用手机摄像头测血压，精度达医疗标准。
Attractor-Vascular Coupling Theory: Formal Grounding and Empirical Validation for AAMI-Standard Cuffless Blood Pressure Estimation from Smartphone Photoplethysmography
- 基于心脏动力学几何特征建模，通过延迟嵌入提取脉搏信号复杂结构。
- 单点校准下收缩压误差仅2.05毫米汞柱，满足美国医疗标准。
- 无需心电图，仅用手机摄像头即可实现临床级无袖带血压监测。
本文提出吸引子-血管耦合理论（AVCT），构建数学框架证明心脏吸引子几何形态蕴含足够血压信息，可用于符合AAMI标准的无袖带血压估计，并通过基于光电容积脉搏波（PPG）的校准模型进行实证验证。该理论基于心脏稳定性理论，采用Takens延迟嵌入与吸引子形态提取实现可操作化。两个定理、一个命题和一个推论形式化证明了使用PPG吸引子特征进行血压估计的合理性，并预测特征重要性层级。基于轻量梯度提升机（LightGBM）的模型在46名受试者数据上（来自BIDMC ICU n=9 和 VitalDB 手术数据 n=37），经严格留一被试交叉验证（LOSO-CV）评估，共29,684个窗口。模型在收缩压（SBP）上实现平均绝对误差（MAE）2.05 mmHg，舒张压（DBP）MAE为1.67 mmHg，相关系数分别为 r=0.990 与 r=0.991，满足AAMI/IEEE SP10要求（误差低于5 mmHg）。个体平均误差中位数为1.87/1.54 mmHg，70%/76%的受试者个体达标。仅用九个智能手机吸引子特征的纯PPG消融实验，性能与需心电图+PPG的模型相差仅0.05 mmHg，表明仅用手机摄像头即可实现临床级血压追踪，且优于以往少传感器的泛化留一验证结果。四个AVCT预测均获定量证实，校准后误差较未校准降低91.5%（epsilon_cal = 0.915）。不同于事后可解释人工智能方法，AVCT预测特征满足可解释人工智能可信度（EAT）框架的架构忠实性准则，将血压估计建立在非线性动力系统理论基础上。
原文摘要 · Abstract (English)
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.
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