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Prediction of fetal state from the cardiotocogram recordings using adaptive neuro-fuzzy inference systems

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Abstract

In this study, a new scheme was presented for the prediction of fetal state from fetal heart rate (FHR) and the uterine contraction (UC) signals obtained from cardiotocogram (CTG) recordings. CTG recordings are widely used in pregnancy and provide very valuable information regarding fetal well-being. The information effectively extracted from these recordings can be used to predict pathological state of the fetus and makes an early intervention possible before there is an irreversible damage to the fetus. The proposed scheme is based on adaptive neuro-fuzzy inference systems (ANFIS). Using features extracted from the FHR and UC signals, an ANFIS was trained to predict the normal and the pathological state. The method was tested with clinical data that consist of 1,831 CTG recordings. Out of these 1,831 recordings, 1,655 of them were classified as normal and the remaining 176 were classified as pathological by a consensus of three expert obstetricians. It was demonstrated that the ANFIS-based method was able to classify the normal and the pathologic states with 97.2 and 96.6 % accuracy, respectively.

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Authors and Affiliations

  1. Department of Mechatronics Engineering, Kocaeli University, Umuttepe Campus, 41380, Kocaeli, Turkey

    Hasan Ocak & Huseyin Metin Ertunc

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  1. Hasan Ocak
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  2. Huseyin Metin Ertunc
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Correspondence to Hasan Ocak.

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Ocak, H., Ertunc, H.M. Prediction of fetal state from the cardiotocogram recordings using adaptive neuro-fuzzy inference systems. Neural Comput & Applic 23, 1583–1589 (2013). https://doi.org/10.1007/s00521-012-1110-3

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  • DOI: https://doi.org/10.1007/s00521-012-1110-3

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