Safer Births - Using Deep Neural Networks on Fetal Heart Rate Signals

Abstract

Infant death is a big issue, especially in Africa and parts of Asia where between 24 and 30 [21] in every thousand do not survive the first month. In Europe this number is only 5.9 in every thousand. Reading fetal heart rate signals requires specialists and is time consuming and tedious work. The objective of this thesis is to determine if deep neural networks can detect birth complications based on fetal heart rate signals collected by using a Moyo fetal heart rate monitor as a part of the Safer Births project. The best method found in this study included augmenting the data to get similarly sized classes, creating spectrogram images, and using a convolutional neural network for classification. The final method produced an F1-score of 0.13 and detected 21.875% of the births were bag-mask ventilation was needed immediately after. The proposed methods tested in this study have not been able to detect birth complications accurately, due to insufficient amounts of data, and low-quality signals missing important features for detection.