Assessing the Reidentification Risks Posed by Deep Learning Algorithms Applied to ECG Data

Authors

Arin Ghazarian, Chapman UniversityFollow
Jianwei Zheng, Chapman UniversityFollow
Daniele Struppa, Chapman UniversityFollow
Cyril Rakovski, Chapman UniversityFollow

Document Type

Article

Publication Date

6-23-2022

Abstract

ECG (Electrocardiogram) data analysis is one of the most widely used and important tools in cardiology diagnostics. In recent years the development of advanced deep learning techniques and GPU hardware have made it possible to train neural network models that attain exceptionally high levels of accuracy in complex tasks such as heart disease diagnoses and treatments. We investigate the use of ECGs as biometrics in human identification systems by implementing state-of-the-art deep learning models. We train convolutional neural network models on approximately 81k patients from the US, Germany and China. Currently, this is the largest research project on ECG identification. Our models achieved an overall accuracy of 95.69%. Furthermore, we assessed the accuracy of our ECG identification model for distinct groups of patients with particular heart conditions and combinations of such conditions. For example, we observed that the identification accuracy was the highest (99.7%) for patients with both ST changes and supraventricular tachycardia. We also found that the identification rate was the lowest for patients diagnosed with both atrial fibrillation and complete right bundle branch block (49%). We discuss the implications of these findings regarding the reidentification risks of patients based on ECG data and how seemingly anonymized ECG datasets can cause privacy concerns for the patients.

Comments

This article was originally published in IEEE Access, volume 10, in 2022. https://doi.org/10.1109/ACCESS.2022.3185615

Recommended Citation

A. Ghazarian, J. Zheng, D. Struppa and C. Rakovski, "Assessing the Reidentification Risks Posed by Deep Learning Algorithms Applied to ECG Data," in IEEE Access, vol. 10, pp. 68711-68723, 2022, https://doi.org/10.1109/ACCESS.2022.3185615.

Peer Reviewed

1

Creative Commons License

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