Predicting high risk birth from real large-scale cardiotocographic data using multi-input convolutional neural networks

Abstract

Apgar score is a test applied 1 minute after birth to check the infant health and can be performed as much as needed. The goal of this paper is to apply a deep learning (DL) method called convolutional neural network (CNN) to predict infants with potentially low Apgar score. Our CNN is a multi-input model that accepts denoised cardiotocography (CTG) images and gestational age. In the first half of the paper, we use basic machine learning (ML) techniques to explore what features and target labels are most effective. In the latter half, we verify to what extent the prediction accuracies can be improved by using our CNN model. Using 5-folds cross validation (CV), the CNN model performance scored an Area Under Curve (AUC) of 0.958 when classifying infants with Apgar score 5 minutes < 7 and AUC of 0.955 if Apgar score 1 or 5 minutes < 6 without using feature extraction algorithms. We conclude that the built model can be utilized as a prognosis tool to predict fetuses with a low Apgar score. Still, we think that a one model isn't enough as obstetricians could benefit more from multiple models that help predict different risks to fetuses.