Forthcoming

Privacy-preserving Framework for Automated Detection of Arrhythmia in ECG Data

Authors

DOI:

https://doi.org/10.26636/jtit.2025.FITCE2024.2042

Keywords:

arrhythmia detection, differential privacy, ECG data, privacy enhancing technologies

Abstract

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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Published

2025-03-07

Issue

Early View

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ARTICLES FROM THIS ISSUE

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Copyright (c) 2025 Kacper Gil, Andres Vejar

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
K. Gil and A. Vejar, “Privacy-preserving Framework for Automated Detection of Arrhythmia in ECG Data”, JTIT, vol. 99, no. 1, Mar. 2025, doi: 10.26636/jtit.2025.FITCE2024.2042.