想在心电图分类上搞轻量化部署？LSTrans用知识蒸馏和低秩适配把大模型压缩到可穿戴设备上跑，基准测试上内存和速度都优化了。
LSTrans提出了一种轻量级混合模型，专为资源受限的可穿戴设备上的自动心电图分类设计。该模型采用1D卷积骨干与交错层架构，捕捉宏观节律趋势和微观形态变化，并级联Transformer编码器建模长程时间依赖。通过低秩适配压缩关键层参数空间，结合同质与异质知识蒸馏，将高容量教师模型的诊断知识迁移至学生模型。在多个基准数据集上，LSTrans在诊断灵敏度和资源效率之间取得平衡，显著降低了峰值内存占用和下游训练延迟。代码已在GitHub开源。
LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification
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.