Attractor-Vascular Coupling Theory：面向 AAMI 标准手机 PPG 无袖带血压估计的理论构建与实证验证
Attractor-Vascular Coupling Theory: Formal Grounding and Empirical Validation for AAMI-Standard Cuffless Blood Pressure Estimation from Smartphone Photoplethysmography
AI 导读
论文提出 Attractor-Vascular Coupling Theory（AVCT），从 PPG 吸引子几何推导可解释的血压预测因子。
正文 · 原文
Abstract: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.
| Subjects: | Medical Physics (physics.med-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.10871 [physics.med-ph] |
| (or arXiv:2605.10871v3 [physics.med-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2605.10871 arXiv-issued DOI via DataCite |
Submission history
From: Timothy Oladunni [view email]
[v1]
Mon, 11 May 2026 17:24:42 UTC (214 KB)
[v2]
Sun, 17 May 2026 18:50:48 UTC (214 KB)
[v3]
Fri, 23 Oct 2026 00:06:50 UTC (111 KB)
来源：arXiv cs.AI · arxiv.org