Temporal point processes (TPPs) provide a natural mathematical framework for modeling heartbeats due to capturing underlying physiological inductive biases. In this work, we apply density-based neural TPPs to model heartbeat dynamics from 18 subjects. We adapt a goodness-of-fit framework from classical point process literature to Neural TPPs and use it to optimize hyperparameters, identify appropriate training sequence lengths to capture temporal dependencies, and demonstrate zero-shot predictive capability on heartbeat data.

翻译：时序点过程（TPPs）因其能够捕捉潜在的生理归纳偏置，为心跳建模提供了一个自然的数学框架。在本研究中，我们应用基于密度的神经TPPs对18名受试者的心跳动力学进行建模。我们借鉴经典点过程文献中的拟合优度框架，将其应用于神经TPPs，并利用该框架优化超参数、确定能够捕捉时序依赖性的合适训练序列长度，并在心跳数据上展示了零样本预测能力。