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bioRxiv · 10.64898/2026.08.12.744524

Lognormal Neural Point Process Models for Interpretable Heartbeat Dynamics

Kumar, B. R.·Ramsundar, B.·Subramanian, S.
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Abstract

Neural temporal point processes (NTPPs) are powerful tools for modeling sequences of timestamped events with statistical temporal structure. Density-based NTPPs, in particular, are an interesting opportunity to merge the universal function approximation capability of neural networks with a defined statistical model in a way that has many potential applications. We demonstrate one such application to heartbeat dynamics, a physiologic point process. We specifically apply a lognormal mixture NTPP to compute instantaneous estimates of the mean and standard deviation of beat-to-beat intervals. We compare our results to the state of art (Barbieri et al.) point process model for heartbeat dynamics, which uses a more physiologically rigorous inverse Gaussian model. We find that the NTPP model maintains reasonable accuracy while improving upon robustness to noise.

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Kumar, B. R., Ramsundar, B., Subramanian, S.. 2026-08-20. Lognormal Neural Point Process Models for Interpretable Heartbeat Dynamics. https://doi.org/10.64898/2026.08.12.744524

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