首个基于MIMIC-III-Ext-PPG的多任务脉搏波临床预测基准，可同时评估心律与生理参数。
Deriving Health Metrics from the Photoplethysmogram: Benchmarks and Insights from MIMIC-III-Ext-PPG
- 构建统一框架，用深度学习模型同时处理心律分类与呼吸率、心率、血压回归任务。
- 对房颤检测达AUROC 0.96，呼吸率/心率/血压误差分别为2.97/1.13/16.13/8.70 bpm/mmHg。
- 首次系统分析不同血压、心率及人群亚组的性能差异，揭示波形特征影响而非模型偏差。
脉搏波（PPG）是临床预测中最广泛采集的生物信号之一，但现有基于PPG的算法通常在小规模、质量不确定的数据集上训练，难以进行有意义的比较。本文基于 ame~数据集，建立了全面的PPG临床预测基准，覆盖多类心律分类及生理参数回归任务，包括呼吸率（RR）、心率（HR）和血压（BP）。特别地，首次对房颤（AF）和房扑（AFLT）以外的常见心律失常进行了全面评估，并按血压、心率及人口统计学亚组分层分析性能表现。采用成熟的深度学习架构，实现房颤检测的高精度（AUROC = 0.96），以及精准的生理参数估计（RR MAE: 2.97 bpm；HR MAE: 1.13 bpm；SBP/DBP MAE: 16.13/8.70 mmHg）。跨数据集验证显示房颤检测具有优异泛化能力（AUROC = 0.97），而临床亚组分析表明不同子群体间性能存在显著差异，这些差异更可能源于人群特异性波形特征，而非模型系统性偏差。该框架建立了首个多任务PPG临床预测集成基准，证明了PPG信号支持多重同步监测的潜力，并为未来算法开发提供了关键基线。
原文摘要 · Abstract (English)
Photoplethysmography (PPG) is one of the most widely captured biosignals for clinical prediction tasks, yet PPG-based algorithms are typically trained on small-scale datasets of uncertain quality, which hinders meaningful algorithm comparisons. We present a comprehensive benchmark for PPG-based clinical prediction using the \dbname~dataset, establishing baselines across the full spectrum of clinically relevant applications: multi-class heart rhythm classification, and regression of physiological parameters including respiratory rate (RR), heart rate (HR), and blood pressure (BP). Most notably, we provide the first comprehensive assessment of PPG for general arrhythmia detection beyond atrial fibrillation (AF) and atrial flutter (AFLT), with performance stratified by BP, HR, and demographic subgroups. Using established deep learning architectures, we achieved strong performance for AF detection (AUROC = 0.96) and accurate physiological parameter estimation (RR MAE: 2.97 bpm; HR MAE: 1.13 bpm; SBP/DBP MAE: 16.13/8.70 mmHg). Cross-dataset validation demonstrates excellent generalizability for AF detection (AUROC = 0.97), while clinical subgroup analysis reveals marked performance differences across subgroups by BP, HR, and demographic strata. These variations appear to reflect population-specific waveform differences rather than systematic bias in model behavior. This framework establishes the first integrated benchmark for multi-task PPG-based clinical prediction, demonstrating that PPG signals can effectively support multiple simultaneous monitoring tasks and providing essential baselines for future algorithm development.
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