Deep Generative Models: The winning key for large and easily accessible ECG datasets?

Area of Science:

• Cardiology
• Artificial Intelligence
• Medical Informatics

Background:

• High-quality, large datasets are crucial for advancing artificial intelligence (AI) in cardiac clinical research.
• Researchers face significant challenges in accessing or creating sufficient electrocardiogram (ECG) signal datasets.
• Existing ECG datasets are often limited in size and accessibility, hindering AI development.

Purpose of the Study:

• To address the scarcity of large and accessible electrocardiogram (ECG) datasets for cardiac research.
• To investigate the potential of deep generative models (DGMs) in generating synthetic ECG data.
• To analyze the capabilities and limitations of DGMs for ECG data synthesis and anonymization.

Main Methods:

• Identification and examination of the primary reasons behind the lack of large ECG datasets.
• In-depth analysis of deep generative models (DGMs) for cardiac data generation.
• Evaluation of DGMs' capacity for generating synthetic ECG signals and supporting data anonymization.

Main Results:

• Deep generative models (DGMs) can generate substantial quantities of synthetic ECG signals.
• DGMs offer a potential solution for data anonymization, facilitating easier data sharing while preserving patient privacy.
• The application of DGMs can promote research progress and collaboration within the framework of open science.

Conclusions:

• Deep generative models (DGMs) present a promising approach to overcome the limitations of current electrocardiogram (ECG) datasets.
• Further research is required to standardize synthetic data quality evaluation and ensure algorithm stability for reliable ECG data generation.
• Leveraging DGMs can significantly accelerate advancements in cardiac clinical research through enhanced data accessibility and privacy preservation.