Personalized Calibration and Hybrid Feature Fusion for Continuous Blood Pressure Estimation Using PPG and ECG Signals

Continuous and noninvasive blood pressure (BP) monitoring is important for hypertension screening and long-term cardiovascular management. Continuous BP estimation using photoplethysmography (PPG) and electrocardiography (ECG) has become a promising alternative to conventional cuff-based measurements. However, handcrafted feature-based methods are sensitive to fiducial point detection, while end-to-end deep models often require large labeled datasets and may ignore subject-specific BP baselines. This study proposes a personalized calibration and hybrid feature fusion framework for continuous BP estimation from ECG and PPG signals. The framework integrates physiologically interpretable handcrafted features, self-supervised waveform representations, and subject-specific prior BP information to predict systolic and diastolic BP. The handcrafted branch extracts pulse transit time, heart rate variability, and PPG morphological descriptors, whereas a masked reconstruction-based self-supervised encoder learns latent waveform embeddings without BP labels. Personalized calibration incorporates base BP from the earliest calibration file through an alpha-weighted strategy, and the fused representation is fed into XGBoost regressors. The framework was evaluated on a private dataset and the BP-UCI dataset, achieving SBP/DBP MAEs of 7.25/3.98 mmHg and 7.85/4.02 mmHg, respectively. Comparative and ablation results indicate improved baseline performance in the current experimental setting, suggesting the methodological feasibility of the proposed framework for small-sample continuous BP estimation. Further validation with larger and more diverse cohorts is still required.