CP-PPG: A Dual-Channel PPG Dataset for Studying the Impact of Contact Pressure on Blood Pressure Biosensing

Photoplethysmography (PPG) based consumer wearable devices have been widely used for continuous health monitoring. However, PPG is vulnerable to the contact pressure (CP) between sensors and human skin, resulting in signal morphology distortions and unreliable measurements. Hence, we present a novel dual-channel PPG Dataset (CP-PPG). It includes fingertip PPG signals recorded at several different contact pressures, along with systolic (SBP) and diastolic (DBP) blood pressure and some basic physiological information from 142 subjects. It covers an age range of 21–86 years and contains 54% abnormal blood pressure cases. This "CP-PPG" Dataset is designed to analysis of how contact pressure influences PPG morphology and supports developing methods to improve the measurement reliability of PPG-based blood pressure biosensing.------------------------------------------------------------------------------------------------------------------------------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: [e-mail] or [e-mail] <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.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, <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>^{<strong>*</strong>}. 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* &amp; 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 &amp; 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 &amp; 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 &amp; 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>

光电容积描记法（Photoplethysmography，PPG）相关的消费级可穿戴设备已被广泛应用于连续健康监测领域。然而，PPG信号极易受传感器与人体皮肤间的接触压力（Contact Pressure，CP）影响，会导致信号形态畸变与测量结果不可靠。为此，本研究提出一种新型双通道PPG数据集（CP-PPG）。该数据集包含142名受试者在多种不同接触压力下采集的指尖PPG信号，同时收录了受试者的收缩压（Systolic Blood Pressure，SBP）、舒张压（Diastolic Blood Pressure，DBP）以及部分基础生理信息。受试者年龄跨度为21至86岁，其中54%存在血压异常情况。本“CP-PPG”数据集旨在研究接触压力对PPG信号形态的影响，并为开发提升基于PPG的血压生物传感测量可靠性的方法提供支撑。

上述数据集由CardioWorks团队采集并管理。若您对该数据集或相关研究存在疑问，可通过以下邮箱联系我们：[e-mail] 或 [e-mail]。
CardioWorks团队专注于基于PPG的便携式或可穿戴心血管健康检测与疾病评估研究。如需获取更多研究数据集与已发表论文，请关注以下内容：
### 数据集：
1. PPG-BP数据库：https://doi.org/10.6084/m9.figshare.5459299.v5
2. 基于四波长PPG信号的无创血红蛋白检测：https://doi.org/10.6084/m9.figshare.22256143.v1
3. 基于四波长PPG信号的无袖带血压测量：https://doi.org/10.6084/m9.figshare.23283518.v1

### 已发表论文：
[1] Mohamed Elgendi、Richard Fletcher、梁永波等. 光电容积描记法在高血压评估中的应用[J]. npj Digital Medicine, 2019, 2(1):1-11.（2019）[链接]
[2] 胡旭涛、尹世民、张希庄、Carlo Menon、方成、陈正成、Mohamed Elgendi* 与梁永波*. 基于光电容积描记法与轻量梯度提升机的血压分层[J]. Frontiers in Physiology, 2023, 14(1072273):1-11.（2023）[链接]
[3] 梁永波、尹世民、唐群峰、郑振宇、Mohamed Elgendi* 与陈正成*. 基于心电图信号的深度学习算法分类心跳事件[J]. Frontiers in Physiology, 2020年10月2日. Doi: 10.3389/fphys.2020.569050.（2020）[链接]
[4] 程鹏、陈正成*、李全忠、龚琼、朱建明、梁永波*. 结合时频分析与深度学习的光电容积描记信号房颤识别[J]. IEEE Access, 8:172692-172706（2020）[链接]
[5] 郑振宇、陈正成*、胡方荣、朱建明、唐群峰、梁永波*. 结合卷积神经网络与长短期记忆网络技术的心律失常自动诊断[J]. Electronics, 2020, 9(1):1-15.（2020）[链接]
[6] 梁永波、Derek Abbott、Newton Howard、Kenneth Lim、Rabab Ward 与Mohamed Elgendi*. 脉搏波传导时间评估血压的有效性如何？基于MIMIC数据库的研究挑战与建议[J]. Journal of Clinical Medicine, 8:1-14, doi:10.3390/jcm8030337（2019）[链接]
[7] 梁永波、陈正成*、刘桂勇、Mohamed Elgendi*. 适用于中国人群血压监测的短时长光电容积描记数据集[J]. Scientific data, doi:10.1038/sdata.2018.20（2018）[链接]
[8] 梁永波、Mohamed Elgendi*、陈正成* 与Rabab Ward. 适用于短时长光电容积描记信号的最优滤波器[J]. Scientific data, 5:180076, doi:10.1038/sdata.2018.76（2018）[链接]
[9] 梁永波、陈正成*、Rabab Ward 与Mohamed Elgendi*. 基于光电容积描记法的高血压评估：风险分层方法[J]. Journal of Clinical Medicine, 8, doi:10.3390/jcm8010012（2018）[链接]
[10] 梁永波、陈正成、Rabab Ward 与Mohamed Elgendi*. 基于心电图与光电容积描记信号的高血压评估：MIMIC数据库验证[J]. Diagnostics, 8, doi:10.3390/diagnostics8030065（2018）[链接]
[11] 梁永波、陈正成、Rabab Ward 与Mohamed Elgendi*. 光电容积描记法与深度学习：提升高血压风险分层能力[J]. Biosensors, 8, doi:10.3390/bios8040101（2018）[链接]
[12] 董旭浩、王梓仪、曹亮丽、陈正成*、梁永波*. 结合混合关系向量机的鲸鱼优化算法：一种基于光电容积描记信号的高鲁棒性呼吸频率预测模型[J]. Diagnostics, 2023, 13(5):1-14. [链接]
[13] 陈正成、秦慧珊、葛文军、李诗勇*、梁永波*. 基于四波长光电容积描记法的无创血红蛋白检测系统研究[J]. Electronics, 2023, 12(6):1-12. [链接]
[14] 张洋、朱建明、梁永波、陈洪波、尹世民与陈正成*. 基于能量守恒法的无创血糖检测系统[J]. Physiological measurement, 2017, 38:325-342.
[15] 梁永波、Ahmed Hussain、Derek Abbott、Carlo Menon、Rabab Ward 与Mohamed Elgendi*. 数据变换的影响：一种心电图心跳分类方法[J]. Frontiers in Digital Health, 2020年12月23日. doi:10.3389/fdgth.2020.610956（2020）[链接]