论文标题
改善DNN进行ECG信号分类的鲁棒性：噪声与信号比率的观点
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective
论文作者
论文摘要
心电图（ECG）是监测心血管系统状况的最广泛使用的诊断工具。在许多研究实验室中已经开发了深层神经网络（DNN），用于自动解释ECG信号，以鉴定患者心脏中潜在的异常。研究表明，鉴于大量数据，DNN的分类准确性可以达到人类专家心脏病专家水平。对于缺乏人类专家心脏病专家的发展中国家，基于DNN的自动ECG诊断系统将是一个负担得起的解决方案。但是，尽管在分类精度方面表现出色，但已表明DNN非常容易受到对抗性攻击的影响：DNN输入的细微变化可能会以高信心导致错误的分类输出。因此，提高DNN的对抗性鲁棒性来进行ECG信号分类，这是一种至关重要的生命应用，这是具有挑战性的。在这项工作中，我们建议从噪声与信号比（NSR）的角度提高DNN鲁棒性，并开发了两种方法，以最大程度地减少NSR在训练过程中。我们评估了Physionnets Mit-BIH数据集的提议方法，结果表明，我们提出的方法可提高针对PGD对抗攻击和SPSA攻击的鲁棒性，并且清洁数据的准确性最小。
Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the cardiovascular system. Deep neural networks (DNNs), have been developed in many research labs for automatic interpretation of ECG signals to identify potential abnormalities in patient hearts. Studies have shown that given a sufficiently large amount of data, the classification accuracy of DNNs could reach human-expert cardiologist level. A DNN-based automated ECG diagnostic system would be an affordable solution for patients in developing countries where human-expert cardiologist are lacking. However, despite of the excellent performance in classification accuracy, it has been shown that DNNs are highly vulnerable to adversarial attacks: subtle changes in input of a DNN can lead to a wrong classification output with high confidence. Thus, it is challenging and essential to improve adversarial robustness of DNNs for ECG signal classification, a life-critical application. In this work, we proposed to improve DNN robustness from the perspective of noise-to-signal ratio (NSR) and developed two methods to minimize NSR during training process. We evaluated the proposed methods on PhysionNets MIT-BIH dataset, and the results show that our proposed methods lead to an enhancement in robustness against PGD adversarial attack and SPSA attack, with a minimal change in accuracy on clean data.
