  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. However, despite
of the excellent performance in classification accuracy, it has been shown that
DNNs are highly vulnerable to adversarial noises which are subtle changes in
input of a DNN and lead to a wrong class-label prediction with a high
confidence. Thus, it is challenging and essential to improve robustness of DNNs
against adversarial noises for ECG signal classification, a life-critical
application. In this work, we designed a CNN for classification of 12-lead ECG
signals with variable length, and we applied three defense methods to improve
robustness of this CNN for this classification task. The ECG data in this study
is very challenging because the sample size is limited, and the length of each
ECG recording varies in a large range. The evaluation results show that our
customized CNN reached satisfying F1 score and average accuracy, comparable to
the top-6 entries in the CPSC2018 ECG classification challenge, and the defense
methods enhanced robustness of our CNN against adversarial noises and white
noises, with a minimal reduction in accuracy on clean data.
