LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification

• URL: http://arxiv.org/abs/2607.10784v1
• Date: Sun, 12 Jul 2026 14:23:51 GMT
• Title: LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification
• Abstract summary: We propose LSTrans, a lightweight hybrid model designed for efficient and sensitive ECG analysis.<n>LSTrans introduces a specialized 1D convolutional backbone with an interleaved layer architecture to capture both macroscopic rhythmic trends and microscopic morphological variations.<n>We employ homogeneous and heterogeneous knowledge distillation to transfer diagnostic expertise from high-capacity teacher models to the student.
• Score: 12.358392606750442
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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