轻量级心电图分类模型，提升诊断准确率与效率
EfficientECG: Cross-Attention with Feature Fusion for Efficient Electrocardiogram Classification
- 基于EfficientNet改进，引入跨注意力融合多导联特征
- 在多个数据集上实现高精度，参数量更少
- 适合医疗场景快速部署，支持性别年龄等多特征分析
心电图（ECG）通过测量心脏电活动，可有效检测心脏异常，具有快速、无创、信息丰富等特点，应用前景广阔。本文研究新型深度学习技术，旨在构建高效精准的心电图诊断模型，显著减轻医务人员负担。不同于现有模型误诊率较高的问题，本工作提出的EfficientECG模型基于EfficientNet架构，采用端到端训练自动提取特征，能有效处理多种导联类型、高频长序列的ECG数据。在此基础上，进一步提出一种基于跨注意力机制的特征融合方法，用于分析包含性别、年龄等多维度特征的多导联心电图数据。在多个代表性心电图数据集上的实验表明，该模型在精度、多特征融合能力及模型轻量化方面均优于当前最优方法。
原文摘要 · Abstract (English)
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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