发表机构
School of Mechano-Electronic Engineering Xidian University Xi'an, China; School of Cyber Engineering Xidian University Xi'an, China; DCST, BNRist, RIIT, Institute of Internet Industry Tsinghua University Beijing, China（机械电子工程学院 西安电子科技大学 西安中国; 网络工程学院 西安电子科技大学 西安中国; DCST、BNRist、RIIT、互联网产业研究院 清华大学 北京中国）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对可穿戴设备上心电图分类计算成本高的问题，提出轻量级混合模型LSTrans，它结合一维卷积主干、Transformer编码器及知识蒸馏，在多基准数据集实验中实现诊断灵敏度与资源效率的平衡，减少内存占用和训练延迟。
AI 中文摘要
在资源受限的可穿戴设备上部署用于自动心电图分类的深度学习模型因计算成本高而具有挑战性。为解决此问题，我们提出了LSTrans，一种为高效且灵敏的心电图分析设计的轻量级混合模型。LSTrans引入了具有交错层架构的专门一维卷积主干，以捕捉宏观节律趋势和微观形态变化。该主干与Transformer编码器级联以建模长程时间依赖性，并在关键层采用低秩适应来压缩模型并减少可训练参数空间。我们还采用同构和异构知识蒸馏将诊断专业知识从高容量教师模型转移到学生模型。在多个基准数据集上的实验结果表明，LSTrans在诊断灵敏度和资源效率之间实现了有竞争力的平衡，大幅减少了下游适应期间的峰值内存占用和训练延迟。
英文摘要
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.
CommentsSubmitted to BIBM 2026
