Blood Pressure Measurement based on Four-wavelength PPG Signals

Blood Pressure Measurement based on Four-wavelength PPG SignalsThis dataset was collected primarily to explore the role of PPG signals with different wavelengths in the prediction of cuffless blood pressure. 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 cuffless blood pressure measurement models using single or multiple PPG signals.The dataset contains four-wavelength PPG signals and blood pressure values measured by OMRON HEM-7201. There are data files "ppg_data" and physiological information files in the dataset. The physiological information files are saved as an Excel document named as "Subject Information.xlsx". SBP and DBP represent systolic blood pressure and diastolic blood pressure. SBP and DBP were measured before PPG signal collection and PPG signal collection begins immediately after blood pressure measurement. The dataset contains a total of 180 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: 940nmThe 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:[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> 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>doi: 10.3389/fdgth.2020.610956 (2020), <b>Link</b>

基于四波长光电容积描记（Photoplethysmography, PPG）信号的血压测量数据集。本数据集主要用于探究不同波长的光电容积描记信号在无袖带血压预测中的作用。所采集的PPG信号来自受试者食指，为反射式信号。本数据集可用于开展多波长PPG信号的数据挖掘研究，也可基于单路或多路PPG信号构建新型无袖带血压测量模型。
本数据集包含四波长PPG信号以及由欧姆龙（OMRON）HEM-7201型血压计测得的血压值。数据集内包含数据文件"ppg_data"与受试者生理信息文件，其中生理信息文件保存为名为"Subject Information.xlsx"的Excel文档。本数据集所用的SBP与DBP分别指代收缩压（Systolic Blood Pressure）与舒张压（Diastolic Blood Pressure），二者均在PPG信号采集前完成测量，且血压测量结束后立即启动PPG信号采集。
本数据集共包含180名受试者的相关数据，每段信号时长为60秒，采样频率为200 Hz。数据集内信号的波长信息如下：通道1：660 nm；通道2：730 nm；通道3：850 nm；通道4：940 nm。
本数据集由<b><i>CardioWorks团队</i></b>采集并维护。若您对该数据集或相关研究存在疑问，请通过以下邮箱联系我们：[e-mail] 或 [e-mail]。
<b><i>CardioWorks团队</i></b>专注于基于PPG的便携式或可穿戴式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，请关注以下成果：
[1] Mohamed Elgendi、Richard Fletcher、梁永波（Yongbo Liang）等. 光电容积描记术在高血压评估中的应用[J]. <b><i>npj Digital Medicine</i></b>, 2019, 2(1):1-11.（2019）<b>Link</b>
[2] 胡旭涛（Xudong Hu）、殷世民（Shimin Yin）、张希庄（Xizhuang Zhang）、Carlo Menon、方成（Cheng Fang）、陈正诚（Zhencheng Chen）、穆罕默德·埃尔根迪（Mohamed Elgendi）*、梁永波（Yongbo Liang）*. 基于光电容积描记术与轻梯度提升机的血压分层[J]. <b><i>Frontiers in Physiology</i></b>, 2023, 14(1072273): 1-11.（2023）<b>Link</b>
[3] 梁永波（Yongbo Liang）、殷世民（Shimin Yin）、唐群峰（Qunfeng Tang）、郑振宇（Zhenyu Zheng）、穆罕默德·埃尔根迪（Mohamed Elgendi）*、陈正诚（Zhencheng Chen）*. 基于心电信号的深度学习算法分类心跳事件[J]. <b><i>Frontiers in Physiology</i></b>, 2020年10月2日. Doi: 10.3389/fphys.2020.569050.（2020）<b>Link</b>
[4] 程鹏（Peng Cheng）、陈正诚（Zhencheng Chen）*、李全忠（Quanzhong Li）、龚琼（Qiong Gong）、朱建明（Jianming Zhu）、梁永波（Yongbo Liang）*. 结合时频分析与深度学习的PPG信号房颤识别[J]. <b><i>IEEE Access</i></b>, 8, 172692-172706（2020）. <b>Link</b>
[5] 郑振宇（Zhenyu Zheng）、陈正诚（Zhencheng Chen）*、胡方荣（Fangrong Hu）、朱建明（Jianming Zhu）、唐群峰（Qunfeng Tang）、梁永波（Yongbo Liang）*^{<strong>*</strong>}. 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]. <b><i>Electronics</i></b>, 2020, 9(1): 1-15. <b>Link</b>
[6] 梁永波（Yongbo Liang）、德里克·阿博特（Derek Abbott）、牛顿·霍华德（Newton Howard）、肯尼斯·林（Kenneth Lim）、拉巴布·沃德（Rabab Ward）、穆罕默德·埃尔根迪（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]. <b><i>Journal of Clinical Medicine</i></b>, 8, 1-14, doi:10.3390/jcm8030337（2019）. <b>Link</b>
[7] 梁永波（Yongbo Liang）、陈正诚（Zhencheng Chen）*、刘桂勇（Guiyong Liu）、穆罕默德·埃尔根迪（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]. <b><i>Scientific data</i></b>, doi:10.1038/sdata.2018.20（2018）. <b>Link</b>
[8] 梁永波（Yongbo Liang）、穆罕默德·埃尔根迪（Mohamed Elgendi）*、陈正诚（Zhencheng Chen）*、拉巴布·沃德（Rabab Ward）. 短时长光电容积描记信号的最优滤波方法[J]. <b><i>Scientific data</i></b>, 5, 180076, doi:10.1038/sdata.2018.76（2018）. <b>Link</b>
[9] 梁永波（Yongbo Liang）、陈正诚（Zhencheng Chen）*、拉巴布·沃德（Rabab Ward）、穆罕默德·埃尔根迪（Mohamed Elgendi）*. 基于光电容积描记术的高血压评估：风险分层方法[J]. <b><i>Journal of Clinical Medicine</i></b>, 8, doi:10.3390/jcm8010012（2018）. <b>Link</b>
[10] 梁永波（Yongbo Liang）、陈正诚（Zhencheng Chen）、拉巴布·沃德（Rabab Ward）、穆罕默德·埃尔根迪（Mohamed Elgendi）*. 基于心电与光电容积描记信号的高血压评估：MIMIC数据库验证[J]. <b><i>Diagnostics</i></b>, 8, doi:10.3390/diagnostics8030065（2018）. <b>Link</b>
[11] 梁永波（Yongbo Liang）、陈正诚（Zhencheng Chen）、拉巴布·沃德（Rabab Ward）、穆罕默德·埃尔根迪（Mohamed Elgendi）*. 光电容积描记术与深度学习：提升高血压风险分层能力[J]. <b><i>Biosensors</i></b>, 8, doi:10.3390/bios8040101（2018）. <b>Link</b>
[12] 董旭浩（Xuhao Dong）、王紫怡（Ziyi Wang）、曹亮丽（Liangli Cao）、陈正诚（Zhencheng Chen）*、梁永波（Yongbo Liang）*. 结合混合关系向量机的鲸鱼优化算法：一种基于PPG信号的高鲁棒性呼吸频率预测模型[J]. <b><i>Diagnostics</i></b>, 2023, 13(5): 1-14. <b>Link</b>
[13] 陈正诚（Zhencheng Chen）、秦惠珊（Huishan Qin）、葛文军（Wenjun Ge）、李世勇（Shiyong Li）*、梁永波（Yongbo Liang）*. 基于四波长光电容积描记术的无创血红蛋白检测系统研究[J]. <b><i>Electronics</i></b>, 2023, 12(6): 1-12. <b>Link</b>
[14] 张洋（Yang Zhang）、朱建明（Jianming Zhu）、梁永波（Yongbo Liang）、陈洪波（Hongbo Chen）、殷世民（Shimin Yin）、陈正诚（Zhencheng Chen）*. 基于能量守恒法的无创血糖检测系统[J]. <b><i>Physiological measurement</i></b>, 2017, 38: 325-342.
[15] 梁永波（Yongbo Liang）、艾哈迈德·侯赛因（Ahmed Hussain）、德里克·阿博特（Derek Abbott）、Carlo Menon、拉巴布·沃德（Rabab Ward）、穆罕默德·埃尔根迪（Mohamed Elgendi）*. 数据变换的影响：心电心跳分类方法研究[J]. <b><i>Frontiers in Digital Health</i></b>, 2020年12月23日. doi: 10.3389/fdgth.2020.610956（2020）, <b>Link</b>