Key result
LPS-Net outperforms state-of-the-art methods by ~3% in ECG abnormality clustering accuracy.
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The proposed LPS-Net improves unsupervised multi-lead ECG abnormality recognition by incorporating lead-specific pathological sensitivity.
The electrocardiogram (ECG) is a crucial non-invasive tool for diagnosing cardiovascular diseases. The standard 12-lead ECG configuration provides complementary spatio-temporal projections of cardiac electrical activity from distinct anatomical perspectives, enabling comprehensive pathological assessment. Unsupervised multi-lead ECG abnormality recognition has emerged as a promising approach, yet existing methods suffer from two limitations: insufficient attention to key pathological features and neglect of heterogeneous lead sensitivity. To address these issues, we propose the concept of lead-specific pathological sensitivity and develop the Lead-specific Pathological Sensitivity-aware Network (LPS-Net), comprising three modules: a Multi-scale Lead-specific Spatio-Temporal Encoder for discriminative representations, a Lead-specific Pathological Sensitivity-aware Topological Consensus Module that adaptively captures heterogeneous lead contributions, and an Asymmetric Hybrid Decoder that uses topological consensus to regularise reconstruction. Extensive experiments on four public 12-lead ECG benchmarks (PTB-XL, Chapman, CPSC-2018, and SPH) demonstrate the effectiveness of LPS-Net. On PTB-XL, LPS-Net achieves a clustering accuracy of 0.76 and a Macro F1 of 0.74 across five pathological superclass categories without any label information, outperforming the state-of-the-art method by 3%. Cross-dataset transfer experiments further confirm its generalisation ability. Ablation studies verify that the improvement originates specifically from the pathological-sensitivity formulation, and interpretability analyses reveal that the learned lead weight distributions align with clinical prior knowledge.
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Qin et al. (2026) studied Cardiovascular diseases (ECG abnormality recognition). Lead-specific Pathological Sensitivity-aware Network (LPS-Net) vs. State-of-the-art method was evaluated on Clustering accuracy and Macro F1 on PTB-XL across five pathological superclass categories. The Lead-specific Pathological Sensitivity-aware Network (LPS-Net) achieved a clustering accuracy of 0.76 and a Macro F1 of 0.74 on the PTB-XL dataset, outperforming the state-of-the-art by 3%.
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