Paperdoi:10.1016/j.bspc.2026.111680
Lead-adaptive self-supervised ECG representation model for multi-scenario ECG monitoring
Biomedical Signal Processing and Control (Elsevier), published 2026-10-21; licence cc-by-nc-nd per OpenAlex
TL;DR
Stage 1: VQ-VAE ECG tokenizer turns raw segments into morphology-aware waveform tokens, with patch and lead-time positional encodings. Stage 2: masked autoencoder with an adaptive-lead training algorithm so one backbone handles 1-, 3-, 8- and 12-lead inputs. Pretrained on 178,602 multi-center ECG records; nine downstream tasks.
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Abstract
Background Electrocardiogram (ECG) is a critical non-invasive tool for the early detection of cardiovascular diseases. However, ECG interpretation remains challenging because of signal complexity, limited medical resources, and heterogeneous monitoring settings. Most deep learning models are still designed for specific tasks and fixed lead configurations, limiting their transfer across clinical and wearable ECG scenarios. Objective This study aims to develop a lead-adaptive self-supervised ECG representation model that supports transferable analysis across representative tasks and lead configurations. Methods The proposed model follows a two-stage framework. First, a VQ-VAE-based ECG tokenizer converts raw ECG segments into morphology-aware waveform tokens and integrates patch and lead-time positional encodings to retain spatiotemporal context. Second, a Masked Autoencoder (MAE) extracts rich representations from unlabeled data, empowered by a novel adaptive-lead training algorithm to generalize across varying lead configurations. Results After pretraining on 178,602 multi-center ECG records, the model was evaluated on nine downstream ECG tasks under representative 1-, 3-, 8-, and 12-lead settings. Compared with a plain MAE baseline, it improved average macro-F1 by +2.56, +4.85, and +12.80 points on broad multi-label, morphology-related, and rhythm-related classification, respectively. Ablation analyses further supported the contributions of ECG tokenization and lead-adaptive pretraining. Conclusions The proposed model improves representation transfer across the evaluated ECG monitoring scenarios. By providing a robust representation backbone for intelligent ECG analysis, it offers a transferable technical solution for clinical ECG interpretation and wearable ECG monitoring, and may reduce the need to develop separate models for each task and lead configuration.
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APA
Li, Z., Tian, Y., Wei, X., Wang, M., Jin, Y., Shi, Z., Xiaohan, T., Liu, Y., Zhao, L., & Liu, C. (2027). Lead-adaptive self-supervised ECG representation model for multi-scenario ECG monitoring. Biomedical Signal Processing and Control. https://doi.org/10.1016/j.bspc.2026.111680
BibTeX
@article{li2027leadadaptive,
title = {{Lead-adaptive self-supervised ECG representation model for multi-scenario ECG monitoring}},
author = {Li, Zhiyuan and Tian, Yuanyuan and Wei, Xiaoyang and Wang, Mengxiao and Jin, Yanrui and Shi, Zehua and Xiaohan, Tang and Liu, Yushun and Zhao, Liqun and Liu, Chengliang},
year = {2027},
journal = {Biomedical Signal Processing and Control},
doi = {10.1016/j.bspc.2026.111680}
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