph recordings from the patients at a gestational age of >32 weeks are interpreted using the algorithm. For the same set of data experts interpretations are recorded and stored. The computerized interpretation results are compared with the results from manual CTG interpretation using Bland Altman, a statistical method for method comparison. The entire process is tested on 15 CTG recordings. The results obtained this way shows that the CTG interpretation using Kubli Score is reliable and specific, especially, while rating amplitude of fluctuation and frequency of fluctuation. And also they offer major advantages compared to subjective assessment. A scoring system for fetal surveillance, like Kubli score, is a systematic way of interpreting antepartum cardiotocograph recordings.
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References
M. V. Kamath and E. L. Fallen (1993), Power spectral analysis of heart rate variability: a noninvasive signature of cardiac autonomic function, Crit. Rev. Biomed. Eng., vol. 21, no. 3, pp. 245–311.
Signorini MG, Magenes G, Cerutti S, and Arduini D (2003), Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings, IEEE Trans. Biomed. Eng., vol. 50, no. 3, pp. 365–374.
J. P. Lecanuet and B. Schaal (1996), Fetal sens ory competencies, Eur. J.Obstet. Gynecol. Reprod. Biol., vol. 68, no. 1–2, pp. 1–23.
George Georgoulas, Chrysostomos D. Stylios and Peter P. Groumpos (2006), Predicting the Risk of Metabolic Acidosis for Newborns Based on Fetal Heart Rate Signal Classification Using Support Vector Machines., IEEE Trans. Biomed. Eng., vol. 53, no.5.
Ronaldo C. Gismondi a, Renan Moritz Varnier R. Almeida, Antonio Fernando C. Infantosi (2002), Artificial neural networks for infant mortality modeling, Comp. Metthods and Programs in Biomed., vol. 69, pp. 237–247.
Trimbos J.B, Keirse M. J. N. C (2005), Observer variability in assessment of antepartum cardiotocograms, BJOG, vol. 85, pp. 900–906.
Ayres-de-Campos D, Bernardes J, Garrido A, de Sa J P M, and Pereira-Leite L (2000), SisPorto 2.0: a program for automated analysis of cardiotocograms, J. Matern.. Fetal Med., vol. 9, pp. 311–318.
Bernardes J, Moura C, de Sa J P M, and Pereira-Leite L (1991), The Porto system for automated cardiotocographic signal analysis, J. Perinat.Med., vol. 19, pp. 61–65.
Niranjana Krupa B, Mohd. Ali. M.A and Zahedi E (2008), Computerized fetal heart rate baseline estimation based on number and continuity of occurrences, IFMBE proceedings.
Williams KP, Farquharson DF, Bebbington M, et al (2003), Screening for fetal well-being in a high-risk pregnant population comparing the non stress test with umbilical artery Doppler velocimetry: a randomized controlled clinical trial, AJOG, vol 188, pp. 1366–1371.
British columbia reproductive care program (2005), Perinatal forms guidelines.
Implementing midwifery services in British Columbia (2006), A manual for hospitals and health regions.
JM. Bland, DG. Altman (1986), Statistical methods for assessing agreement between two methods of clinical measurement, Lance, 1(8476):307–10.
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Krupa, N., Hasan, F.M., Ali, M.A.M., Zahedi, E. (2009). Computerized Interpretation of Cardiotocographs Using Kubli Score. In: Vander Sloten, J., Verdonck, P., Nyssen, M., Haueisen, J. (eds) 4th European Conference of the International Federation for Medical and Biological Engineering. IFMBE Proceedings, vol 22. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89208-3_229
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DOI: https://doi.org/10.1007/978-3-540-89208-3_229
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