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+<h1>Brno University of Technology Smartphone PPG Database (BUT PPG)</h1>
+<p><strong>Creators:</strong> Andrea Nemcova, Radovan Smisek, Eniko Vargova, Lucie Maršánová, Martin Vitek, Lukas Smital, Marina Filipenska, Pavlina Sikorova, Pavel Gálík</p>
+<p><strong>Published:</strong> Aug. 23, 2024. <strong>Version:</strong> 2.0.0</p>
+
+<h2>Citation</h2>
+<p>When using this resource, please cite:<br>
+Nemcova, A., Smisek, R., Vargova, E., Maršánová, L., Vitek, M., Smital, L., Filipenska, M., Sikorova, P., & Gálík, P. (2024). Brno University of Technology Smartphone PPG Database (BUT PPG) (version 2.0.0). PhysioNet. <a href="https://doi.org/10.13026/tn53-8153">https://doi.org/10.13026/tn53-8153</a>.</p>
+
+<p>Additionally, please cite the original publication:<br>
+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.</p>
+
+<p>Please include the standard citation for PhysioNet:<br>
+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.</p>
+
+<h2>Abstract</h2>
+<p>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 3,888 10-second recordings of PPGs and associated ECG signals used for determination of reference HR. All of the signals contain QRS complex positions, most signals contain moreover accelerometric data (ACC), and single-value annotations of blood pressure, blood oxygen saturation and glycaemia. The data were collected from 50 subjects (25 female, 25 male) aged between 19 to 76 years at rest and during various types of movement. Recordings were carried out between August 2020 and December 2021. PPG data were collected by smartphones Xiaomi Mi9 and Huawei P20 Pro with sampling frequency of 30 Hz. Reference ECG signals and ACC data were recorded using a mobile recorder Bittium Faros 360 or 180 with a sampling frequency of 1,000 Hz (ECG) and 100 Hz (ACC). 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.</p>
+
+<h2>Background</h2>
+<p>This database was primarily created for the purpose of developing and evaluating algorithms designed to assess the quality of PPG records and algorithms designed for estimation of HR from PPG. Evaluation of PPG quality and estimation of HR from PPG have become popular research topics, driven in part by the increased use of smartphones for health monitoring. Most smartphone HR estimation applications are neither tested nor certified, and few are verified by medical experts. Some exceptions exist for atrial fibrillation detection, such as Preventicus Heartbeats, FibriCheck, or Happitech. Quality estimation before HR analysis can improve robustness and reliability.</p>
+
+<h2>Methods</h2>
+<p>We recorded 3,888 10-second PPG signals using the Xiaomi Mi9 and Huawei P20 Pro smartphones. Subjects placed their index finger or ear on the smartphone camera, covering the lit LED light. PPG signals were created from the red channel of the videos. ECG signals were recorded simultaneously as reference using Bittium Faros devices. Signals were recorded at rest and during activities like walking, coughing, or talking. Blood pressure, glycaemia, and SpO2 were also measured.</p>
+
+<p>The signals were processed to remove initial instability. Sampling frequencies: 30 Hz for PPG, 1,000 Hz for ECG, and 100 Hz for ACC. QRS complexes were detected in ECG signals using a robust method combining three techniques and manually verified. HR was calculated as the median HR over 10 seconds. Annotators labeled signal quality and estimated HR independently using custom software without access to ECG.</p>
+
+<h2>Data Description</h2>
+<p>Each record contains:</p>
+<ul>
+<li>PPG signals (*_PPG.dat, *_PPG.hea)</li>
+<li>ECG signals (*_ECG.dat, *_ECG.hea)</li>
+<li>ACC signals (*_ACC.dat, *_ACC.hea) - available for records 112001 onwards</li>
+<li>Annotations (*_.qrs for QRS complexes)</li>
+<li>Quality and reference HR (quality-hr-ann.csv)</li>
+<li>Subject information (subject-info.csv)</li>
+</ul>
+
+<h2>Usage Notes</h2>
+<p>The database is intended for training and evaluating algorithms for PPG quality assessment (good/poor quality classification) and HR estimation. It may also be used for QRS detection and exploring physiological parameters estimation (e.g., SpO2, BP, glycaemia).</p>
+
+<h2>Release Notes</h2>
+<p>This is the second release of the database. It adds new signals (IDs from 112001), additional ACC data, and new annotations for blood pressure, SpO2, and glycaemia. QRS complex positions were added to all signals.</p>
+
+<h2>Ethics</h2>
+<p>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) and September 30, 2021 (IRB Protocol EC:EK:06b/2021). Informed written consent was obtained from all subjects.</p>
+
+<h2>Acknowledgements</h2>
+<p>This work was funded by the United States Office of Naval Research (ONR) Global, award numbers N62909-19-1-2006 and N62909-23-1-2050.</p>
+
+<h2>Conflicts of Interest</h2>
+<p>The authors declare no conflicts of interest.</p>
+
+<h2>References</h2>
+<ol>
+<li>Siddiqui, S. A., Zhang, Y., Feng, Z., &amp; Kos, A. (2016). A Pulse Rate Estimation Algorithm Using PPG and Smartphone Camera. <em>Journal of Medical Systems, 40</em>(5). <a href="https://doi.org/10.1007/s10916-016-0485-6">doi:10.1007/s10916-016-0485-6</a>.</li>
+<li>Orphanidou, C. (2018). Signal Quality Assessment in Physiological Monitoring State of the Art and Practical Considerations. Cham: Springer. <a href="https://doi.org/10.1007/978-3-319-68415-4">doi:10.1007/978-3-319-68415-4</a>.</li>
+<li>Naeini, E. K., Azimi, I., Rahmani, A. M., Liljeberg, P., &amp; Dutt, N. (2019). A Real-time PPG Quality Assessment Approach for Healthcare Internet-of-Things. <em>Procedia Computer Science, 151</em>, 551-558. <a href="https://doi.org/10.1016/j.procs.2019.04.074">doi:10.1016/j.procs.2019.04.074</a>.</li>
+<li>Nemcova, A., Jordanova, I., Varecka, M., Smisek, R., Marsanova, L., Smital, L., &amp; Vitek, M. (2020). Monitoring of heart rate, blood oxygen saturation, and blood pressure using a smartphone. <em>Biomedical Signal Processing and Control, 59</em>. <a href="https://doi.org/10.1016/j.bspc.2020.101928">doi:10.1016/j.bspc.2020.101928</a>.</li>
+<li>Tabei, F., Zaman, R., Foysal, K. H., Kumar, R., Kim, Y., &amp; Chong, J. W. (2019). A novel diversity method for smartphone camera-based heart rhythm signals in the presence of motion and noise artifacts. <em>Plos One, 14</em>(6). <a href="https://doi.org/10.1371/journal.pone.0218248">doi:10.1371/journal.pone.0218248</a>.</li>
+<li>Smital, L., Marsanova, L., Smisek, R., Nemcova, A., &amp; Vitek, M. (2020, September). Robust QRS Detection Using Combination of Three Independent Methods. In Computing in cardiology 2020.</li>
+<li>International Electrotechnical Commission. (2014). Medical electrical equipment. Particular requirements for the basic safety and essential performance of electrocardiographic monitoring equipment (IEC 60601-2-27).</li>
+<li>Peng, R., Zhou, X., Lin, W., &amp; Zhang, Y. (2015). Extraction of Heart Rate Variability from Smartphone Photoplethysmograms. <em>Computational and Mathematical Methods in Medicine, 2015</em>, 1-11. <a href="https://doi.org/10.1155/2015/516826">doi:10.1155/2015/516826</a>.</li>
+</ol>