通过融合多视角与不确定性，提升心电图心律失常分类的准确率和鲁棒性。
Uncertainty-Aware Multi-view Arrhythmia Classification from ECG
- 从单导联心电图提取一维波形与二维时空图像两种视图
- 在两个数据集上优于现有方法，对噪声和伪影更鲁棒
- 适用于临床心律失常检测，尤其适合信号质量差的场景
我们提出一种深度神经网络架构，实现基于心电图（ECG）的不确定性感知多视角心律失常分类。该方法从单导联心电图中学习两种不同视图（1D与2D），以捕捉不同类型信息。通过融合技术降低因噪声和伪影引起的视图冲突，结合不确定性建模获得更强的最终预测。框架包含三个模块：(1) 时间序列模块用于学习心电图形态特征；(2) 图像空间学习模块用于提取时空特征；(3) 不确定性感知融合模块，整合两视图信息。在两个真实世界数据集上的实验表明，该框架不仅显著提升心律失常分类性能，且对心电图中的噪声和伪影具有更强的鲁棒性。
原文摘要 · Abstract (English)
We propose a deep neural architecture that performs uncertainty-aware multi-view classification of arrhythmia from ECG. Our method learns two different views (1D and 2D) of single-lead ECG to capture different types of information. We use a fusion technique to reduce the conflict between the different views caused by noise and artifacts in ECG data, thus incorporating uncertainty to obtain stronger final predictions. Our framework contains the following three modules (1) a time-series module to learn the morphological features from ECG; (2) an image-space learning module to learn the spatiotemporal features; and (3) the uncertainty-aware fusion module to fuse the information from the two different views. Experimental results on two real-world datasets demonstrate that our framework not only improves the performance on arrhythmia classification compared to the state-of-the-art but also shows better robustness to noise and artifacts present in ECG.
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