Early Stage Prediction of Caesarean Vs Normal Vaginal Delivery Using Artificial Intelligence

Authors

  • Naveena B  Students, Department of biomedical Engineering, Dhanalakshmi Srinivasan Institute of Technology, Trichy, Tamilnadu, India
  • Pavithra U  Students, Department of biomedical Engineering, Dhanalakshmi Srinivasan Institute of Technology, Trichy, Tamilnadu, India
  • Shalini R  Students, Department of biomedical Engineering, Dhanalakshmi Srinivasan Institute of Technology, Trichy, Tamilnadu, India
  • Sivaranjini S  Students, Department of biomedical Engineering, Dhanalakshmi Srinivasan Institute of Technology, Trichy, Tamilnadu, India
  • Nishanthini R  Assistant professor in Department of Biomedical Engineering, Dhanalakshmi Srinivasan Institute of Technology, Trichy, Tamilnadu, India

Keywords:

Early Prediction of Delivery, CTG (Cardio Toco Graphy)

Abstract

Machine learning techniques provide learning mechanism that can be used to induce knowledge from data. A few studies exist on the use of machine learning techniques for medical diagnosis, prediction and treatment. In this study we evaluate different machine learning techniques for birth classification (cesarean or normal). Data on cesarean section is collected and different medical factors are identified that result in cesarean births. A birth classification model is built using decision tree and artificial neural networks. In this paper, we provide method of classifying caesarean section and normal vaginal deliveries using fetal heart rate signals and uterine contractions using Artificial intelligence. Here we predict the status of fetal using machine learning technique

References

  1. Sinai Talaulikar, Vikram, and S. Arulkumaran. "Medico-Legal Issues with CTG Interpretation." Current Women S Health Reviews 2013.9(2013):145-157.
  2. G. Georgoulas, D. Stylios and P. Groumpos, "Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines," in IEEE Transactions on Biomedical Engineering, vol. 53, no. 5, pp. 875-884, May 2006
  3. A. Fanelli, G. Magenes, M. Campanile and M. G. Signorini, "Quantitative Assessment of Fetal Well-Being Through CTG Recordings: A New Parameter Based on Phase-Rectified Signal Average," in IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 5, pp. 959-966, Sept. 2013.
  4. V. Chudá?ek, J. Andén, S. Mallat, P. Abry and M. Doret, "Scattering Transform for Intrapartum Fetal Heart Rate Variability Fractal Analysis: A Case-Control Study," in IEEE Transactions on Biomedical Engineering, vol. 61, no. 4, pp. 1100-1108, April 2014.
  5. P. A. Warrick*, E. F. Hamilton, D. Precup and R. E. Kearney, "Identification of the Dynamic Relationship Between Intrapartum Uterine Pressure and Fetal Heart Rate for Normal and Hypoxic Fetuses," in IEEE Transactions on Biomedical Engineering, vol. 56, no. 6, pp. 1587-1597, June 2009.
  6. Goddard, Ros. "Electronic fetal monitoring." Jama 155.1(2001):1525-1526.
  7. H. Helgason, P. Abry, P. Goncalvès, C. Gharib, P. Gaucherand and M. Doret, "Adaptive Multiscale Complexity Analysis of Fetal Heart Rate," in IEEE Transactions on Biomedical Engineering, vol. 58, no. 8, pp. 2186-2193, Aug. 2011.
  8. S. Dash, J. G. Quirk and P. M. Djuri?, "Fetal Heart Rate Classification Using Generative Models," in IEEE Transactions on Biomedical Engineering, vol. 61, no. 11, pp. 2796-2805, Nov. 2014.
  9. G. Georgoulas, D. Stylios and P. Groumpos, "Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines," in IEEE Transactions on Biomedical Engineering, vol. 53, no. 5, pp. 875-884, May 2006.
  10. Tsui, Sheng Yang, C. S. Liu, and C. W. Lin. "Modified maternal ECG cancellation for portable fetal heart rate monitor." Biomedical Signal Processing & Control 32(2016).
  11. M. Lichman?UCI Machine Learning Repository, California: University of California, School of Information and Computer Science,2013.
  12. W. Yin, X. Yang, L. Zhang and E. Oki, "ECG Monitoring System Integrated With IR-UWB Radar Based on CNN," in IEEE Access, vol. 4, pp. 6344-6351, 2016.
  13. J. Jezewski, T. Kupka and K. Horoba, "Extraction of Fetal Heart-Rate Signal as the Time Event Series From Evenly Sampled Data Acquired Using Doppler Ultrasound Technique," in IEEE Transactions on Biomedical Engineering, vol. 55, no. 2, pp. 805-810, Feb. 2008.
  14. Ocak, Hasan, and H. M. Ertunc. "Prediction of fetal state from the cardiotocogram recordings using adaptive neuro-fuzzy inference systems." Neural Computing & Applications 23.6(2013):1583-1589. 14 Huang, Mei Ling, and Y. Y. Hsu. "Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network."Journal of Biomedical Science & Engineering 05.9(2012).
  15. Ayresdecampos D, Spong C Y, Chandraharan E. FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography.J. International Journal of Gynecology & Obstetrics, 2015, 131(1):13-24.

International Journal of Scientific Research in Science and Technology

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Published

2020-03-05

Issue

Volume 5, Issue 5, March-April-2020

Section

Research Articles

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How to Cite

[1]
Naveena B, Pavithra U, Shalini R, Sivaranjini S, Nishanthini R, " Early Stage Prediction of Caesarean Vs Normal Vaginal Delivery Using Artificial Intelligence, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 5, Issue 5, pp.145-152, March-April-2020.