Robust learning framework for a scalable remote monitoring of autonomic dysreflexia: use-case in spinal cord injury

Abstract

Abstract
The autonomous nervous system (ANS) response in neurological disorders is a direct modifiable risk factor for cardiovascular health, however, difficulty in remote monitoring and objective assessment has made it underrepresented in preventive healthcare. Particularly, autonomic dysreflexia (AD) is a dangerous hypertensive emergency, potentially life-threatening in people with spinal cord injury (SCI), yet detection outside clinical settings remains reactive and episodic. This study presents an interpretable and scalable framework for creating a digital biomarker from multimodal wearables in data scarcity through vital sign attribution analysis in multiple body locations, evaluated with 27 subjects undergoing clinical examination with objective blood pressure measurements. Our framework learns from diverse biosignals—lectrocardiography (ECG), photoplethysmography (PPG), bioimpedance, skin temperature, heart rate, and respiratory rate—proving robustness to sensor failure and a pathway to remote monitoring. This study identified heart rate and ECG as dominant predictors, with PPG providing complementary value, under simulated single-modality failure or noisy channels. This work advances a feasible path to equitable, remote monitoring of the ANS response, reducing dependence on intermittent BP measurements and enabling earlier intervention.