CHF Detection From Long-Term ECGs Using Dual-View Class-Specific Broad Aggregation Network
提出一种双视角类别特定宽聚合网络（DCBA-Net），从长期心电图双导联中提取多尺度特征并缓解类别不平衡，在公开数据集上达到高检测性能，可作为辅助诊断工具。
Congestive heart failure (CHF) is a chronic heart condition with high morbidity and mortality, manifesting as persistent abnormal rhythms and electrophysiological disturbances across multiple cardiac regions. Early and accurate detection of CHF using electrocardiograms (ECGs) is essential for clinical management. However, existing algorithms, typically tailored for short-term single-lead recordings, fail to capture multi-scale and multi-view cardiac abnormalities. Additionally, the pronounced class imbalance, with normal samples predominating, substantially compromises the sensitivity of mediocre models to CHF cases. To address these challenges, this article proposes a novel dual-view class-specific broad aggregation network (DCBA-Net) capable of extracting and integrating multi-scale temporal dynamics from long-term ECGs of limb lead II and chest lead V1. Specifically, an ECGNeXt architecture with multi-kernel depthwise convolutions (DWConvs) and channel attention mechanisms (CAMs) as the backbone is first constructed to extract both local and global disease-related features from the two ECG leads. Subsequently, in the information integration stage, a new objective function is designed to enhance interlead interactions by simultaneously enforcing view discrepancy and target consistency constraints. Furthermore, this function assigns class-specific coefficients to elevate the prominence of CHF cases, thereby alleviating the class imbalance problem. Finally, DCBA-Net aggregates consensus and complementary decisions on both leads for improved CHF detection. Experimental results on two publicly available databases show that DCBA-Net achieves 100% across all metrics under the intrapatient paradigm, with an accuracy of 99.4%, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$</tex-math> </inline-formula>-score of 98.98% and a G-mean of 99.1% under the interpatient paradigm, outperforming advanced results and demonstrating its immense potential as an auxiliary CHF diagnostic tool.