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Artificial neural networks applied to fetal monitoring in labour

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Abstract

Birth asphyxia can result in death or permanent brain damage. To prevent it, the fetal heart rate (FHR) is recorded in labour on a paper strip. In clinical practice, the complicated FHR patterns are assessed by eye, which is error-prone, inconsistent and unreliable. Objective alternatives are needed and thus we investigated the applicability of feed-forward artificial neural networks (ANNs) for FHR analysis. Six FHR features were extracted and combined with six clinical parameters to form a feature space of 12 dimensions. The feature space was reduced to six dimensions by principal component analysis. Subsequently, a network committee of ten ANNs was trained with the data of 124 patients (a balanced set of 62 adverse, coded 1, and 62 normal outcomes, coded 0). The ANN committee was tested on another balanced set of 252 patients obtaining misclassification rate of 36%. Finally, the committee was tested on a large dataset of 7,568 patients (non-balanced). As the committee output continuously increased from 0 to 1, there was a consistent growth of the adverse outcome rate (from 0.26 to 5.3%) and the low umbilical pH rate (from 2.6 to 16.7%.) Based on this correlation between the committee output and the risk of compromise, we concluded that ANNs can be successfully applied to FHR monitoring in labour. However, extensive further work is necessary, for which we outline our plans. To our knowledge, this is the first time that an automated method for FHR diagnostic analysis has been tested on a database of this size.

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Authors and Affiliations

  1. Nuffield Department of Obstetrics & Gynaecology, University of Oxford, Level 3, Women’s Centre, John Radcliffe Hospital, Oxford, OX3 9DU, UK

    Antoniya Georgieva, Mary Moulden & Christopher W. G. Redman

  2. Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, UK

    Antoniya Georgieva & Stephen J. Payne

Authors
  1. Antoniya Georgieva
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  2. Stephen J. Payne
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  3. Mary Moulden
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  4. Christopher W. G. Redman
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Correspondence to Antoniya Georgieva.

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Georgieva, A., Payne, S.J., Moulden, M. et al. Artificial neural networks applied to fetal monitoring in labour. Neural Comput & Applic 22, 85–93 (2013). https://doi.org/10.1007/s00521-011-0743-y

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