Hierarchical fusion of electrocardiogram and phonocardiogram data improves heart failure detection.
Where this comes from
- Record sourced from PubMed, PMID 42028404.
- Also identified by DOI 10.1016/j.patter.2025.101448 and PMC identifier 13100685.
- Licence recorded as CC BY-NC-ND.
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Abstract
Heart failure with reduced ejection fraction (HFrEF) is a clinical syndrome with high morbidity and mortality. Developing cost-effective and easily deployable screening methods is crucial for improving early diagnosis and management. We propose LGC<sup>2</sup>-Net, a hierarchical fusion network that leverages multi-channel electrocardiograms (ECGs) and phonocardiograms (PCGs) for HFrEF detection. LGC<sup>2</sup>-Net simultaneously exploits complementarity across modalities and channels with its channel-specific and channel-shared branches. Each channel-specific branch employs a local-to-global hierarchical attention mechanism to capture both local and global semantic information within each ECG-PCG pair. The channel-shared branch further aligns and aggregates features from all channels, enabling effective modeling of inter-channel correlations. We established a new multi-channel ECG-PCG dataset with 2,480 synchronized recordings collected from 620 subjects using a digital stethoscope. Experiments demonstrate that LGC<sup>2</sup>-Net surpasses existing methods by 7.42% in average accuracy, highlighting its potential as an accurate, non-invasive, and scalable tool for HFrEF screening.