Improving Deep Learning Classifier for Fetus Hypoxia Detection in Cardiotocography Signal

M. Anwar Ma'sum, P. Riskyana Dewi Intan, Wisnu Jatmiko, Adila Alfa Krisnadhi, Noor Akhmad Setiawan, I Made Agus Dwi Suarjaya

Abstract: One of the stage that performed during a maternal and fetal health monitoring is the calculation of fetal heart rate and uterine contraction using Cardiotocography (CTG). The aim of fetal health using CTG is to avoid morbidity and mortality in fetus at risk of hypoxia. This paper propose a hypoxia detection by using classification. In this study, we improve deep learning method in order to increase its capability in detecting hypoxia. The improvement is conducted by several strategies i.e. input representation, data-scaling, data up-sampling, and adjusting classifier layers. The improvement is conducted by several strategies i.e. input representation, data-scaling, data up-sampling, and increasing classifier layers. For whole dataset that achieved by proposed method is 81%. The improved DenseNet achieved 50%, 43%, 46% precision, recall and f1-score respectively for hypoxia class that is not achieved by standard DenseNet.