Extraction of fetal heartbeat locations in abdominal phonocardiograms using deep attention transformer

Assessing fetal health traditionally involves techniques like echocardiography, which require skilled professionals and specialized equipment, making them unsuitable for low-resource settings. An emerging alternative is Phonocardiography (PCG), which offers affordability but suffers from challenges related to accuracy and complexity. To address these limitations, we propose a deep learning model, Fetal Heart Sounds U-NetR (FHSU-NETR), capable of extracting both fetal and maternal heart rates directly from raw PCG signals. FHSU-NETR is designed for practical implementation in various healthcare environments, enhancing accessibility and reliability of fetal monitoring. Due to its enhanced capacity to simulate remote interactions and capture global context, the suggested pipeline utilizes the self-attention mechanism of the transformer. Validated with data from 20 normal subjects, including a case of fetal tachycardia arrhythmia, FHSU-NETR demonstrated exceptional performance. It accurately identified most of the fetal heartbeat locations with a low mean difference in fetal heart rate estimation (-2.55±10.25 bpm) across the entire dataset, and successfully detected the arrhythmia case. Similarly, FHSU-NETR showed a low mean difference in maternal heart rate estimation (-1.15±5.76 bpm) compared to the ground-truth maternal ECG. The model's exceptional ability to identify arrhythmia cases within the dataset underscores its potential for real-world application and generalization. By leveraging the capabilities of deep learning, our proposed model holds promise to reduce the reliance on medical experts for the interpretation of extensive PCG recordings, thereby enhancing efficiency in clinical settings.