Generating Realistic Multi-Beat ECG Signals

2025-05-19

AI Summary

Existing ECG generation methods struggle to model long-range temporal dependencies, limiting synthetic signals to single beats or a few seconds—insufficient for clinical applications requiring multi-minute continuous recordings. To address this, we propose a three-tier diffusion-based synthesis framework: (1) a diffusion model generating high-fidelity single-beat waveforms; (2) a cross-beat temporal feature modeling module to capture rhythm dynamics; and (3) a feature-guided stitching mechanism that integrates multi-scale physiological constraints for coherent long-sequence synthesis. This work achieves the first end-to-end generation of diagnostic-grade, multi-minute (≥5 min) ECGs. Experiments demonstrate that the synthesized signals significantly outperform baselines in morphological fidelity and rhythm consistency. In downstream arrhythmia classification, models trained on our synthetic data achieve a 4.2% accuracy improvement, validating both clinical utility and methodological advancement.

Technology Category

Natural Language Processing: Code Generation / Program Synthesis from Natural LanguageMachine Learning: Deep Generative Models & AutoencodersComputer Vision: Diffusion Models for Vision

Application Category

Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applicationsGraph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Generating realistic long-form ECG signals for clinical applications
• Preserving beat-level and inter-beat features in synthetic ECGs
• Improving arrhythmia classification with synthetic multi-minute ECG sequences

Innovation

• Three-layer framework for long ECG synthesis
• Diffusion model for high-fidelity single beats
• Feature-guided matching for coherent sequences