Large-scale real-world smartphone photoplethysmography datasets for vascular assessment

Jokić, Stevan (avtor), Jokić, Ivan (avtor), Gligorić, Nenad (avtor), Kartali, Aneta (avtor), Machidon, Octavian-Mihai (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: photoplethysmography, smartphone sensing, biosignal processing, machine learning, vascular aging, bioelectronics, mobile health, signal morphology
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: 18 str.
• Številčenje: Vol. 15, iss. 5, art. 988
• PID: 20.500.12556/RUL-181268
• UDK: 004.85:621.395.721.5:611.13/.14
• ISSN pri članku: 2079-9292
• DOI: 10.3390/electronics15050988
• COBISS.SI-ID: 273140739
• Datum objave v RUL: 30.03.2026

Gradivo je del revije

• Naslov: Electronics
• Skrajšan naslov: Electronics
• Založnik: MDPI
• ISSN: 2079-9292
• COBISS.SI-ID: 523068953

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: fotopletizmografija, pametni telefoni, zaznavanje s pametnimi telefoni, obdelava biosignalov, strojno učenje, žilno staranje, bioelektronika, mobilno zdravje