Improving Adversarial Robustness of ECG Classification Based on Lipschitz Constraints and Channel Activation Suppression.

Xin Chen, Yujuan Si, Zhanyuan Zhang, Wenke Yang, Jianchao Feng

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In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Xin ChenSchool of Electronic and Information Engineering (SEIE), Zhuhai College of Science and Technology, Zhuhai 519041, China.

Yujuan SiSchool of Electronic and Information Engineering (SEIE), Zhuhai College of Science and Technology, Zhuhai 519041, China.

Zhanyuan ZhangSchool of Electronic and Information Engineering (SEIE), Zhuhai College of Science and Technology, Zhuhai 519041, China.

Wenke YangSchool of Electronic and Information Engineering (SEIE), Zhuhai College of Science and Technology, Zhuhai 519041, China.

Jianchao FengSchool of Electronic and Information Engineering (SEIE), Zhuhai College of Science and Technology, Zhuhai 519041, China.

Funding

the Featured Innovation Projects of the Guangdong Universities 2022KTSCX189the Guangdong Key Disciplines Project 2022ZDJS140the Natural Science Foundation of Guangdong Province 2023A1515011302

6 · The paper itself

Abstract

Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturbations. This vulnerability can lead to potential medical accidents. This poses new challenges for the application of DNNs in the medical diagnosis of ECG signals. This paper proposes a novel network Channel Activation Suppression with Lipschitz Constraints Net (CASLCNet), which employs the Channel-wise Activation Suppressing (CAS) strategy to dynamically adjust the contribution of different channels to the class prediction and uses the 1-Lipschitz's

Indexed as

AlgorithmsElectrocardiographyNeural Networks, ComputerHumansSignal Processing, Computer-Assistedadversarial robustnessarrhythmia classificationchannel-wise activation suppressingℓ∞ distance network

Identifiers

PMID38733060

DOI10.3390/s24092954

PMCPMC11086233

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