A Deformable Lead-Attention Fusion Network for Multi-Label ECG Classification Integrating Clinical Metadata

Cardiovascular diseases remain a major global health burden, making accurate elec-trocardiogram (ECG) analysis essential for timely diagnosis. While deep learning-based methods have made significant progress in automating ECG classification, they are often limited by the variability of ECG signals, the frequent presence of multiple cardiac abnor-malities, and the inadequate integration of diverse clinical data. This study proposes DLM-Net: a robust deep learning framework for multi-label ECG classification, designed to address these challenges. DLM-Net in-corporates several key innovations. First, the Lead-Specific Deformable Path applies de-formable convolutions independently to each lead. This enables adaptive modelling of non-rigid and complex waveform variations. It is also enhanced by a High-Frequency Module, which captures fine-grained tem-poral details. Secondly, a spatial path based on a ResNet-like backbone and feature pyr-amid network (FPN) fuses multi-scale repre-sentations in order to effectively capture both global and local ECG patterns. In parallel, the Lead Path uses a Lead Attention Trans-former to explicitly model inter-lead de-pendencies. Finally, ECG features from the spatial and lead paths are integrated with clinical tabular data through a multimodal fusion module, in which compact pa-tient-level tabular embeddings provide com-plementary contextual information for ECG representation. Extensive experiments on three public multi-label ECG datasets PTB-XL, CPSC2018 and Chapman demonstrate that DLM-Net achieves compet-itive and stable performance across datasets, with label-wise accuracies of 89.11%, 96.05% and 97.82%, respectively. Overall, DLM-Net provides an effective and flexible framework for multi-label ECG classification, offering improved accuracy and robustness across diverse datasets and demonstrating the effec-tiveness of the proposed architecture in han-dling complex multi-label diagnostic tasks in retrospective settings.