A 1D convolutional neural network trained on balanced windowed segments of fetal heart rate traces classifies abnormal birth outcomes with an AUC of 0.86, outperforming classical baselines tested by the authors.
Computer analysis of antepartum fetal heart rate: 2. detection of accelerations and decelerations,
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Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes
A 1D convolutional neural network trained on balanced windowed segments of fetal heart rate traces classifies abnormal birth outcomes with an AUC of 0.86, outperforming classical baselines tested by the authors.