Balancing privacy and health integrity: A novel framework for ECG signal analysis in immersive environments

The widespread use of immersive technologies such as Virtual Reality, Mixed Reality, and Augmented Reality has led to the continuous collection and streaming of vast amounts of sensitive biometric data. Among the biometric signals collected, ECG (electrocardiogram) stands out given its critical role in healthcare, particularly for the diagnosis and management of cardiovascular diseases. Numerous studies have demonstrated that ECG contains traits to distinctively identify a person. As a result, the need for anonymization methods is becoming increasingly crucial to protect personal privacy while ensuring the integrity of health data for effective clinical utility. Although many anonymization methods have been proposed in the literature, there has been limited exploration into their ability to preserve data integrity while complying with stringent data protection regulations. More specifically, the utility of anonymized signal and the privacy level achieved often present a trade-off that has not been thoroughly addressed. This paper analyzes the trade-off between balancing privacy protection with the preservation of health data integrity in ECG signals focusing on memory-efficient anonymization techniques that are suitable for real-time or streaming applications and do not require heavy memory computation. Moreover, we introduce an analytical framework to evaluate the privacy preservation methods alongside health integrity, incorporating state-of-the-art disease and person identifiers. We also propose a novel metric that assists users in selecting an anonymization method based on their desired trade-off between health insights and privacy protection. The experimental results demonstrate the impact of the de-identification techniques on critical downstream tasks, such as Arrhythmia detection and Myocardial Infarction detection along with identification performance, while statistical analysis reveals the biometric nature of ECG signals. The findings highlight the limitations of using such anonymization methods and models, emphasizing the need for approaches that maintain the clinical relevance of ECG data in real-time and streaming applications, particularly in memory-constrained environments.