PPGNet: Deep Multiple Instance Learning for Automated Hypertension Detection in OSA Patients Using PPG Signals
TL;DR
PPGNet is presented, a deep learning framework for diagnostic detection of established HTN in OSA patients using photoplethysmography signals extracted from standard overnight PSG recordings, enabling earlier HTN identification in this highrisk population without additional instrumentation.
Abstract
Abstract Systemic hypertension (HTN) is a prevalent cardiovascular comorbidity in patients with obstructive sleep apnea (OSA), yet no scalable framework exists for HTN detection using routinely collected polysomnography (PSG) signals. We present PPGNet, a deep learning framework for diagnostic detection of established HTN in OSA patients using photoplethysmography (PPG) signals extracted from standard overnight PSG recordings. The framework employs a Multiple Instance Learning (MIL) approach to model long-term hemodynamic and autonomic patterns across full-night recordings, aggregating 30-second instances into 30-minute bags enriched with sleep stage and apnea context. Evaluated under Leave- One-Patient-Out (LOPO) cross-validation on a clinically annotated cohort of 33 OSA patients, PPGNet achieved AUROC of 86.72%, AUPRC of 88.01%, Accuracy of 79.03%, Sensitivity of 83.33%, and MCC of 0.5842. Attention-based interpretability analysis confirms that the model captures clinically meaningful vascular signatures, including arterial stiffness markers and differential vasomotor responses to apnea events. This work establishes a proof-of-concept for cardiovascular risk stratification as a secondary outcome of routine sleep studies, enabling earlier HTN identification in this highrisk population without additional instrumentation.