Adversarial Training Enhances Robustness in Contrastive ECG Models on PTB-XL and MIT-BIH

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: To what extent does adversarial training improve the robustness of contrastive learning models on ECG datasets like PTB-XL and MIT-BIH, as measured by AUC-ROC under adversarial perturbations compared. 11 claims were extracted from source literature; 11 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

To what extent does adversarial training improve the robustness of contrastive learning models on ECG datasets like PTB-XL and MIT-BIH, as measured by AUC-ROC under adversarial perturbations compared to standard contrastive losses?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance