Deriving Health Metrics from the Photoplethysmogram: Benchmarks and Insights from MIMIC-III-Ext-PPG

TL;DR

This paper establishes a comprehensive benchmark for PPG-based clinical prediction tasks using the MIMIC-III-Ext-PPG dataset, demonstrating strong performance and revealing subgroup differences in arrhythmia detection and physiological parameter estimation.

Contribution

It introduces the first integrated benchmark for multi-task PPG-based clinical prediction, covering arrhythmia detection and physiological parameter regression, with extensive subgroup analysis.

Findings

High accuracy in AF detection (AUROC = 0.96) and generalizability across datasets (AUROC = 0.97)

Effective estimation of RR, HR, and BP with low MAE values

Performance varies across subgroups, reflecting waveform differences rather than bias.

Abstract

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.