Database Open Access
Brno University of Technology Smartphone PPG Database (BUT PPG)
Published: Jan. 18, 2021. Version: 1.0.0
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Nemcova, A., Smisek, R., Vargova, E., Maršánová, L., Vitek, M., & Smital, L. (2021). Brno University of Technology Smartphone PPG Database (BUT PPG) (version 1.0.0). PhysioNet. https://doi.org/10.13026/7vy8-av04.
Nemcova A, Vargova E, Smisek R, Marsanova L, Smital L, Vitek M. Brno University of Technology Smartphone PPG Database (BUT PPG): Annotated Dataset for PPG Quality Assessment and Heart Rate Estimation. BioMed Research International. 2021 Sep 7;2021. https://doi.org/10.1155/2021/3453007.
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Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
Brno University of Technology Smartphone PPG Database (BUT PPG) is a database created by the cardiology team at the Department of Biomedical Engineering, Brno University of Technology, for the purpose of evaluating PPG quality and estimation of heart rate (HR). The data comprises 48 10-second recordings of PPGs and associated ECG signals used for determination of reference HR. The data were collected from 12 subjects (6 female, 6 male) aged between 21 to 61 years. Recordings were carried out between August 2020 and October 2020. 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 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 any analysis.
This database was created for the purpose of developing and evaluating algorithms designed to assess the quality of photoplethysmogram (PPG) records and algorithms designed for estimation of heart rate (HR) from PPG. Evaluation of PPG quality and estimation of HR from PPG has become a popular research topic [1-6], driven in part by the increased use of smartphones for health monitoring. A majority of HR estimation smartphone applications are neither tested, nor certified and moreover are not verified by medical experts (except e.g. Preventicus Heartbeats ). It is logical for monitoring algorithms or applications to carry out signal quality estimation before HR estimation, and this approach may lead to greater robustness and reliability. For example, if a poor PPG signal quality is detected by a monitoring device, the signal may be discarded (preventing distorted and unreliable results) and a new measurement may be requested.
We recorded 48 10-second PPG signals using the Xiaomi Mi9 smartphone. The measured subject puts his or her index finger to the rear side of the smartphone to cover the camera and LED light. The light was turned on and a 30 second video of the finger was recorded. Concurrently, the single-lead ECG signal was recorded as a reference for HR. The Bittium Faros 360 device was used for this purpose. The electrodes were attached to the chest according to the device manual after the skin was properly prepared. PPG and ECG signals were synchronized manually. Each subject was measured four times. The first three measurements were in rest while the subject was sitting. During the fourth measurement, the subject was walking and moving the finger on the lens.
For the database, only the middle 10 seconds of recordings were included, to eliminate signal fluctuations. From the video with resolution of 720 × 1280 px, only the red color channel  was considered and the PPG signal was created using averaging of each frame. Finally, the PPG signal was inverted. The sampling frequency was 30 Hz (frames per second). The sampling frequency of ECG signal was 1,000 Hz. In the ECG signal, the QRS complexes were found using robust QRS detector based on combination of three independent methods  and manually verified. Then, the median HR was calculated (one number for the whole 10 second signal) and this number was considered the reference HR for PPG signal.
The database is balanced in terms of gender (6 female records and 6 male records) and age (21 to 61 years, mean 36 years, median 24 years). Each subject was measured four times. The database contains binary signal-quality labels:
- 1: indicates good quality for HR estimation
- 0: indicates poor quality; signals where the HR cannot be detected reliably and thus unsuitable for analysis.
Quality annotations are based on HR estimation only on PPG signal and comparison with reference ECG value of HR. Each PPG signal was annotated by 5 experts in terms of HR. Annotators were provided with tailor-made software for the purpose of annotating, but use of the software was voluntary. The software included the picture of original PPG signal, decomposition of the PPG signal using stationary wavelet transform, HR estimated from each frequency band, spectrum created using fast Fourier transform and HR estimated from spectrum. Annotators also had the opportunity to decline to estimate the HR when the estimate would not be reliable. Annotators saw only the PPG signal (ECG signals were not provided).
Once the annotations were complete, we attempted to reach consensus for the 5 annotators. Only the "good" annotations were considered, meaning those HR values that differed from the reference HR by less than or equal to 5 bpm. The maximal error value of 5 bpm is based on the international standard IEC 60601-2-27 , moreover in this database it is more strict. When at least three annotators provided good annotations, the signal was considered of
1 ("good quality"). When this condition was not met, the signal was labeled
0 ("poor quality").
All human studies were approved by the Institutional Review Board of DBME Faculty of Electrical Engineering and Communication, Brno University of Technology, on July 27, 2018 (IRB Protocol EC:EK:05b/2018). Informed written consent was obtained from all subjects prior to the studies.
Each record contains PPG signal and one-lead ECG recorded with a sampling frequency of 30 Hz and 1,000 Hz, respectively. All data are provided in the WaveForm Database (WFDB) format. The names (IDs) of the recordings are six-digit numbers where the first three numbers are unique subject identifiers and the next three numbers indicate the measurement number of this subject. The PPG and ECG signals are in two separate files:
The annotations (
QUALITY-HR-ANN.csv) are recorded in a CSV file with three columns. The first column contains signal IDs. The second column contains a binary quality indicator (
1 ("good quality"),
0 ("poor quality"). The third column contains the reference HR.
The raw HR estimations from each annotator are available in
raw-HR-annotations.csv. Patient demographics (gender, age, weight) and motion information are provided in
This database is intended for the development, evaluation and objective comparison of algorithms designed to assess the quality of PPG records and algorithms designed for PPG HR estimation. One of the unique features of the database is that the quality annotations of each signal is based on 5 experts' annotations and their consensus, meeting international standard guidelines, and moreover meeting the stricter variation.
This work has been funded by the United States Office of Naval Research (ONR) Global, award number N62909-19-1-2006.
Conflicts of Interest
The authors have no conflict of interest.
- Orphanidou, C. (2018). Signal Quality Assessment in Physiological Monitoring State of the Art and Practical Considerations. Cham: Springer. doi:10.1007/978-3-319-68415-4.
- Naeini, E. K., Azimi, I., Rahmani, A. M., Liljeberg, P., & Dutt, N. (2019). A Real-time PPG Quality Assessment Approach for Healthcare Internet-of-Things. Procedia Computer Science, 151, 551-558. doi:10.1016/j.procs.2019.04.074.
- Nemcova, A., Jordanova, I., Varecka, M., Smisek, R., Marsanova, L., Smital, L., & Vitek, M. (2020). Monitoring of heart rate, blood oxygen saturation, and blood pressure using a smartphone. Biomedical Signal Processing and Control, 59. doi:10.1016/j.bspc.2020.101928.
- Koenig, N., Seeck, A., Eckstein, J., Mainka, A., Huebner, T., Voss, A., & Weber, S. (2016). Validation of a New Heart Rate Measurement Algorithm for Fingertip Recording of Video Signals with Smartphones. Telemedicine and E-Health, 22(8), 631-636. doi:10.1089/tmj.2015.0212.
- Siddiqui, S. A., Zhang, Y., Feng, Z., & Kos, A. (2016). A Pulse Rate Estimation Algorithm Using PPG and Smartphone Camera. Journal of Medical Systems, 40(5). doi:10.1007/s10916-016-0485-6.
- Tabei, F., Zaman, R., Foysal, K. H., Kumar, R., Kim, Y., & Chong, J. W. (2019). A novel diversity method for smartphone camera-based heart rhythm signals in the presence of motion and noise artifacts. Plos One, 14(6). doi:10.1371/journal.pone.0218248.
- Smital, L., Marsanova, L., Smisek, R., Nemcova, A., & Vitek, M. (2020, September). Robust QRS Detection Using Combination of Three Independent Methods. In Computing in cardiology 2020.
- International Electrotechnical Commission. (2014). Medical electrical equipment. Particular requirements for the basic safety and essential performance of electrocardiographic monitoring equipment (IEC 60601-2-27).
- Peng, R., Zhou, X., Lin, W., & Zhang, Y. (2015). Extraction of Heart Rate Variability from Smartphone Photoplethysmograms. Computational and Mathematical Methods in Medicine, 2015, 1-11. doi:10.1155/2015/516826.
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