  Access to medical data is highly restricted due to its sensitive nature,
preventing communities from using this data for research or clinical training.
Common methods of de-identification implemented to enable the sharing of data
are sometimes inadequate to protect the individuals contained in the data. For
our research, we investigate the ability of generative adversarial networks
(GANs) to produce realistic medical time series data which can be used without
concerns over privacy. The aim is to generate synthetic ECG signals
representative of normal ECG waveforms. GANs have been used successfully to
generate good quality synthetic time series and have been shown to prevent
re-identification of individual records. In this work, a range of GAN
architectures are developed to generate synthetic sine waves and synthetic ECG.
Two evaluation metrics are then used to quantitatively assess how suitable the
synthetic data is for real world applications such as clinical training and
data analysis. Finally, we discuss the privacy concerns associated with sharing
synthetic data produced by GANs and test their ability to withstand a simple
membership inference attack. For the first time we both quantitatively and
qualitatively demonstrate that GAN architecture can successfully generate time
series signals that are not only structurally similar to the training sets but
also diverse in nature across generated samples. We also report on their
ability to withstand a simple membership inference attack, protecting the
privacy of the training set.
