SynSigGAN: Generative Adversarial Networks for Synthetic Biomedical Signal Generation.

ABSTRACT: Automating medical diagnosis and training medical students with real-life situations requires the accumulation of large dataset variants covering all aspects of a patient's condition. For preventing the misuse of patient's private information, datasets are not always publicly available. There is a need to generate synthetic data that can be trained for the advancement of public healthcare without intruding on patient's confidentiality. Currently, rules for generating synthetic data are predefined and they require expert intervention, which limits the types and amount of synthetic data. In this paper, we propose a novel generative adversarial networks (GAN) model, named SynSigGAN, for automating the generation of any kind of synthetic biomedical signals. We have used bidirectional grid long

SUBMITTER: Hazra D 

PROVIDER: S-EPMC7761837 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature