FHSU-NETR: Transformer-Based Deep Learning Model for the Detection of Fetal Heart Sounds in Phonocardiography

Assessing fetal well-being using conventional tools requires skilled clinicians for interpretation and can be susceptible to noise interference, especially during lengthy recordings or when maternal effects contaminate the signals. In this study, we present a novel transformer-based deep learning model called fetal heart sounds U-Net Transformer (FHSU-NETR) for automated extraction of fetal heart activity from raw phonocardiography (PCG) signals. The model was trained using a realistic synthetic dataset and validated on data recorded from 20 healthy mothers at the pregnancy outpatient clinic of Tohoku University Hospital, Japan. The model successfully extracted fetal PCG signals; achieving a heart rate mean difference of-1.5 bpm compared to the ground-truth calculated from fetal electrocardiogram (ECG). By leveraging deep learning, FHSU-NETR would facilitates timely interpretation of lengthy PCG recordings while reducing the heavy reliance on medical experts; thereby enhancing the efficiency in clinical practice.