Leveraging Statistical Shape Priors in GAN-based ECG Synthesis

TL;DR

This paper introduces a novel GAN-based method that incorporates statistical ECG shape priors to generate realistic ECG signals, improving data augmentation for better cardiac disease diagnosis.

Contribution

It presents a new approach combining statistical shape modeling with GANs for ECG synthesis, enhancing realism over existing methods.

Findings

Generated ECG signals are more realistic than state-of-the-art baselines.

The approach effectively models temporal and amplitude variations of ECG signals.

Improved ECG data quality can enhance classification performance.

Abstract

Electrocardiogram (ECG) data collection during emergency situations is challenging, making ECG data generation an efficient solution for dealing with highly imbalanced ECG training datasets. In this paper, we propose a novel approach for ECG signal generation using Generative Adversarial Networks (GANs) and statistical ECG data modeling. Our approach leverages prior knowledge about ECG dynamics to synthesize realistic signals, addressing the complex dynamics of ECG signals. To validate our approach, we conducted experiments using ECG signals from the MIT-BIH arrhythmia database. Our results demonstrate that our approach, which models temporal and amplitude variations of ECG signals as 2-D shapes, generates more realistic signals compared to state-of-the-art GAN based generation baselines. Our proposed approach has significant implications for improving the quality of ECG training datasets, which can ultimately lead to better performance of ECG classification algorithms. This research contributes to the development of more efficient and accurate methods for ECG analysis, which can aid in the diagnosis and treatment of cardiac diseases.