Employing Generative Networks for Synthetic Phonocardiogram and Electrocardiogram Signal Creation: A Privacy-Ensured Approach to Data Augmentation in Heart Diagnostics

Abstract

The diagnosis of various cardiac conditions necessitates meticulous analysis of Phonocardiogram (PCG) and Electrocardiogram (ECG) signals. In light of this, artificial intelligence and machine learning, coupled with computer-assisted diagnostic techniques, have been progressively integrated into modern healthcare systems, facilitating clinicians in making crucial diagnostic decisions. However, the effectiveness of these deep learning applications hinges on the availability of extensive training data, which exacerbates the risk of privacy violations. In response to this dilemma, research into methodologies for synthetic patient data generation has witnessed a surge. It has been observed that most attempts to generate synthetic ECG and PCG signals focus on modeling the statistical distributions of the available real training data, a process known as Data Augmentation. Among the various data augmentation techniques, Generative Adversarial Networks (GANs) have gained significant traction in recent years. This paper conducts an in-depth exploration and evaluation of GANs, specifically Deep Convolutional GANs and Conditional GANs, for the generation of synthetic ECG and PCG signals.