SLLD: Single-lead ECG LQTS detection framework based on knowledge distillation

Long QT syndrome (LQTS) has received increasing attention because of its association with sudden cardiac death. Traditional LQTS diagnosis relies on standard 12-lead electrocardiograms (ECGs) in hospital settings, which may limit timely detection outside clinical settings. With the proliferation of portable single-lead ECG devices, single-lead diagnostic solutions offer new potential for efficient arrhythmia screening. However, single-lead systems are inherently limited by information sparsity, and the complex, variable waveform morphology of LQTS further challenges accurate detection.

To address this, we propose a Single-Lead LQTS Detection (SLLD) framework based on knowledge distillation from a 12-lead teacher model. By leveraging an efficient knowledge transfer mechanism, the proposed framework enhances the representation learning capability of the single-lead network for LQTS-related ECG classification. Specifically, we design a feature fusion module to integrate local waveform morphology and temporal dependencies of ECG signals, and use a first-order temporal derivative augmentation strategy to supplement the raw signal with local dynamic variation information, thereby enabling the model to better characterize subtle waveform and temporal changes in LQTS-related ECG signals.

Experimental results on public datasets demonstrate that the SLLD framework achieves improved performance over state-of-the-art methods. Specifically, SLLD achieved an AUC of 0.903 and an average F1-score of 0.706 on the Georgia dataset. Notably, our method improved the LQTS-specific F1-score by 4.9 percentage points over the strongest evaluated baseline, indicating the utility of the proposed framework.

The SLLD framework mitigates the inherent information sparsity of single-lead signals by distilling diagnostic knowledge from multi-lead systems, demonstrating the potential of knowledge-distillation-based single-lead LQTS screening.