A Four-Wavelength Photoplethysmography dataset for non-invasive hemoglobin assessment
<p dir="ltr">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...
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| _version_ | 1849927635502628864 |
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| author | Liang yongbo (4822017) |
| author_facet | Liang yongbo (4822017) |
| author_role | author |
| dc.creator.none.fl_str_mv | Liang yongbo (4822017) |
| dc.date.none.fl_str_mv | 2025-11-25T07:57:12Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.22256143.v6 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Hemoglobin_detection_based_on_four-wavelength_PPG_signal_zip/22256143 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medical biotechnology diagnostics (incl. biosensors) hemoglobin analysis PPG signal noninvasive biosensing technologies |
| dc.title.none.fl_str_mv | A Four-Wavelength Photoplethysmography dataset for non-invasive hemoglobin assessment |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p dir="ltr">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.</p><p>------------------------------------------------------------------------------------------------------------------------------</p><p dir="ltr">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.</p><p dir="ltr">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:</p><p dir="ltr">Dataset:</p><ol><li>PPG-BP Database: <a href="https://doi.org/10.6084/m9.figshare.5459299.v5" rel="noreferrer" target="_blank">https://doi.org/10.6084/m9.figshare.5459299.v5</a></li><li>Cuff-less Blood Pressure Measurement based on Four-wavelength PPG Signals：<a href="https://doi.org/10.6084/m9.figshare.23283518.v1" target="_blank">https://doi.org/10.6084/m9.figshare.23283518.v1</a></li></ol><p dir="ltr">Published Articles:</p><p dir="ltr">[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><a href="https://www.nature.com/articles/s41746-019-0136-7" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>）<a href="https://www.frontiersin.org/articles/10.3389/fphys.2023.1072273/full" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>）<a href="https://www.frontiersin.org/articles/10.3389/fphys.2020.569050/full" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>). <a href="https://ieeexplore.ieee.org/abstract/document/9201275" target="_blank"><b>Link</b></a></p><p dir="ltr">[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. <a href="https://www.mdpi.com/2079-9292/9/1/121" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>). <a href="https://www.mdpi.com/2077-0383/8/3/337" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>). <a href="https://www.nature.com/articles/sdata201820" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>). <a href="https://www.nature.com/articles/sdata201876" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>). <a href="https://www.mdpi.com/2077-0383/8/1/12" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>). <a href="https://www.mdpi.com/2075-4418/8/3/65" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>). <a href="https://www.mdpi.com/2079-6374/8/4/101" target="_blank"><b>Link</b></a></p><p dir="ltr">[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. <a href="https://www.mdpi.com/2075-4418/13/5/913/html" target="_blank"><b>Link</b></a></p><p dir="ltr">[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. <a href="https://www.mdpi.com/2079-9292/12/6/1346" target="_blank"><b>Link</b></a></p><p dir="ltr">[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.</p><p dir="ltr">[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), <a href="https://www.frontiersin.org/articles/10.3389/fdgth.2020.610956/full" target="_blank"><b>Link</b></a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara_9db8f7ead9b311e245eca0a9a05efca8 |
| identifier_str_mv | 10.6084/m9.figshare.22256143.v6 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/22256143 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Four-Wavelength Photoplethysmography dataset for non-invasive hemoglobin assessmentLiang yongbo (4822017)Medical biotechnology diagnostics (incl. biosensors)hemoglobin analysisPPG signalnoninvasive biosensing technologies<p dir="ltr">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.</p><p>------------------------------------------------------------------------------------------------------------------------------</p><p dir="ltr">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.</p><p dir="ltr">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:</p><p dir="ltr">Dataset:</p><ol><li>PPG-BP Database: <a href="https://doi.org/10.6084/m9.figshare.5459299.v5" rel="noreferrer" target="_blank">https://doi.org/10.6084/m9.figshare.5459299.v5</a></li><li>Cuff-less Blood Pressure Measurement based on Four-wavelength PPG Signals：<a href="https://doi.org/10.6084/m9.figshare.23283518.v1" target="_blank">https://doi.org/10.6084/m9.figshare.23283518.v1</a></li></ol><p dir="ltr">Published Articles:</p><p dir="ltr">[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><a href="https://www.nature.com/articles/s41746-019-0136-7" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>）<a href="https://www.frontiersin.org/articles/10.3389/fphys.2023.1072273/full" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>）<a href="https://www.frontiersin.org/articles/10.3389/fphys.2020.569050/full" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>). <a href="https://ieeexplore.ieee.org/abstract/document/9201275" target="_blank"><b>Link</b></a></p><p dir="ltr">[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. <a href="https://www.mdpi.com/2079-9292/9/1/121" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>). <a href="https://www.mdpi.com/2077-0383/8/3/337" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>). <a href="https://www.nature.com/articles/sdata201820" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>). <a href="https://www.nature.com/articles/sdata201876" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>). <a href="https://www.mdpi.com/2077-0383/8/1/12" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>). <a href="https://www.mdpi.com/2075-4418/8/3/65" target="_blank"><b>Link</b></a></p><p dir="ltr">[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>). <a href="https://www.mdpi.com/2079-6374/8/4/101" target="_blank"><b>Link</b></a></p><p dir="ltr">[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. <a href="https://www.mdpi.com/2075-4418/13/5/913/html" target="_blank"><b>Link</b></a></p><p dir="ltr">[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. <a href="https://www.mdpi.com/2079-9292/12/6/1346" target="_blank"><b>Link</b></a></p><p dir="ltr">[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.</p><p dir="ltr">[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), <a href="https://www.frontiersin.org/articles/10.3389/fdgth.2020.610956/full" target="_blank"><b>Link</b></a></p>2025-11-25T07:57:12ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.6084/m9.figshare.22256143.v6https://figshare.com/articles/dataset/Hemoglobin_detection_based_on_four-wavelength_PPG_signal_zip/22256143CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/222561432025-11-25T07:57:12Z |
| spellingShingle | A Four-Wavelength Photoplethysmography dataset for non-invasive hemoglobin assessment Liang yongbo (4822017) Medical biotechnology diagnostics (incl. biosensors) hemoglobin analysis PPG signal noninvasive biosensing technologies |
| status_str | publishedVersion |
| title | A Four-Wavelength Photoplethysmography dataset for non-invasive hemoglobin assessment |
| title_full | A Four-Wavelength Photoplethysmography dataset for non-invasive hemoglobin assessment |
| title_fullStr | A Four-Wavelength Photoplethysmography dataset for non-invasive hemoglobin assessment |
| title_full_unstemmed | A Four-Wavelength Photoplethysmography dataset for non-invasive hemoglobin assessment |
| title_short | A Four-Wavelength Photoplethysmography dataset for non-invasive hemoglobin assessment |
| title_sort | A Four-Wavelength Photoplethysmography dataset for non-invasive hemoglobin assessment |
| topic | Medical biotechnology diagnostics (incl. biosensors) hemoglobin analysis PPG signal noninvasive biosensing technologies |