Abstract
Cardiotocography records the fetal heart rate and Uterine contractions which is used to monitor the fetal distress during delivery. This signal supports the physicians to assess the fetal and maternal risk. During the last decade, various technique have proposed computer-aided assessment of fetal distress. The drawback of these techniques are complex in extraction of features and costlier in classification algorithm utilized. This paper proposes a feature selection technique Multivariate Adaptive Regression Spline and Recursive Feature Elimination to evaluate 48 numbers of linear and non-linear features and to classify using Decision tree and k-Nearest Neighbor algorithms. Experimental results shows the performance of the proposed with state-of-the-art techniques.
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References
Cömert, Z., Fatih, A.: Evaluation of fetal distress diagnosis during delivery stages based on linear and nonlinear features of fetal heart rate for neural network community. Int. J. Comput. Appl. 156(4), 26–31 (2016)
Sameni, R.: A review of fetal ECG signal processing ıssues and promising directions. Open Pacing Electrophysiol. Ther. J. 3, 4–20 (2010)
Goldberger, A.L., Amaral, L.A.N., Glass, L., et al.: PhysioBank, PhysioToolkit, and PhysioNet. Circulation 101(23), E215–E220 (2000)
Iraji, M.S.: Prediction of fetal state from the cardiotocogram recordings using neural network models. Artif. Intell. Med. 96, 33–44 (2019)
Ramla, M., Sangeetha, S., Nickolas, S.: Fetal health state monitoring using decision tree classifier from cardiotocography measurements. In: 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1799–1803. IEEE (2018)
Cömert, Z., Kocamaz, A.F.: Comparison of machine learning techniques for fetal heart rate classification. Acta Phys. Pol. A 132(3), 451–454 (2017)
Deb, S., Islam, S.M.R., Johura, F.T., Huang, X.: Extraction of linear and non-linear features of electrocardiogram signal and classification. In: 2017 2nd International Conference on Electrical and Electronic Engineering (ICEEE), pp. 1–4. IEEE (2017)
Karabulut, E.M., Ibrikci, T.: Analysis of cardiotocogram data for fetal distress determination by decision tree based adaptive boosting approach. J. Comput. Commun. 2(09), 32 (2014)
Padmavathi, S., Ramanujam, E.: Naïve Bayes classifier for ECG abnormalities using multivariate maximal time series Motif. Procedia Comput. Sci. 47, 222–228 (2015)
Balayla, J., Shrem, G.: Use of artificial intelligence (AI) in the interpretation of intrapartum fetal heart rate (FHR) tracings: a systematic review and meta-analysis. Arch. Gynecol. Obstet. 300, 1–8 (2019)
Friedman, J.H.: Multivariate adaptive regression splines. Ann. Stat. 19(1), 1–67 (1991)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1–3), 389–422 (2002)
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Ramanujam, E., Chandrakumar, T., Nandhana, K., Laaxmi, N.T. (2020). Prediction of Fetal Distress Using Linear and Non-linear Features of CTG Signals. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_5
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DOI: https://doi.org/10.1007/978-3-030-37218-7_5
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