DOI: 10.6084/m9.figshare.22256143.v6
title: A Four-Wavelength Photoplethysmography dataset for non-invasive hemoglobin assessment

description: Welcome to the Hb-PPG dataset repository. This dataset is designed to study physiological correlations of PPG signals within different wavelengths, and is eligible for in-depth analysis of relationships between PPG signals and hemoglobin. Hb-PPG also supports conducting joint analysis of multiple physiological parameters including body blood glucose, blood pressure and improving reliability of non-invasive hemoglobin measuring devices.------------------------------------------------------------------------------------------------------------------------------The above dataset is collected and managed by <b><i>CardioWorks Team</i></b>. If you have any questions about the data or relative researches, please contact us by email: liangyongbo@guet.edu.cn or liangyongbo001@gmail.com.The <b><i>CardioWorks Team</i></b> focuses on PPG-based portable or wearable cardiovascular health detection and disease assessment. For more research datasets and published papers, please pay attention to the following:Dataset:PPG-BP Database: https://doi.org/10.6084/m9.figshare.5459299.v5Cuff-less Blood Pressure Measurement based on Four-wavelength PPG Signals：https://doi.org/10.6084/m9.figshare.23283518.v1Published Articles:[1] Mohamed Elgendi, Richard Fletcher, <b>Yongbo Liang</b>, et al. The use of photoplethysmography for assessing hypertension [J]. <b><i>npj Digital Medicine</i></b>, <b>2019</b>, 2(1):1-11.<b>（2019）</b><b>Link</b>[2] Xudong Hu Shimin Yin, Xizhuang Zhang, Carlo Menon, Cheng Fang, Zhencheng Chen, Mohamed Elgendi* and <b>Yongbo Liang*</b>. Blood pressure stratification using photoplethysmography and light gradient boosting machine [J]. <b><i>Frontiers in Physiology</i></b>, <b>2023</b>, 14(1072273): 1-11.（<b>2023</b>）<b>Link</b>[3] <b>Yongbo Liang</b>, Shimin Yin, Qunfeng Tang, Zhenyu Zheng, Mohamed Elgendi* and Zhencheng Chen*. Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals. <b><i>Frontiers in Physiology</i></b>, 02 October 2020. Doi: 10.3389/fphys.2020.569050. （<b>2020</b>）<b>Link</b>[4] Cheng, Peng，Chen, Zhencheng*，Li, Quanzhong，Gong, Qiong，Zhu, Jianming，<b>Liang, Yongbo*</b>. Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning. <b><i>IEEE Access</i></b> 8, 172692-172706 (<b>2020</b>). <b>Link</b>[5] Zhenyu Zheng, Zhencheng Chen*, Fangrong Hu, Jianming Zhu, Qunfeng Tang, <b>Yongbo Liang</b><sup><strong>*</strong></sup>. An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology [J]. <b><i>Electronics</i></b>, <b>2020</b>, 9(1): 1-15. <b>Link</b>[6] <b>Yongbo Liang</b>, Derek Abbott, Newton Howard, Kenneth Lim, Rabab Ward and Mohamed Elgendi*. How Effective Is Pulse Arrival Time for Evaluating Blood Pressure? Challenges and Recommendations from a Study Using the MIMIC Database. <b><i>Journal of Clinical Medicine</i></b>, 8， 1-14, doi:10.3390/jcm8030337 (<b>2019</b>). <b>Link</b>[7] <b>Yongbo Liang</b>, Zhencheng Chen*, Guiyong Liu, Mohamed Elgendi*. A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China. <b><i>Scientific data</i></b>, doi:10.1038/sdata.2018.20 (<b>2018</b>). <b>Link</b>[8] <b>Yongbo Liang</b>, Mohamed Elgendi*, Zhencheng Chen* & Rabab Ward. An optimal filter for short photoplethysmogram signals. <b><i>Scientific data</i></b>, 5, 180076, doi:10.1038/sdata.2018.76 (<b>2018</b>). <b>Link</b>[9] <b>Yongbo Liang</b>, Zhencheng Chen*, Rabab Ward & Mohamed Elgendi*. Hypertension Assessment Using Photoplethysmography: A Risk Stratification Approach.<b> </b><b><i>Journal of Clinical Medicine</i></b>, 8, doi:10.3390/jcm8010012 (<b>2018</b>). <b>Link</b>[10] <b>Yongbo Liang</b>, Zhencheng Chen, Rabab Ward & Mohamed Elgendi*. Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database. <b><i>Diagnostics</i></b>, 8, doi:10.3390/diagnostics8030065 (<b>2018</b>). <b>Link</b>[11] <b>Yongbo Liang</b>, Zhencheng Chen, Rabab Ward & Mohamed Elgendi*. Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification. <b><i>Biosensors</i></b>, 8，doi:10.3390/bios8040101 (<b>2018</b>). <b>Link</b>[12] Xuhao Dong Ziyi Wang, Liangli Cao, Zhencheng Chen*, <b>Yongbo Liang*</b>. Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals [J]. <b><i>Diagnostics</i></b>, 2023, 13(5): 1-14. <b>Link</b>[13] Zhencheng Chen, Huishan Qin, Wenjun Ge, Shiyong Li*, <b>Yongbo Liang*</b>. Research on a Non-Invasive Hemoglobin Measurement System Based on Four-Wavelength Photoplethysmography [J]. <b><i>Electronics</i></b>, 2023, 12(6): 1-12. <b>Link</b>[14] Yang Zhang, Jianming Zhu, <b>Yongbo Liang</b>, Hongbo Chen, Shimin Yin and Zhencheng Chen*. Non-invasive blood glucose detection system based on conservation of energy method. <b><i>Physiological measurement</i></b>, <b>2017</b>, 38: 325-342.[15]<b> </b><b>Yongbo Liang</b>, Ahmed Hussain, Derek Abbott, Carlo Menon, Rabab Ward and Mohamed Elgendi*. Impact of Data Transformation: An ECG Heartbeat Classification Approach. <b><i>Frontiers in Digital Health, Dec 23, 2020</i></b><b><i> </i></b>doi: 10.3389/fdgth.2020.610956 (2020), <b>Link</b>