Non-invasive Hemoglobin Detection based on Four-wavelength PPG Signal

Name: Non-invasive Hemoglobin Detection based on Four-wavelength PPG Signal
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Published: 2025-06-01 03:45:53
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Non-invasive Hemoglobin Detection based on Four-wavelength PPG SignalThis dataset was collected primarily to explore the role of PPG signals with different wavelengths in the prediction of non-invasive hemoglobin measurement. The PPG signals are collected at the human index finger which are the reflex type. This dataset can be used to study data mining of PPG signals with different wavelengths, or it can be used to build novel non-invasive hemoglobin measurement models using single or multiple PPG signals.The dataset contains a total of 58 subjects, each with 60 seconds signal length and 200 Hz sampling frequency.This dataset was supplemented with a dataset on June 5, 2023. The supplemented dataset consists of 20 subjects, each with 60 seconds signal length and 200 Hz sampling frequency.The wavelength information of signals in the dataset is listed as follows：channel1: 660nmchannel2: 730nmchannel3: 850nmchannel4: 940nmIf you use this dataset for further research, please cite the articles on this dataset.Zhencheng Chen, Huishan Qin, Wenjun Ge, Shiyong Li*, Yongbo Liang*. Research on a Non-Invasive Hemoglobin Measurement System Based on Four-Wavelength Photoplethysmography [J]. Electronics, 2023, 12(6): 1-12. Link------------------------------------------------------------------------------------------------------------------------------The above dataset is collected and managed by CardioWorks Team. 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 CardioWorks Team 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.v5Non-invasive Hemoglobin Detection based on Four-wavelength PPG Signal: https://doi.org/10.6084/m9.figshare.22256143.v1Cuff-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, Yongbo Liang, et al. The use of photoplethysmography for assessing hypertension [J]. npj Digital Medicine, 2019, 2(1):1-11.（2019）Link[2] Xudong Hu Shimin Yin, Xizhuang Zhang, Carlo Menon, Cheng Fang, Zhencheng Chen, Mohamed Elgendi* and Yongbo Liang*. Blood pressure stratification using photoplethysmography and light gradient boosting machine [J]. Frontiers in Physiology, 2023, 14(1072273): 1-11.（2023）Link[3] Yongbo Liang, Shimin Yin, Qunfeng Tang, Zhenyu Zheng, Mohamed Elgendi* and Zhencheng Chen*. Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals. Frontiers in Physiology, 02 October 2020. Doi: 10.3389/fphys.2020.569050. （2020）Link[4] Cheng, Peng，Chen, Zhencheng*，Li, Quanzhong，Gong, Qiong，Zhu, Jianming，Liang, Yongbo*. Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning. IEEE Access 8, 172692-172706 (2020). Link[5] Zhenyu Zheng, Zhencheng Chen*, Fangrong Hu, Jianming Zhu, Qunfeng Tang, Yongbo Liang*. An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology [J]. Electronics, 2020, 9(1): 1-15. Link[6] Yongbo Liang, 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. Journal of Clinical Medicine, 8， 1-14, doi:10.3390/jcm8030337 (2019). Link[7] Yongbo Liang, Zhencheng Chen*, Guiyong Liu, Mohamed Elgendi*. A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China. Scientific data, doi:10.1038/sdata.2018.20 (2018). Link[8] Yongbo Liang, Mohamed Elgendi*, Zhencheng Chen* & Rabab Ward. An optimal filter for short photoplethysmogram signals. Scientific data, 5, 180076, doi:10.1038/sdata.2018.76 (2018). Link[9] Yongbo Liang, Zhencheng Chen*, Rabab Ward & Mohamed Elgendi*. Hypertension Assessment Using Photoplethysmography: A Risk Stratification Approach. Journal of Clinical Medicine, 8, doi:10.3390/jcm8010012 (2018). Link[10] Yongbo Liang, Zhencheng Chen, Rabab Ward & Mohamed Elgendi*. Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database. Diagnostics, 8, doi:10.3390/diagnostics8030065 (2018). Link[11] Yongbo Liang, Zhencheng Chen, Rabab Ward & Mohamed Elgendi*. Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification. Biosensors, 8，doi:10.3390/bios8040101 (2018). Link[12] Xuhao Dong Ziyi Wang, Liangli Cao, Zhencheng Chen*, Yongbo Liang*. Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals [J]. Diagnostics, 2023, 13(5): 1-14. Link[13] Zhencheng Chen, Huishan Qin, Wenjun Ge, Shiyong Li*, Yongbo Liang*. Research on a Non-Invasive Hemoglobin Measurement System Based on Four-Wavelength Photoplethysmography [J]. Electronics, 2023, 12(6): 1-12. Link[14] Yang Zhang, Jianming Zhu, Yongbo Liang, Hongbo Chen, Shimin Yin and Zhencheng Chen*. Non-invasive blood glucose detection system based on conservation of energy method. Physiological measurement, 2017, 38: 325-342.[15] Yongbo Liang, Ahmed Hussain, Derek Abbott, Carlo Menon, Rabab Ward and Mohamed Elgendi*. Impact of Data Transformation: An ECG Heartbeat Classification Approach. Frontiers in Digital Health, Dec 23, 2020 doi: 10.3389/fdgth.2020.610956 (2020), Link

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