提出新型心率稳定指数，解决可穿戴设备评估中的方法缺陷。
A Nonlinear Complexity Index for Wearable PPG Cardiovascular Stability: Multiscale Validation, Systematic Evaluation Correction, and Bayesian Parameter Optimization
- 基于心脏稳定性理论构建非线性指标，避免人为参数干扰。
- 修正三大评估误差后，准确率提升至AUC 0.720，tachypnea筛查特异度达96.6%。
- 提供可复现的评估基准，适合临床可穿戴心功能监测研究者使用。
从可穿戴光电容积脉搏波（PPG）中估计心血管稳定性需要一个严谨的非线性框架，但现有方法在参数选择和评估流程上仍存在严重缺陷，导致性能被高估。本文提出一种基于心脏稳定性理论的稳定性约束心血管稳定指数（SCSI），在四个异构的PPG数据集共176,742个片段上，于三个时间尺度进行多尺度验证。跨数据集分析显示，Kruskal-Wallis效应量η² = 0.351（p < 0.001），跨尺度一致性κ > 0.97，且在53例重症监护记录中与呼吸频率显著相关（Spearman r = 0.346，p = 0.011）。识别出三项评估偏差：段级交叉验证泄漏、测试集归一化泄漏、合并AUC权重过重，这些使启发式AUC从真实基线0.573虚增至0.752。修正后结合贝叶斯优化15个联合参数，得到交叉验证AUC为0.720。在18个保留记录上，合并AUC为0.757（95% CI: 0.686–0.828），tachypnea筛查负预测值达0.966；同时公开每记录AUC为0.497 ± 0.207以保证透明。外部验证在42例择期手术记录中获得AUC 0.621，证实跨人群泛化能力。消融分析表明非线性复杂度模块是主导成分，提出仅含三组件的稀疏架构作为最小可部署配置。修正后的评估协议为未来可穿戴心血管稳定指数研究提供可复现基准。
原文摘要 · Abstract (English)
Cardiovascular stability estimation from wearable photoplethysmography (PPG) requires a principled nonlinear framework, yet major gaps persist in heuristic parameter selection and evaluation protocols that inflate reported performance. We introduce a Stability-Constrained Cardiovascular Stability Index (SCSI) grounded in Cardiac Stability Theory and validate it across 176,742 segments from four heterogeneous PPG datasets at three temporal scales. Cross-dataset analysis demonstrates a large Kruskal-Wallis effect size (eta2 = 0.351, p < 0.001), strong cross-scale consistency (kappa > 0.97), and significant correlation with respiratory rate across 53 ICU records (Spearman r = 0.346, p = 0.011). We identify three evaluation artifacts that inflate heuristic AUC from a true baseline of 0.573 to 0.752: segment-level cross-validation leakage, test-set normalization leakage, and pooled-AUC overweighting that conceals per-patient failure. Correcting these artifacts and applying Bayesian optimization over 15 joint parameters yields SCSI with cross-validation AUC of 0.720. On 18 held-out records, SCSI achieves pooled AUC of 0.757 (95% CI: 0.686-0.828) and negative predictive value of 0.966 for tachypnea screening, while per-record AUC of 0.497 +/- 0.207 is disclosed for transparency. External validation on 42 elective-surgery records yields AUC of 0.621, confirming cross-population generalization. Ablation analysis identifies the nonlinear complexity module as the dominant component. A sparse three-component architecture is proposed as the minimal deployable configuration. The corrected protocol provides a reproducible benchmark for future wearable cardiovascular stability indices.
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