ECG Biosignal Deidentification Using Conditional Generative Adversarial Networks.

Electrocardiogram (ECG) signals provide rich information on individuals' potential cardiovascular conditions and disease, ranging from coronary artery disease to the risk of a heart attack. While health providers store and share these information for medical and research purposes, such data is highly vulnerable to privacy concerns, similar to many other types of healthcare data. Recent works have shown the feasibility of identifying and authenticating individuals by using ECG as a biometric due to the highly individualized nature of ECG signals. However, to the best of our knowledge, there does not exist a method in the literature attempting to de-identify ECG signals. In this paper, to address this privacy protection gap, we propose a Generative Adversarial Network (GAN)-based framework for de-identification of ECG signals. We leverage a combination of a standard GAN loss, an Ordinary Differential Equations (ODE)-based, and identity-based loss values to train a generator that de-identifies a ECG signal while preserving structure the ECG signal and information regarding the target cardio vascular condition. We evaluate our framework in terms of both qualitative and quantitative metrics considering different weightings over the above-mentioned losses. Our experiments demonstrate the efficiency of our framework in terms of privacy protection and ECG signal structural preservation.