Abstract
Perinatal mortality and morbidity occurs mainly due to intrauterine fetal hypoxia. This can eventually lead to severe neurological damage like cerebral palsy and in extreme cases to fetal demise. It is thus necessary to monitor the fetus during intrapartum and antepartum period. Cardiotocograph (CTG) as a method of assessing the status of the fetus had been in use since the 1960s. Nowadays it is the most widely used non-invasive technique for the continuous monitoring of the fetal heart rate (FHR) and the uterine contraction pressure (UCP). Though its introduction limited the birth related problems, the accuracy of interpretation was hindered by quite a few factors. Different guidelines that are provided for the interpretation are based on crisp logic which fails to capture the inherent uncertainty present in the medical diagnosis. Misinterpretations had led to inaccurate diagnosis which resulted in many medico-legal litigations. The vagueness present in the physician’s evaluation is best modeled using soft-computing based techniques. In this paper authors used the CTG dataset from UCI Irvine Machine Learning Data Repository which contains 2126 data. Re-current neural network (RNN) was used for classification of CTG using five-fold cross-validation. The result was compared with a previous work using Fuzzy Unordered Rule Induction Algorithm (FURIA) and Fuzzy Membership Function (FMF) techniques. The obtained accuracy was 98%.
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Yilmaz, E., Kilikçier, C.: Determination of fetal state from cardiotocogram using LSSVM with particle swarm optimization and binary decision tree. J. Comput. Math. Methods Med. 2013, 1–8 (2013)
Das, S., Roy, K., Saha, C.K.: A linear time series analysis of fetal heart rate to detect the variability: measures using cardiotocography. In: Bhattacharyya, S., Das, N., Bhattacharyya, D., Mukherjee, A. (eds.) Handbook of Research on Recent Developments in Intelligent Communication Application, vol. 1, pp. 471–495. IGI Global (2017)
Das, S., Roy, K., Saha, C.K.: Application of FURIA in the classification of cardiotocograph. In: IEEE International Conference on Research and Development Prospects on Engineering and Technology, pp. 120–124. IEEE Press, Chennai (2013)
De-Campos, A., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring. Int. J. Gynecol. Obstet. 131(1), 13–24 (2015)
Macones, G.A., et al.: The 2008 National Institute of Child Health and Human Development workshop report on electronic fetal monitoring: update on definitions, interpretation, and research guidelines. J. Am. Coll. Obstet. Gynecol. 112, 661–666 (2008)
Santo, S., Ayres-de-Campos, D., Santos, C., Schnettler, W., Ugwumadu, A., Garca, L.M.D.: Agreement and accuracy using the FIGO, ACOG and NICE cardiotocography interpretation guidelines. Acta Obstet. Gynecol. Scand. 96(2), 166–175 (2017)
Dawes, G.S., Visser, G.H., Goodman, J.D., Redman, C.W.: Numerical analysis of the human fetal heart rate: the quality of ultrasound records. Am. J. Obstet. Gynecol. 141(1), 43–52 (1981)
Alonso-Betanzos, A., Guijarro-Berdiñas, B., Moret-Bonillo, V., López-Gonzalez, S.: The NST-EXPERT project: the need to evolve. J. Artif. Intell. Med. 7(4), 297–313 (1995)
Guijarro-Berdinas, B., Alonso-Betanzos, A., Fontella-Romero, O.: Intelligent analysis and pattern recognition in cardiotocographic signals using a tightly coupled hybrid system. Artif. Intell. 136(1), 1–27 (2002)
Maeda, K., Noguchi, Y., Utsu, M., Nagassawa, K.: Algorithms for computerized fetal heart rate diagnosis with direct reporting. Algorithms 8(1), 395–406 (2015)
de Campos, A., Sousa, P., Costa, A., Bernardes, J.: Omniview-SisPorto®3.5 - a central fetal monitoring station with online alerts based on computerized cardiotocogram+ST event analysis. J. Perinat. Med. 36(3), 260–264 (2008)
A Critical Review of Recurrent Neural Networks for Sequence Learning. https://arxiv.org/abs/1506.00019. Accessed 10 Aug 2018
Li, J., Mohamed, A., Zweig, G., Gong, Y.: LSTM time and frequency recurrence for automatic speech recognition. In: 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 187–191. IEEE, Scottsdale (2015)
UCI Irvine Data Repository. http://archive.ics.uci.edu/ml/datasets/Cardiotocography
Das, S., Roy, K., Saha, C.K.: Fuzzy membership estimation using ANN: a case study in CTG analysis. In: Satapathy, S.C., Biswal, B.N., Udgata, S.K., Mandal, J.K. (eds.) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. AISC, vol. 327, pp. 221–228. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-11933-5_25
Bouguelia, M.R., Nowaczyk, S., Santosh, K.C., Verikas, A.: Agreeing to disagree: active learning with noisy labels without crowdsourcing. Int. J. Mach. Learn. Cybern. 9(8), 1307–1319 (2018)
Vajda, S., Santosh, K.C.: A fast k-nearest neighbor classifier using unsupervised clustering. In: Santosh, K.C., Hangarge, M., Bevilacqua, V., Negi, A. (eds.) RTIP2R 2016. CCIS, vol. 709, pp. 185–193. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-4859-3_17
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Das, S., Mukherjee, H., Obaidullah, S.M., Santosh, K.C., Roy, K., Saha, C.K. (2019). Recurrent Neural Network Based Classification of Fetal Heart Rate Using Cardiotocograph. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_20
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DOI: https://doi.org/10.1007/978-981-13-9184-2_20
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