EfficientECG: Cross-Attention with Feature Fusion for Efficient Electrocardiogram Classification

• URL: http://arxiv.org/abs/2512.03804v2
• Date: Sun, 07 Dec 2025 06:10:14 GMT
• Title: EfficientECG: Cross-Attention with Feature Fusion for Efficient Electrocardiogram Classification
• Authors: Hanhui Deng, Xinglin Li, Jie Luo, Di Wu,
• Abstract summary: We study novel deep learning technologies to effectively manage and analyse ECG data.<n>Our deep learning approaches can automatically extract the features of ECG data through end-to-end training.<n>Our evaluations on ECG datasets validate our model against state-of-the-art works in terms of high precision, multi-feature fusion, and lightweights.
• Score: 7.5367987995144565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electrocardiogram is a useful diagnostic signal that can detect cardiac abnormalities by measuring the electrical activity generated by the heart. Due to its rapid, non-invasive, and richly informative characteristics, ECG has many emerging applications. In this paper, we study novel deep learning technologies to effectively manage and analyse ECG data, with the aim of building a diagnostic model, accurately and quickly, that can substantially reduce the burden on medical workers. Unlike the existing ECG models that exhibit a high misdiagnosis rate, our deep learning approaches can automatically extract the features of ECG data through end-to-end training. Specifically, we first devise EfficientECG, an accurate and lightweight classification model for ECG analysis based on the existing EfficientNet model, which can effectively handle high-frequency long-sequence ECG data with various leading types. On top of that, we next propose a cross-attention-based feature fusion model of EfficientECG for analysing multi-lead ECG data with multiple features (e.g., gender and age). Our evaluations on representative ECG datasets validate the superiority of our model against state-of-the-art works in terms of high precision, multi-feature fusion, and lightweights.

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