In the realm of artificial intelligence, the generation of realistic training data for supervised learning tasks presents a significant challenge. This is particularly true in the synthesis of electrocardiograms (ECGs), where the objective is to develop a synthetic 12-lead ECG model. The primary complexity of this task stems from accurately modeling the intricate biological and physiological interactions among different ECG leads. Although mathematical process simulators have shed light on these dynamics, effectively incorporating this understanding into generative models is not straightforward. In this work, we introduce an innovative method that employs ordinary differential equations (ODEs) to enhance the fidelity of generating 12-lead ECG data. This approach integrates a system of ODEs that represent cardiac dynamics directly into the generative model's optimization process, allowing for the production of biologically plausible ECG training data that authentically reflects real-world variability and inter-lead dependencies. We conducted an empirical analysis of thousands of ECGs and found that incorporating cardiac simulation insights into the data generation process significantly improves the accuracy of heart abnormality classifiers trained on this synthetic 12-lead ECG data.

翻译：在人工智能领域，为监督学习任务生成逼真的训练数据是一项重大挑战。这在心电图（ECG）的合成中尤为突出，其目标是开发一种合成的12导联ECG模型。该任务的主要复杂性源于如何准确建模不同ECG导联间复杂的生物学和生理学相互作用。尽管数学过程模拟器已揭示了这些动态特性，但将这种理解有效整合到生成模型中并非易事。在本研究中，我们提出了一种创新方法，利用常微分方程（ODEs）来增强生成12导联ECG数据的保真度。该方法将代表心脏动态的ODE系统直接整合到生成模型的优化过程中，从而能够产生生物学上合理、真实反映现实世界变异性和导联间依赖关系的ECG训练数据。通过对数千份ECG进行实证分析，我们发现将心脏模拟的洞见融入数据生成过程，能显著提升基于此合成12导联ECG数据训练的心脏异常分类器的准确性。