Synthesis of Realistic ECG using Generative Adversarial Networks

TL;DR

This paper explores the use of GANs to generate realistic and diverse synthetic ECG signals that can be used for research and training, while also addressing privacy concerns and resistance to re-identification attacks.

Contribution

It introduces novel GAN architectures for ECG synthesis, evaluates their effectiveness quantitatively and qualitatively, and demonstrates their privacy-preserving capabilities against membership inference attacks.

Findings

GANs can generate structurally similar ECG signals.

Synthetic ECGs are diverse across samples.

Generated data withstands simple membership inference attacks.

Abstract

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.