Descriptor : A new , short-recorded photoplethysmogram dataset for blood pressure monitoring in China

Open clinical trial data provide a valuable opportunity for researchers worldwide to assess new hypotheses, validate published results, and collaborate for scientific advances in medical research. Here, we present a health dataset for the non-invasive detection of cardiovascular disease (CVD), containing 657 data segments from 219 subjects. The dataset covers an age range of 20–89 years and records of diseases including hypertension and diabetes. Data acquisition was carried out under the control of standard experimental conditions and specifications. This dataset can be used to carry out the study of photoplethysmograph (PPG) signal quality evaluation and to explore the intrinsic relationship between the PPG waveform and cardiovascular disease to discover and evaluate latent characteristic information contained in PPG signals. These data can also be used to study early and noninvasive screening of common CVD such as hypertension and other related CVD diseases such as diabetes.

[1]  Survi Kyal,et al.  Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice , 2015, IEEE Transactions on Biomedical Engineering.

[2]  John Allen Photoplethysmography and its application in clinical physiological measurement , 2007, Physiological measurement.

[3]  Zbignevs Marcinkevics,et al.  Effect of probe contact pressure on the photoplethysmographic assessment of conduit artery stiffness , 2013, Journal of biomedical optics.

[4]  David A. Clifton,et al.  Signal-Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring , 2015, IEEE Journal of Biomedical and Health Informatics.

[5]  Mingshan Sun,et al.  Optical blood pressure estimation with photoplethysmography and FFT-based neural networks. , 2016, Biomedical optics express.

[6]  Dingchang Zheng,et al.  Non-invasive quantification of peripheral arterial volume distensibility and its non-linear relationship with arterial pressure. , 2009, Journal of biomechanics.

[7]  Ahmet Resit Kavsaoglu,et al.  A novel feature ranking algorithm for biometric recognition with PPG signals , 2014, Comput. Biol. Medicine.

[8]  M. Elgendi On the Analysis of Fingertip Photoplethysmogram Signals , 2012, Current cardiology reviews.

[9]  G. Di Candio,et al.  Different Impact of Essential Hypertension on Structural and Functional Age-Related Vascular Changes , 2017, Hypertension.

[10]  Paul S. Addison,et al.  Slope Transit Time (STT): A Pulse Transit Time Proxy requiring Only a Single Signal Fiducial Point , 2016, IEEE Transactions on Biomedical Engineering.

[11]  Sung-Chun Tang,et al.  Identification of Atrial Fibrillation by Quantitative Analyses of Fingertip Photoplethysmogram , 2017, Scientific Reports.

[12]  Mohamed Elgendi,et al.  Optimal Signal Quality Index for Photoplethysmogram Signals , 2016, Bioengineering.

[13]  Mehrdad Nourani,et al.  An adaptive deep learning approach for PPG-based identification , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[14]  Se Dong Min,et al.  Feasibility study for the non-invasive blood pressure estimation based on ppg morphology: normotensive subject study , 2017, BioMedical Engineering OnLine.

[15]  F N van de Vosse,et al.  Estimation of distributed arterial mechanical properties using a wave propagation model in a reverse way. , 2010, Medical engineering & physics.

[16]  Ken Kelley,et al.  A Best Practice for Research , 2007 .

[17]  Wan-Young Chung,et al.  Wireless Machine-to-Machine Healthcare Solution Using Android Mobile Devices in Global Networks , 2013, IEEE Sensors Journal.

[18]  Lijing L. Yan,et al.  Validation of the Omron HEM-7201 upper arm blood pressure monitor, for self-measurement in a high altitude environment, according to the European Society of Hypertension International Protocol revision 2010 , 2013, Journal of Human Hypertension.

[19]  H Hsiu,et al.  Effects of different contacting pressure on the transfer function between finger photoplethysmographic and radial blood pressure waveforms , 2011, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[20]  Per Olofsson,et al.  Digital Photoplethysmography for Assessment of Arterial Stiffness: Repeatability and Comparison with Applanation Tonometry , 2015, PloS one.

[21]  Panayiotis A Kyriacou,et al.  Photoplethysmography for the Assessment of Haemorheology , 2017, Scientific Reports.

[22]  Weidong Wang,et al.  A comb filter based signal processing method to effectively reduce motion artifacts from photoplethysmographic signals , 2015, Physiological measurement.

[23]  Myoungho Lee,et al.  Relations between ac-dc components and optical path length in photoplethysmography. , 2011, Journal of biomedical optics.

[24]  M. Nitzan,et al.  Photoplethysmographic waveform characteristics of newborns with coarctation of the aorta , 2017, Journal of Perinatology.

[25]  Boreom Lee,et al.  Blockwise PPG Enhancement Based on Time-Variant Zero-Phase Harmonic Notch Filtering , 2017, Sensors.

[26]  Xiao-Rong Ding,et al.  Multi-wavelength photoplethysmography method for skin arterial pulse extraction. , 2016, Biomedical optics express.

[27]  Steve Warren,et al.  Two-Stage Approach for Detection and Reduction of Motion Artifacts in Photoplethysmographic Data , 2010, IEEE Transactions on Biomedical Engineering.

[28]  Joon Lee,et al.  Signal Quality Estimation With Multichannel Adaptive Filtering in Intensive Care Settings , 2012, IEEE Transactions on Biomedical Engineering.