Doctoral Training Centre, University of Oxford, Rex Richards Building, South Parks Road, Oxford OX1 3QU, UK. Nuffield Department of Obstetrics and Gynaecology, University of Oxford, Level 3, Women's Centre, John Radcliffe Hospital, Oxford OX3 9DU, UK. Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford. Headington, Oxford OX3 7DQ, UK.
Physiol Meas. 2014 Jul;35(7):1357-71. doi: 10.1088/0967-3334/35/7/1357. Epub 2014 May 22.
The fetal heart rate (FHR) is monitored on a paper strip (cardiotocogram) during labour to assess fetal health. If necessary, clinicians can intervene and assist with a prompt delivery of the baby. Data-driven computerized FHR analysis could help clinicians in the decision-making process. However, selecting the best computerized FHR features that relate to labour outcome is a pressing research problem. The objective of this study is to apply genetic algorithms (GA) as a feature selection method to select the best feature subset from 64 FHR features and to integrate these best features to recognize unfavourable FHR patterns. The GA was trained on 404 cases and tested on 106 cases (both balanced datasets) using three classifiers, respectively. Regularization methods and backward selection were used to optimize the GA. Reasonable classification performance is shown on the testing set for the best feature subset (Cohen's kappa values of 0.45 to 0.49 using different classifiers). This is, to our knowledge, the first time that a feature selection method for FHR analysis has been developed on a database of this size. This study indicates that different FHR features, when integrated, can show good performance in predicting labour outcome. It also gives the importance of each feature, which will be a valuable reference point for further studies.
胎儿心率（FHR）在分娩过程中通过纸质记录条（胎心监护图）进行监测，以评估胎儿的健康状况。如果有必要，临床医生可以进行干预并协助婴儿尽快分娩。基于数据的计算机化 FHR 分析可以帮助临床医生做出决策。然而，选择与分娩结果相关的最佳计算机化 FHR 特征是一个紧迫的研究问题。本研究旨在应用遗传算法（GA）作为特征选择方法，从 64 个 FHR 特征中选择最佳特征子集，并整合这些最佳特征来识别不利的 FHR 模式。GA 在 404 个病例上进行训练，并在 106 个病例（均为平衡数据集）上使用三种分类器进行测试。使用正则化方法和后向选择对 GA 进行了优化。对于最佳特征子集，在测试集上显示出了合理的分类性能（使用不同的分类器，Cohen's kappa 值为 0.45 至 0.49）。据我们所知，这是首次在如此大规模的数据库上开发 FHR 分析的特征选择方法。本研究表明，不同的 FHR 特征在整合后可以在预测分娩结果方面表现出良好的性能。它还给出了每个特征的重要性，这将是进一步研究的有价值的参考点。
