Robust Cuffless Blood Pressure Estimation via PPG-Conditioned Diffusion and Contrastive Transformer Fusion
Hypertension is the leading preventable risk factor for cardiovascular disease, yet continuous blood pressure (BP) monitoring is still largely inaccessible outside of clinical settings. Photoplethysmography (PPG), collected via consumer wearables, offers a promising non-invasive method for continuous cuffless BP monitoring, but PPG signals lack the physiological information necessary for reliable BP estimation. Conversely, specific electrocardiogram (ECG)-level features, namely QRS timing and pulse transit time, show a direct correlation to BP. Unfortunately, most wearables lack the dedicated electrodes needed for accurate ECG measurement. Building on Ji and Zhou's (SenSys '24) demonstration that ECG can be reconstructed from PPG via a diffusion model, we address a gap left open by existing work: real-world wearable deployments introduce signal degradation, such as motion artifacts, sensor noise, and intermittent data loss that state-of-the-art frameworks do not account for, leaving the clinical reliability of diffusion-reconstructed ECG-based BP estimators unestablished. We propose replacing the BiLSTM, the state-of-the-art estimator used by Ji and Zhou, with a Transformer, whose self-attention captures long-range crossmodal dependencies between the generated ECG and PPG, and we introduce supervised contrastive learning to make the learned representations invariant to noise, missing data, and subject variation. On MIMIC-II and MIMIC-BP benchmarks, our Diffusion+Transformer framework achieves mean absolute errors of 4.50 mmHg (SBP) and 2.39 mmHg (DBP) on clean signals, improving over the BiLSTM baseline by 0.27 mmHg (SBP) and 0.28 mmHg (DBP), with substantially better robustness under temporal data removal across both datasets. Contrastive learning (SupCon) reduces error under additive noise conditions: at 50% noise, adding SupCon reduces SBP MAE from 25.65 to 11.94 mmHg and DBP MAE from 20.94 to 5.86 mmHg.