{"ID":6536225,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10784","arxiv_id":"2607.10784","title":"LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification","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 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.","short_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...","url_abs":"https://arxiv.org/abs/2607.10784","url_pdf":"https://arxiv.org/pdf/2607.10784v1","authors":"[\"Yi Zhao\",\"Jiajun Gao\",\"Chenyang Xu\",\"Yuxi Zhou\",\"Hao Wang\"]","published":"2026-07-12T14:23:51Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":614149,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-14T01:21:01.169441415Z","DeletedAt":null,"paper_id":6536225,"paper_url":"https://arxiv.org/abs/2607.10784","paper_title":"LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification","repo_url":"https://github.com/zyee00128/LSTrans4BIBM","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}