Siamese neural network-enhanced electrocardiography can re-identify anonymized healthcare data
Author(s)
Type
Journal Article
Abstract
Aims
Many research databases with anonymized patient data contain electrocardiograms (ECGs) from which traditional identifiers have been removed. We evaluated the ability of artificial intelligence (AI) methods to determine the similarity between ECGs and assessed whether they have the potential to be misused to re-identify individuals from anonymized datasets.
Methods and results
We utilized a convolutional Siamese neural network (SNN) architecture, which derives a Euclidean distance similarity metric between two input ECGs. A secondary care dataset of 864 283 ECGs (72 455 subjects) was used. Siamese neural network-electrocardiogram (SNN-ECG) achieves an accuracy of 91.68% when classifying between 2 689 124 same-subject pairs and 2 689 124 different-subject pairs. This performance increases to 93.61% and 95.97% in outpatient and normal ECG subsets. In a simulated ‘motivated intruder’ test, SNN-ECG can identify individuals from large datasets. In datasets of 100, 1000, 10 000, and 20 000 ECGs, where only one ECG is also from the reference individual, it achieves success rates of 79.2%, 62.6%, 45.0%, and 40.0%, respectively. If this was random, the success would be 1%, 0.1%, 0.01%, and 0.005%, respectively. Additional basic information, like subject sex or age-range, enhances performance further. We also found that, on the subject level, ECG pair similarity is clinically relevant; greater ECG dissimilarity associates with all-cause mortality [hazard ratio, 1.22 (1.21–1.23), P < 0.0001] and is additive to an AI-ECG model trained for mortality prediction.
Conclusion
Anonymized ECGs retain information that may facilitate subject re-identification, raising privacy and data protection concerns. However, SNN-ECG models also have positive uses and can enhance risk prediction of cardiovascular disease.
Many research databases with anonymized patient data contain electrocardiograms (ECGs) from which traditional identifiers have been removed. We evaluated the ability of artificial intelligence (AI) methods to determine the similarity between ECGs and assessed whether they have the potential to be misused to re-identify individuals from anonymized datasets.
Methods and results
We utilized a convolutional Siamese neural network (SNN) architecture, which derives a Euclidean distance similarity metric between two input ECGs. A secondary care dataset of 864 283 ECGs (72 455 subjects) was used. Siamese neural network-electrocardiogram (SNN-ECG) achieves an accuracy of 91.68% when classifying between 2 689 124 same-subject pairs and 2 689 124 different-subject pairs. This performance increases to 93.61% and 95.97% in outpatient and normal ECG subsets. In a simulated ‘motivated intruder’ test, SNN-ECG can identify individuals from large datasets. In datasets of 100, 1000, 10 000, and 20 000 ECGs, where only one ECG is also from the reference individual, it achieves success rates of 79.2%, 62.6%, 45.0%, and 40.0%, respectively. If this was random, the success would be 1%, 0.1%, 0.01%, and 0.005%, respectively. Additional basic information, like subject sex or age-range, enhances performance further. We also found that, on the subject level, ECG pair similarity is clinically relevant; greater ECG dissimilarity associates with all-cause mortality [hazard ratio, 1.22 (1.21–1.23), P < 0.0001] and is additive to an AI-ECG model trained for mortality prediction.
Conclusion
Anonymized ECGs retain information that may facilitate subject re-identification, raising privacy and data protection concerns. However, SNN-ECG models also have positive uses and can enhance risk prediction of cardiovascular disease.
Date Issued
2025-05-01
Date Acceptance
2025-01-27
Citation
European Heart Journal: Digital Health, 2025, 6 (3), pp.417-426
ISSN
2634-3916
Publisher
Oxford University Press
Start Page
417
End Page
426
Journal / Book Title
European Heart Journal: Digital Health
Volume
6
Issue
3
Copyright Statement
© The Author(s) 2025. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/40395429
PII: ztaf011
Subjects
Artificial intelligence
Continuous monitoring
Electrocardiogram
Identification
Siamese neural network
Publication Status
Published
Coverage Spatial
England
Date Publish Online
2025-02-25