Reconstructing ECG from indirect signals: a denoising diffusion approach.

Area of Science:

• Artificial Intelligence
• Biomedical Signal Processing
• Computational Biology

Background:

• Electrocardiogram (ECG) signal synthesis is crucial for research and clinical applications.
• Existing generative models face challenges with high-fidelity waveform generation and robustness to signal degradations.

Purpose of the Study:

• Introduce RhythmDiff, a novel diffusion-based generative model for synthesizing high-fidelity 12-lead ECG signals.
• Enhance ECG interpretation and cardiac monitoring capabilities, particularly in challenging data conditions.

Main Methods:

• RhythmDiff utilizes structured state-space modeling for efficient capture of ECG waveform characteristics.
• A Bayesian inverse problem formulation embeds RhythmDiff as a prior, leading to the MGPS algorithm for conditional ECG generation.
• The framework is designed to be robust against noise, missing data patterns, and artifacts.

Main Results:

• RhythmDiff demonstrates superior performance in multi-lead ECG reconstruction and noise reduction compared to state-of-the-art models.
• Evaluated across multiple benchmark datasets, the model shows significant improvements in signal synthesis fidelity.
• The derived MGPS algorithm enables conditional ECG generation resilient to various signal degradations.

Conclusions:

• RhythmDiff offers a powerful new tool for generating realistic ECG signals, advancing AI in cardiology.
• The framework enhances the reliability of ECG interpretation, supporting clinical settings and wearable technologies.
• This work facilitates broader real-time cardiac health monitoring and personalized medicine applications.