Smartphone PPG datasets achieved up to 90.08% classification accuracy and under 10 years age prediction error for vascular assessment using machine learning models.
Can machine learning models accurately classify vascular morphology and predict chronological age using large-scale, real-world smartphone camera-based photoplethysmography (PPG) datasets?
The release of large-scale, curated smartphone PPG datasets provides a robust foundation for developing and validating machine learning models for non-invasive vascular assessment and aging research.
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The development of reliable smartphone-based methods for vascular assessment is limited by the scarcity of large-scale, high-quality, real-world photoplethysmography (PPG) datasets. This work introduces two openly reusable smartphone camera-based PPG datasets curated from over one million unconstrained recordings, designed to support vascular morphology analysis and vascular aging research. The first dataset comprises approximately 5000 high-fidelity PPG heartbeat templates labeled into four morphological classes based on dicrotic notch characteristics, enabling assessment of arterial waveform structure beyond chronological age. The second dataset contains about 10,000 demographically balanced PPG samples curated for chronological age regression using rigorous subject-level balancing and correlation-based quality control. A standardized processing pipeline is presented, including beat alignment, ensemble averaging, and objective signal acceptance criteria to ensure morphological stability. To validate dataset utility, multiple machine learning models were benchmarked using raw signals, second derivatives, and compact Gaussian representations, achieving classification accuracy up to 90.08% and age prediction error below 10 years. By prioritizing real-world data quality, transparency, and reuse, this work provides a robust foundation for scalable, interpretable, and reproducible research in smartphone-based vascular assessment.
Jokić et al. (Fri,) reported a other. Smartphone PPG datasets achieved up to 90.08% classification accuracy and under 10 years age prediction error for vascular assessment using machine learning models.