PhysioJEPA: Joint Embedding Representations of Physiological Signals for Real Time Risk Estimation in the Intensive Care Unit

Benjamin Fox, Dung T. Hoang, Joy Jiang, Pushkala Jayaraman, ANKIT PAREKH, Girish N Nadkarni, Ankit Sakhuja

Keywords: self-supervision, transformer, JEPA, physiological signals, multi-modal, multichannel, electrocardiography, photoplethysmography, critical care, hypotension, shock

TL;DR: PhysioJEPA learns representations of bedside monitoring physiological signal data via a joint embedding predictive architecture (JEPA) learning procedure and can be finetuned to estimate risk for hypotension and shock in critical care settings.

Track: Proceedings

Abstract: Self-supervised learning of multi-modal, high-frequency physiological signals is largely unexplored, despite its potential for critical care applications. We present PhysioJEPA, a Joint Embedding Predictive Architecture (JEPA) designed for multi-modal physiological signals from critical care bedside monitoring devices. PhysioJEPA learns representations from 30-minute segments of physiological signals from three channels: arterial blood pressure, electrocardiography lead II, and photoplethysmography. Trained on over 10.7 million minutes of data from 4,282 intensive care unit stays (N=2,631 patients) in the Medical Information Mart for Intensive Care-III (MIMIC-III) Waveform Database, the learned, frozen representations of PhysioJEPA can be used to estimate 5-minute risk of hypotension (AUROC = 0.83 [Confidence Interval or CI 0.83-0.84]) and shock index (AUROC = 0.95 [0.95-0.96]), with comparable performance to a self-supervised Patch Time Series Transformer framework (AUROC = 0.87 [0.86-0.87] and 0.96 [0.96-0.96]), better performance compared to another JEPA physiological signal model, ECG-JEPA (AUROC = 0.73 [0.72-74] and 0.92 [0.92-0.93]), and better performance compared to a supervised convolutional model (AUROC = 0.78 [0.78-0.78] and 0.95 [0.95-0.95]). Notably, it can generalize to an independent healthcare system (AUROC = 0.78 [0.78-0.78] and 0.92 [0.92-0.93]) better than all comparison models. These results suggest that self-supervised JEPA representation learning is a promising approach for multi-modal bedside monitoring signal data.

General Area: Models and Methods

Specific Subject Areas: Representation Learning, Foundation Models, Time Series, Supervised Learning

Data And Code Availability: Yes

Ethics Board Approval: Yes

Entered Conflicts: I confirm the above

Anonymity: I confirm the above

Code URL: https://github.com/benmfox/PhysioJEPA

Submission Number: 202