Individual-level Public-Private Decoupling for Continuous Blood Pressure Waveform Prediction

Existing methods for continuous arterial blood pressure (ABP) waveform prediction from photoplethysmography (PPG) do not adequately distinguish inter-individual differences in blood pressure. To address this limitation, we propose Individual-level Public-Private Decoupling (IPPD), a network that decomposes PPG representations into statistically decorrelated Public and Private features and expresses the ABP prediction as the sum of a public waveform and an individual residual. To better distinguish within-individual consistency from between-individual variation, we introduce dual-view contrastive learning into the Private branch. On VitalDB-360, IPPD achieves the best individual-level prediction performance. Without target-case calibration or fine-tuning, it attains a waveform RMSE of 11.65 mmHg and a waveform MAE of 9.91 mmHg across 30 held-out test cases, outperforming existing blood-pressure prediction baselines. The code will be released.