IEEE Journal of Biomedical and Health Informatics · Oct 26, 2026
LF-NET: A Low-Frequency Self-Supervised Model for Coronary Artery Disease Detection From Phonocardiogram Signals
By Fynn M, Marocchi M, Rashid J et al.
Coronary artery disease (CAD) remains the leading cause of cardiovascular morbidity and mortality worldwide. As myocardial infarction or stroke maybe the first manifestation of CAD, there is a critical need for early, non-invasive pre-screening. Heart sounds, recorded as phonocardiogram (PCG) signals, provide a low-cost diagnostic avenue. However, CAD-related murmurs are low in both frequency and intensity, rendering them inaudible during traditional auscultation. Deep learning has shown promise for PCG-based CAD classification, yet limited dataset sizes have driven reliance on fine-tuning foundation models. Models such as Wav2Vec 2.0, HuBERT, and EnCodec are pre-trained primarily on speech and environmental audio, while others like OPERA utilise respiratory sounds. Consequently, their feature representations are biased toward higher-frequency content and may be suboptimal for low-frequency PCG analysis. To address this, we propose LF-NET and LF-NET2D; self-supervised frameworks pre-trained on low-frequency ocean sound recordings that share similar frequency range with PCG signals. An energy-based noise removal algorithm is integrated into the proposed framework to extract the cleanest PCG segments from recordings obtained in real clinical environments with typical hospital noise. After fine-tuning, these lightweight models achieve comparable single-channel accuracy to existing foundation models while using fewer parameters and outperform them in multichannel configurations. Fusing LF-NET with OPERA-CE yields a state-of-the-art subject-level accuracy of 80.88% on the current 296-subject practically challenging dataset, approximately 3% higher than standalone existing foundation models.