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Increased Risks of Re-identification For Patients Posed by Deep Learning-Based ECG Identification Algorithms

ECGs analysis is an important tool in cardiac diagnosis. ECG data also have the potential to be used as a biometric source that allows precise person identification similar to the widely used fingerprint and iris recognition techniques. However, this phenomenon also raises serious privacy concerns. In this study, we provide a complete, multi-step ECG identification algorithm using a private database of ECG recordings. We train and validate our AI model on approximately 40k patients which makes this study by far the largest research project in this field. Moreover, our best model attained an exceptionally high accuracy of 94.56%. In addition to discussing the general implications of the deployment of such systems related to privacy, for the first time, we also assess the accuracy of ECG-based identification for distinct heart condition groups (and combinations of such conditions) and the corresponding privacy implications. For instance, we discovered that in contrast to initial expectation that identification accuracy for healthy normal sinus rhythm should be the highest, the identification accuracy is higher for patients with sinus tachycardia or patients who are diagnosed with both ST changes and supraventricular tachycardia. This puts these patients at a higher risk of privacy issues due to re-identification. On the other hand, we observed that patients with premature ventricular contractions have an identification accuracy as low as 78.54%. The identification rate for patients with a pacemaker is 80.2%.Clinical relevance—While ECG as a biometric can be a potentially useful technology, it also raises serious concerns regarding the privacy of cardiac patients. Especially, the ECG Identification algorithms empowered by deep learning can increase the risk of re-identification.