Context-Augmented, Session-Calibrated Blood Pressure Tracking from Photoplethysmography

Continuous cuffless blood pressure (BP) monitoring is essential for connected health systems and wearable devices, enabling early detection, longitudinal tracking, and personalized management of cardiovascular disease. Many prior approaches attempt to estimate BP indirectly by reconstructing electrocardiography (ECG) from photoplethysmography (PPG), assuming ECG provides a stronger physiological link to BP. However, ECG sensing is less accessible in wearable settings and may introduce unnecessary complexity. In this work, we first perform a large-scale physiological correlation analysis on the MIMIC-III waveform database, revealing that PPG exhibits substantially stronger coupling with arterial blood pressure (ABP) ($|r|=0.247$, $p<0.001$) than ECG does ($r=0.018$, $p=0.187$), challenging the assumption that ECG provides a superior intermediate representation. Motivated by this insight, we conduct a systematic comparison between direct PPG-to-BP prediction and ECG-mediated pipelines using multiple state-of-the-art deep learning models. Across 1.74M segments from 3,127 patients, direct PPG-to-BP prediction achieves British Hypertension Society Grade A performance ($\mathrm{MAE}_{\mathrm{SBP}} = 4.82 mmHg$, $\mathrm{MAE}_{\mathrm{DBP}} = 4.31 mmHg$), outperforming all ECG-mediated approaches, which achieve only Grade B accuracy. Our findings suggest that accurate continuous BP monitoring can be achieved directly from wearable PPG signals, enabling simpler, more efficient pipelines for real-world connected health systems.

We present Attractor-Vascular Coupling Theory (AVCT), a theory-guided framework for deriving interpretable blood pressure (BP) predictors from photoplethysmography (PPG) attractor geometry. AVCT connects vascular dynamics, PPG observation, delay-coordinate reconstruction, and attractor descriptors within a unified framework for formulating testable hypotheses, without presupposing the physiological sufficiency of a particular low-dimensional feature set. We evaluate a LightGBM-based estimator using leave-one-subject-out cross-validation (LOSO-CV) across two complementary experimental settings. Experiment 1 comprises 46 ICU/surgical subjects from BIDMC and VitalDB, with ECG, PPG, and repeated reference BP measurements. The calibrated full model achieves systolic/diastolic BP (SBP/DBP) mean absolute errors (MAEs) of 2.05/1.67 mmHg, compared with 2.02/1.63 mmHg for the PPG-attractor-only model. Experiment 2 includes 217 ambulatory subjects from the PPG-BP dataset (651 PPG windows; one cuff-based BP reference per subject) and assesses cross-subject calibrated BP reconstruction rather than within-subject BP tracking. The PPG-attractor model achieves SBP/DBP MAEs of 1.98/0.96 mmHg. Across both cohorts, incorporating ECG-derived features provides no practically meaningful improvement in MAE. Calibration substantially reduces between-subject offset error, while morphological and dynamical descriptors contribute complementary predictive information. Although the reported numerical errors fall within commonly cited thresholds associated with AAMI/ISO and BHS criteria, these findings do not establish compliance with device-validation standards. Collectively, the results support AVCT as a mechanistically motivated representation framework for interpretable PPG-based BP estimation and provide a foundation for prospective validation using repeated reference BP measurements and smartphone-acquired PPG.

Continuous cuffless blood pressure (BP) monitoring remains challenging due to motion artifacts, physiological variability, and the limited robustness of conventional pulse transit time (PTT) models under dynamic conditions. Many prior approaches rely on multi-second windows to stabilize estimation, an assumption that is frequently violated during real-world monitoring with intermittent signal corruption. Here, we show that discriminative BP-related information is preserved at the single-beat level and present a lightweight multi-modal wearable framework for continuous BP estimation. The system integrates synchronized chest electrocardiography (ECG) and ear-clip reflectance photoplethysmography, each co-located with a 6-axis inertial measurement unit to provide motion context. We introduce a hybrid learning architecture in which a one-dimensional convolutional neural network extracts a 64-dimensional embedding from individual PPG beats and fuses it with 30 physiology-grounded features, including PTT statistics and heart rate variability, followed by LightGBM regression. The method was evaluated using a multi-phase stress protocol ($n=10$) and the PulseDB public dataset with subject-disjoint validation. Across 30 independent runs, the model achieved mean absolute errors of $4.02 \pm 0.21$~mmHg for systolic BP and $1.79 \pm 0.05$~mmHg for diastolic BP, corresponding to a 28.2% reduction in combined MAE relative to baseline models. By enabling beat-wise estimation without long temporal context, this framework supports computationally efficient cuffless BP monitoring suitable for wearable deployment under practical resource constraints. The source code for this work is available at https://github.com/SYMBIOX-Lab/BP-wireless.