TransDiffECG:通过基于变压器的扩散建模,通过语义控制的ECG合成
Yuxin Lin1, Jing Ma1, Suyu Dong2
1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China.
Journal of biomedical informatics
|October 29, 2025
概括
基于变压器的扩散模型TransDiffECG产生可控制的合成心电图 (ECG),以改善医疗保健数据. 这种先进的模型有效地增强了ECG数据稀缺性和不平衡问题.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 医疗信号处理 医疗信号处理
背景情况:
- 目前用于心电图 (ECG) 合成的生成模型缺乏细粒度控制.
- 这限制了它们在解决医疗保健数据稀缺和不平衡方面的有用性.
研究的目的:
- 开发一种生产多样化和语义可控的合成ECG的模型.
- 填补可解释的ECG数据生成中的关键缺口.
主要方法:
- 提出了一个新的基于变压器的扩散模型TransDiffECG.
- 集成的语义信息注入和全球时间建模,用于可控制的心电图合成.
- 用下游任务对单线程 (QTDB) 和多线程 (LUDB) ECG 数据集进行评估.
主要成果:
- 在LUDB数据集上,TransDiffECG在信号质量方面表现优于最先进的基线.
- 实现了优越的信号质量 (MMD: 3.21×10-2;皮尔森相关性: 0.6177).
- 用合成心电图增强数据改善了心房的分类 (AUROC:0.9451) 和细分任务.
结论:
- 通过结合临床解释性和生成灵活性,TransDiffECG推进了合成医疗信号生成.
- 能够生成语义上可控制和临床上有效的ECG.
- 扩大生成模型在医疗保健研究和实践中的应用潜力.
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