To the best of our knowledge, there is no annotated database of PPG signals recorded by smartphone publicly available. This article introduces Brno University of Technology Smartphone PPG Database (BUT PPG) which is an original database created by the cardiology team at the Department of Biomedical Engineering, Brno University of Technology, for the purpose of evaluating photoplethysmographic (PPG) signal quality and estimation of heart rate (HR). The data comprises 48 10-second recordings of PPGs and associated electrocardiographic (ECG) signals used for determination of reference HR. The data were collected from 12 subjects (6 female, 6 male) aged between 21 and 61. PPG data were collected by smartphone Xiaomi Mi9 with sampling frequency of 30 Hz. Reference ECG signals were recorded using a mobile ECG recorder (Bittium Faros 360) with a sampling frequency of 1,000 Hz. Each PPG signal includes annotation of quality created manually by biomedical experts and reference HR. PPG signal quality is indicated binary: 1 indicates good quality for HR estimation, 0 indicates signals where HR cannot be detected reliably, and thus, these signals are unsuitable for further analysis. As the only available database containing PPG signals recorded by smartphone, BUT PPG is a unique tool for the development of smart, user-friendly, cheap, on-the-spot, self-home-monitoring of heart rate with the potential of widespread using.

Department of Biomedical Engineering Faculty of Electrical Engineering and Communication Brno University of Technology Technicka 12 Brno 616 00 Czech Republic

Institute of Scientific Instruments The Czech Academy of Sciences Kralovopolska 147 Brno 612 64 Czech Republic

See more in PubMed

Orphanidou C. Signal Quality Assessment in Physiological Monitoring State of the Art and Practical Considerations. 1st. Cham, Switzerland: Springer; 2018. DOI

Naeini E. K., Azimi I., Rahmani A. M., Liljeberg P., Dutt N. A real-time PPG quality assessment approach for healthcare internet-of-things. Procedia Computer Science. 2019;151:551–558. doi: 10.1016/j.procs.2019.04.074. DOI

Nemcova A., Jordanova I., Varecka M., et al. Monitoring of heart rate, blood oxygen saturation, and blood pressure using a smartphone. Biomedical Signal Processing and Control. 2020;59 doi: 10.1016/j.bspc.2020.101928. DOI

Koenig N., Seeck A., Eckstein J., et al. Validation of a new heart rate measurement algorithm for fingertip recording of video signals with smartphones. Telemedicine and e-Health. 2016;22(8):631–636. doi: 10.1089/tmj.2015.0212. PubMed DOI

Siddiqui S. A., Zhang Y., Feng Z., Kos A. A pulse rate estimation algorithm using PPG and smartphone camera. Journal of Medical Systems. 2016;40(5) doi: 10.1007/s10916-016-0485-6. PubMed DOI

Tabei F., Zaman R., Foysal K. H., Kumar R., Kim Y., Chong J. W. A novel diversity method for smartphone camera-based heart rhythm signals in the presence of motion and noise artifacts. PLoS One. 2019;14(6):p. e0218248. doi: 10.1371/journal.pone.0218248. PubMed DOI PMC

Garmin. The heart rate sensor on my Garmin watch is not accurate. United States: Garmin Support Center; 2021. https://support.garmin.com/en-US/?faq=xQwjQjzUew4BF1GYcusE59.

Polar. Training with wrist-based heart rate. 2021. M430 User Manual https://support.polar.com/e_manuals/M430/Polar_M430_user_manual_English/Content/Training-with-wrist-based-heart-rate.htm.

Peng R., Zhou X., Lin W., Zhang Y. Extraction of heart rate variability from smartphone photoplethysmograms. Computational and Mathematical Methods in Medicine. 2015;2015:11. doi: 10.1155/2015/516826.516826 PubMed DOI PMC

Smital L., Marsanova L., Smisek R., Nemcova A., Vitek M. Robust QRS detection using combination of three independent methods. 2020 Computing in Cardiology; 2020; Rimini, Italy. pp. 1–4. DOI

International Electrotechnical Commission. Medical Electrical Equipment. Particular Requirements for the Basic Safety and Essential Performance of Electrocardiographic Monitoring Equipment (IEC 60601-2-27) 2015.

Nemcova A., Smisek R., Vargova E., Maršánová L., Vitek M., Smital L. Brno University of Technology Smartphone PPG Database (BUT PPG) PhysioNet. 2021 doi: 10.13026/7vy8-av04. PubMed DOI PMC

Goldberger A., Amaral L. A., Glass L., et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):e215–e220. doi: 10.1161/01.cir.101.23.e215. PubMed DOI

Smíšek R., Maršánová L., Němcová A., Vítek M., Kozumplík J., Nováková M. CSE database: extended annotations and new recommendations for ECG software testing. Medical & Biological Engineering & Computing. 2017;55(8):1473–1482. doi: 10.1007/s11517-016-1607-5. PubMed DOI

Nemcova A., Smisek R., Opravilová K., Vitek M., Smital L., Maršánová L. Brno University of Technology ECG Quality Database (BUT QDB) PhysioNet. 2020 doi: 10.13026/kah4-0w24. DOI

Brno University of Technology Smartphone PPG Database (BUT PPG): Annotated Dataset for PPG Quality Assessment and Heart Rate Estimation