Key result
A differentially-private CNN reduces arrhythmia detection accuracy by ~15% versus a baseline privacy-unaware model.
Why the study?
The integration of machine learning in biomedical engineering requires frameworks to ensure user data security, privacy, and reduce the risk of biometric identification.
Absolute Event Rate: 73.35% vs 88.46%
Implementing differential privacy in ECG arrhythmia detection models reduces classification accuracy compared to baseline models, highlighting a trade-off between data privacy and diagnostic performance.
May limit clinical use of arrhythmia AI due to accuracy costs; leaves open refinements balancing privacy and diagnostic performance.
The integration of machine learning in biomedical engineering applications is crucial to ensure user data security and privacy. This work explores anonymization and differential privacy (DP) frameworks to reduce the risk of biometric identification. The DP method is used to train models in biosignal data without compromising the diagnostic results. The proposed approach for privacy-preserving arrhythmia detection uses a machine learning diagnostic system that reduces discrepancies between prepossessed and raw data, maintaining a correct level of diagnostic precision while improving privacy. The application is evaluated using a control model to analyze the accuracy difference when using privacy-preserving input data.
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Gil et al. (2025) studied Arrhythmia (n=12,000). Differentially-private Convolutional Neural Network vs. Baseline Convolutional Neural Network (privacy-unaware) was evaluated on Classification accuracy. A differentially-private convolutional neural network for arrhythmia detection achieved an accuracy of 73.35%, compared to 88.46% for the baseline privacy-unaware model.
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