Article
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Generating Fake ECGs using GANs for Anonymizing Healthcare Data
Version 1
: Received: 31 August 2020 / Approved: 3 September 2020 / Online: 3 September 2020 (05:26:01 CEST)
How to cite: Piacentino, E.; Guarner, A.; Angulo, C. Generating Fake ECGs using GANs for Anonymizing Healthcare Data. Preprints 2020, 2020090060 Piacentino, E.; Guarner, A.; Angulo, C. Generating Fake ECGs using GANs for Anonymizing Healthcare Data. Preprints 2020, 2020090060
Abstract
In personalized healthcare, an ecosystem for the manipulation of reliable and safe private data should be orchestrated. This paper describes a first approach for the generation of fake 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.
Keywords
GAN; ECG; anonymization; healthcare data; sensors; data transformation
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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