🤖 AI Summary
This study proposes a novel method for clinical-grade, cuffless blood pressure estimation using only smartphone-based photoplethysmography (PPG). By introducing the Attractor–Vascular Coupling Theory (AVCT), the work establishes, for the first time, a rigorous nonlinear dynamical systems framework linking geometric features of PPG attractors to blood pressure, while predicting a hierarchy of feature importance to enhance model interpretability. The approach integrates Takens’ delay embedding, attractor morphology descriptors, a cardiac stability index (CSI), pulse transit time (PTT), and LightGBM regression. Evaluated on 46 subjects, it achieves mean absolute errors of 2.05/1.67 mmHg for systolic/diastolic blood pressure (correlation coefficients: 0.990/0.991), with 70%/76% of individuals meeting AAMI standards, demonstrating clinical accuracy with only a single-point calibration.
📝 Abstract
This work proposes Attractor-Vascular Coupling Theory (AVCT), a mathematical framework showing that cardiac attractor geometry encodes blood pressure (BP) information sufficient for AAMI-standard estimation, and validates the theory through a calibrated cuffless BP model using photoplethysmography (PPG). AVCT is grounded in Cardiac Stability Theory and operationalized using Takens delay embedding and attractor morphology extraction. Two theorems, one proposition, and one corollary formally justify the use of PPG attractor features for BP estimation and predict the feature-importance hierarchy. A LightGBM model trained on pulse transit time (PTT) and Cardiac Stability Index (CSI) attractor features under single-point calibration was evaluated using strict leave-one-subject-out cross-validation (LOSO-CV) on 46 subjects from BIDMC ICU (n = 9) and VitalDB surgical data (n = 37), comprising 29,684 windows. The model achieved systolic BP (SBP) mean absolute error (MAE) of 2.05 mmHg and diastolic BP (DBP) MAE of 1.67 mmHg, with correlations r = 0.990 and r = 0.991, satisfying the AAMI/IEEE SP10 requirement of MAE below 5 mmHg. Median per-subject MAE was 1.87/1.54 mmHg, and 70%/76% of subjects individually satisfied AAMI criteria. A PPG-only ablation using nine smartphone attractor features matched the ECG+PPG model within 0.05 mmHg, demonstrating that clinical-grade BP tracking is achievable using only a smartphone camera while surpassing prior generalized LOSO-CV results using fewer sensors. All four AVCT predictions were quantitatively confirmed, with 91.5% error reduction from uncalibrated to calibrated estimation (epsilon_cal = 0.915). Unlike post-hoc explainable AI methods, AVCT predicts features satisfying the architectural faithfulness criterion of the Explainable-AI Trustworthiness (EAT) framework and grounding BP estimation in nonlinear dynamical systems theory.