Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning

2026-05-01

AI Summary

Existing self-supervised approaches treat multi-site physiological signals as interchangeable views, overlooking their intrinsic temporal dynamics and physiological conduction relationships. This work proposes xMAE, a novel framework that, for the first time, incorporates directional temporal structure into multimodal self-supervised pretraining. By leveraging a masked cross-modal reconstruction mechanism, xMAE explicitly models the physiological constraints and temporal delays between signals such as ECG and PPG to capture realistic conduction processes. Evaluated across 19 downstream tasks—including cardiovascular prediction, anomaly detection, and sleep staging—the method outperforms both unimodal and multimodal baselines on 15 tasks and demonstrates strong generalization across diverse devices and acquisition conditions.

Abstract

Biosignals acquired from different locations on the body often provide temporally ordered views of the same underlying physiological process. However, most existing self supervised learning methods treat these signals as interchangeable views, overlooking the directional temporal dynamics that link them. A canonical example is the relationship between electrocardiography (ECG), which captures the electrical activation initiating each heartbeat, and photoplethysmography (PPG), which records the resulting peripheral pulse delayed by vascular dynamics. To capture this structured relationship, we introduce xMAE, a biosignal pretraining framework that leverages masked cross modal reconstruction across temporally ordered biosignals as a training time constraint to encourage physiologically meaningful timing structure in the learned representations. We show that pretraining with xMAE yields representations that outperform both unimodal and multimodal baselines on 15 of 19 downstream tasks, including cardiovascular outcome prediction, abnormal laboratory test detection, sleep staging, and demographic inference, while generalizing across devices, body locations, and acquisition settings. Further analysis suggests that the ECG PPG timing structure is reflected in the learned PPG representations. More broadly, xMAE demonstrates the effectiveness of incorporating temporal structure into multimodal pretraining when signals observe different stages of a shared underlying process. Code is available at https://github.com/hzhou3/xMAE.

Problem

• biosignal representation learning
• temporal dynamics
• cross-modal reconstruction
• physiological process
• self-supervised learning

Innovation

• masked cross-modal reconstruction
• physiology-aware representation learning
• temporal structure modeling
• biosignal pretraining
• ECG-PPG timing dynamics