Paper Detail
Yi Zhao, Jiajun Gao, Chenyang Xu, Yuxi Zhou, Hao Wang
Deploying deep learning models for automated electrocardiogram classification on resource-constrained wearable devices remains challenging due to high computational costs. To address this, we propose LSTrans, a lightweight hybrid model designed for efficient and sensitive ECG analysis. LSTrans introduces a specialized 1D convolutional backbone with an interleaved layer architecture to capture both macroscopic rhythmic trends and microscopic morphological variations. This backbone is cascaded with a Transformer encoder to model long-range temporal dependencies, incorporating Low-Rank Adaptation across critical layers to compress the model and reduce the trainable parameter space. We further employ homogeneous and heterogeneous knowledge distillation to transfer diagnostic expertise from high-capacity teacher models to the student. Experimental results on multiple benchmark datasets demonstrate that LSTrans achieves a competitive balance between diagnostic sensitivity and resource efficiency, substantially reducing peak memory footprints and training latency during downstream adaptation. The source code is available for review at https://github.com/zyee00128/LSTrans4BIBM.
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@article{zhao2026lstrans,
title = {LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification},
author = {Yi Zhao and Jiajun Gao and Chenyang Xu and Yuxi Zhou and Hao Wang},
year = {2026},
abstract = {Deploying deep learning models for automated electrocardiogram classification on resource-constrained wearable devices remains challenging due to high computational costs. To address this, we propose LSTrans, a lightweight hybrid model designed for efficient and sensitive ECG analysis. LSTrans introduces a specialized 1D convolutional backbone with an interleaved layer architecture to capture both macroscopic rhythmic trends and microscopic morphological variations. This backbone is cascaded wit},
url = {https://arxiv.org/abs/2607.10784},
keywords = {cs.LG, cs.AI},
eprint = {2607.10784},
archiveprefix = {arXiv},
}
{}