Event Fields: Learning Latent Event Structure for Waveform Foundation Models

TL;DR

This paper introduces a new waveform foundation model that captures latent event structures in physiological signals, improving robustness and performance over traditional sequence-based methods.

Contribution

It presents a self-supervised learning framework that models physiological signals as interacting events, extending to multimodal data and outperforming sequence-based baselines.

Findings

Improves arrhythmia classification accuracy

Enhances waveform retrieval robustness

Increases label efficiency in physiological modeling

Abstract

We propose a new class of waveform foundation models that departs from conventional sequence based representations by modeling physiological time series as realizations of latent event processes. Rather than treating signals as collections of local tokens or patches, our approach assumes that clinically meaningful structure arises from temporally extended, interacting events whose boundaries and dynamics are not directly observed. To capture this structure, we introduce a self supervised learning framework that enforces consistency across stochastic segmentations and time frequency projections of the same waveform, encouraging representations that are invariant to signal level perturbations while preserving event level organization. The resulting model combines a segmentation aware encoder with a latent interaction operator that captures dependencies among inferred events, and naturally extends to multimodal settings by aligning modalities through shared event representations. Across a range of physiological benchmarks, including arrhythmia classification, hemodynamic prediction, and waveform retrieval, the proposed method improves performance, robustness, and label efficiency relative to strong sequence based baselines. These results suggest that shifting from signal centric to event centric representations provides a more appropriate inductive bias for modeling physiological dynamics and offers a complementary path to scaling foundation models in healthcare.