Fusing Tabular Features and Deep Learning for Fetal Heart Rate Analysis: A Clinically Interpretable Model for Fetal Compromise Detection

Abstract

Objective: Cardiotocography (CTG) is commonly used to monitor fetal heart rate (FHR) and assess fetal well-being during labor. However, its effectiveness in reducing adverse outcomes remains limited due to low sensitivity and high false-positive rates. This study aims to develop an interpretable deep learning model that fuses FHR time series with tabular clinical features to improve prediction of fetal compromise (umbilical artery pH < 7.05). Methods: We introduce Fusion ResNet, a novel architecture combining residual convolutional networks for FHR signal processing with a parallel neural network for tabular features. The model was trained and internally validated on a private dataset of 9,8..