用分层生成法合成多分钟真实感心电图，保持波形和心跳间关系。
Generating Realistic Multi-Beat ECG Signals
- 分三步生成：先用扩散模型造单个心跳，再建心跳间特征，最后按特征匹配拼成长序列。
- 合成的心电图在心律失常分类任务中显著优于直接生成长序列的方法。
- 适合医疗数据增强、教学模拟等需长时间真实心电图的场景。
生成合成心电图在医疗领域有广泛应用，如教学、情景模拟和趋势预测。尽管近期扩散模型在生成短段心电图方面表现优异，但在生成临床应用所需的长序列时仍面临挑战。本文提出一种新型三层合成框架，用于生成逼真的长时序心电图信号。首先使用扩散模型生成高保真单个心跳波形，然后合成保留关键时间依赖性的心跳间特征，最后通过特征引导匹配将心跳组合成连贯的长序列。全面评估表明，生成的合成心电图在波形细节和临床相关的节律关系上均表现良好。在心律失常分类任务中，该方法生成的长序列显著优于端到端的长序列扩散生成模型，展现出更强下游应用潜力。该方法可生成前所未有的多分钟级心电图序列，同时保留关键诊断特征。
原文摘要 · Abstract (English)
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.
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