Density-based Neural Temporal Point Processes for Heartbeat Dynamics

Sandya Subramanian; Bharath Ramsundar

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arXiv · 2511.22096

Density-based Neural Temporal Point Processes for Heartbeat Dynamics

Sandya Subramanian·Bharath Ramsundar

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Abstract

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.

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Sandya Subramanian, Bharath Ramsundar. 2025-11-27. Density-based Neural Temporal Point Processes for Heartbeat Dynamics. https://arxiv.org/abs/2511.22096

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