Tytuł:

Privacy-preserving Framework for Automated Detection of Arrhythmia in ECG Data, Journal of Telecommunications and Information Technology, 2025, Special Issue

Tytuł publikacji grupowej:

2025, Special Issue, JTIT-artykuły

Autor:

Gil, Kacper ; Vejar, Andres

Temat i słowa kluczowe:

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

Opis:

kwartalnik

Abstrakt:

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.

Numer:

Special Issue

Wydawca:

National Institute of Telecommunications

Data wydania:

2025, Special Issue

Typ zasobu:

artykuł

DOI:

10.26636/jtit.2025.FITCE2024.2042

eISSN:

on-line: ISSN 1899-8852

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Prawa:

Biblioteka Naukowa Instytutu Łączności

Licencja:

CC BY 4.0