Generating Realistic Multi-Beat ECG Signals

TL;DR

This paper introduces a three-layer framework for generating realistic long-form ECG signals, combining diffusion models and feature-guided matching to produce sequences suitable for clinical and educational use.

Contribution

A novel multi-stage synthesis approach that effectively generates long-duration ECG signals with preserved morphological and temporal features.

Findings

Synthetic ECGs maintain morphological fidelity.

Generated sequences outperform diffusion-only models in arrhythmia classification.

Approach enables multi-minute ECG sequence generation.

Abstract

Generating synthetic ECG data has numerous applications in healthcare, from educational purposes to simulating scenarios and forecasting trends. While recent diffusion models excel at generating short ECG segments, they struggle with longer sequences needed for many clinical applications. This paper proposes a novel three-layer synthesis framework for generating realistic long-form ECG signals. We first generate high-fidelity single beats using a diffusion model, then synthesize inter-beat features preserving critical temporal dependencies, and finally assemble beats into coherent long sequences using feature-guided matching. Our comprehensive evaluation demonstrates that the resulting synthetic ECGs maintain both beat-level morphological fidelity and clinically relevant inter-beat relationships. In arrhythmia classification tasks, our long-form synthetic ECGs significantly outperform end-to-end long-form ECG generation using the diffusion model, highlighting their potential for increasing utility for downstream applications. The approach enables generation of unprecedented multi-minute ECG sequences while preserving essential diagnostic characteristics.