Computer methods and programs in biomedicine · 2026
SLLD: Single-lead ECG LQTS detection framework based on knowledge distillation.
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Zhou Xiaoyu, Yang Wenming, Wang Guijin, Hu Yuanyun, Lin Xiaojian, Liu Yu, Zhang Ping, Chen Bo
Аннотация
BACKGROUND AND OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.