Rapid detection of fetal compromise using input length invariant deep learning on fetal heart rate signals

Abstract Standard clinical practice to assess fetal well-being during labour utilises monitoring of the fetal heart rate (FHR) using cardiotocography. However, visual evaluation of FHR signals can result in subjective interpretations leading to inter and intra-observer disagreement. Therefore, recent studies have proposed deep-learning-based methods to interpret FHR signals and detect fetal compromise. These methods have typically focused on evaluating fixed-length FHR segments at the conclusion of labour, leaving little time for clinicians to intervene. In this study, we propose a novel FHR evaluation method using an input length invariant deep learning model to progressively evaluate FHR as labour progresses and achieve rapid detection of fetal compromise. Using a new performance metric called time to predict (TTP), we achieve a 25% reduction in the time taken to detect fetal compromise compared to the state-of-the-art multimodal convolutional neural network (MCNN), with equivalent sensitivity at a 20% false positive rate. A diagnostic system based on our approach could potentially enable earlier intervention for fetal compromise and improve clinical outcomes.