Abstract
In personalized healthcare, an ecosystem for the manipulation of reliable and safe private data should be orchestrated. This paper describes an approach for the generation of synthetic electrocardiograms (ECGs) based on Generative Adversarial Networks (GANs) with the objective of anonymizing users’ information for privacy issues. This is intended to create valuable data that can be used both in educational and research areas, while avoiding the risk of a sensitive data leakage. As GANs are mainly exploited on images and video frames, we are proposing general raw data processing after transformation into an image, so it can be managed through a GAN, then decoded back to the original data domain. The feasibility of our transformation and processing hypothesis is primarily demonstrated. Next, from the proposed procedure, main drawbacks for each step in the procedure are addressed for the particular case of ECGs. Hence, a novel research pathway on health data anonymization using GANs is opened and further straightforward developments are expected.
Highlights
In recent years there has been a huge proliferation of solutions that store and process personal health data and infer knowledge, from mobile health apps to smart wearable sensors [1]
This paper elaborates on a first approach about using Generative Adversarial Networks (GANs) for the generation of synthetic data, with the objective of anonymizing users information in the health sector, especially in the case of ECGs, a multi-dimensional dynamical system with a raw/image data dual definition
From very recent precedent work it is demonstrated that our proposed GAN system, using standard GAN architectures, is able to get good results on anonymization for personal data on general health data
Summary
In recent years there has been a huge proliferation of solutions that store and process personal health data and infer knowledge, from mobile health apps to smart wearable sensors [1]. A question raised about the feasibility of using GAN systems to generate synthetic data, not necessarily images, that imitates the attributes of a private health database If possible, this generated machine would be a very useful tool as it is enabling unlimited similar-to-the-original data without compromising the privacy of the original elements. We focus on several concerns emerging from the original anonymization procedure about data transformation in images, usual deep learning drawbacks (mode collapse, diminished gradient) and evaluation measures (convergence, quality) Their analysis and results complete the current study and open new lines of research in domains as soft-sensors, deep learning and metrics. A discussion about main results follows and further research issues are listed
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