Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals
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Cardiotocography (CTG) is a screening tool used in daily obstetric practice to determine fetal wellbeing. Its interpretation is generally performed visually by the field experts, and this visual inspection is an error-prone and subjective process. In addition, it leads to several drawbacks, such as variability among the observers and low reproducibility rates. To tackle these drawbacks, a novel computer-aided diagnostic (CAD) model is proposed. As novel diagnostic indices, the features provided by the common spatial patterns (CSP) were considered in this study. The experiments were carried out on a publicly available CTU-UHB Intrapartum CTG database. Four different data division criteria were evaluated individually. The proposed model relied upon a combination of the conventional as well as the CSP features and machine learning models such as an artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (kNN). To validate the successes of the models, the five-fold cross-validation method was employed. The results validated that the CSP features ensured an increase in the performances of the machine learning models in the fetal hypoxia detection task. Also, the most effective results were provided by the SVM classifier with an accuracy of 94.75%, a sensitivity of 74.29% and a specificity of 99.55%. Consequently, thanks to the proposed model, a novel, consistent, and robust diagnostic model ensured for predicting fetal hypoxia. (C) 2020 Elsevier Ltd. All rights reserved.