{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T08:24:15Z","timestamp":1785918255258,"version":"3.56.0"},"reference-count":46,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,15]],"date-time":"2019-12-15T00:00:00Z","timestamp":1576368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Found of China","award":["81971700"],"award-info":[{"award-number":["81971700"]}]},{"name":"National Natural Science Found of China","award":["81371713"],"award-info":[{"award-number":["81371713"]}]},{"name":"Graduate Research and Innovation Foundation of Chongqing, China","award":["CYS18017"],"award-info":[{"award-number":["CYS18017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Blood pressure is an extremely important blood hemodynamic parameter. The pulse wave contains abundant blood-pressure information, and the convenience and non-invasivity of its measurement make it ideal for non-invasive continuous monitoring of blood pressure. Based on combined photoplethysmography and electrocardiogram signals, this study aimed to extract the waveform information, introduce individual characteristics, and construct systolic and diastolic blood-pressure (SBP and DBP) estimation models using the back-propagation error (BP) neural network. During the model construction process, the mean impact value method was employed to investigate the impact of each feature on the model output and reduce feature redundancy. Moreover, the multiple population genetic algorithm was applied to optimize the BP neural network and determine the initial weights and threshold of the network. Finally, the models were integrated for further optimization to generate the final individualized continuous blood-pressure monitoring models. The results showed that the predicted values of the model in this study correlated significantly with the measured values of the electronic sphygmomanometer. The estimation errors of the model met the Association for the Advancement of Medical Instrumentation (AAMI) criteria (the SBP error was 2.5909 \u00b1 3.4148 mmHg, and the DBP error was 2.6890 \u00b1 3.3117 mmHg) and the Grade A British Hypertension Society criteria.<\/jats:p>","DOI":"10.3390\/s19245543","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T05:19:38Z","timestamp":1576473578000},"page":"5543","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography"],"prefix":"10.3390","volume":"19","author":[{"given":"Haiyan","family":"Wu","sequence":"first","affiliation":[{"name":"College of Bioengineering, Chongqing University, Chongqing 400044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Bioengineering, Chongqing University, Chongqing 400044, China"},{"name":"Chongqing Medical Electronics Engineering Technology Center, Chongqing 400044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengze","family":"Li","sequence":"additional","affiliation":[{"name":"College of Bioengineering, Chongqing University, Chongqing 400044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lackovi\u0107, I. (2017). Inspection and Testing of Noninvasive Blood Pressure Measuring Devices. Biomedical Engineering, Springer Nature Singapore Pte Ltd.","DOI":"10.1007\/978-981-10-6650-4_5"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/0140-6736(90)90878-9","article-title":"Blood pressure, stroke, and coronary heart disease Part 1, prolonged differences in blood pressure: Prospective observational studies corrected for the regression dilution bias","volume":"335","author":"Macmahon","year":"1990","journal-title":"Lancet"},{"key":"ref_3","unstructured":"World Health Organization (2016). World Health Statistics 2016: Monitoring Health for the SDGs, WHO."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fortino, G., and Giampa, V. (May, January 29). PPG-based methods for non invasive and continuous blood pressure measurement: An overview and development issues in body sensor networks. Proceedings of the 2010 IEEE International Workshop on Medical Measurements and Applications, Ottawa, ON, Canada.","DOI":"10.1109\/MEMEA.2010.5480201"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/hr.2014.149","article-title":"Prevalence of white-coat and masked hypertension in national and international registries","volume":"38","author":"Manuel","year":"2015","journal-title":"Hypertens. Res."},{"key":"ref_6","first-page":"1","article-title":"Comparison of Invasive and Noninvasive Blood Pressure Measurements for Assessing Signal Complexity and Surgical Risk in Cardiac Surgical Patients","volume":"1","author":"Gibson","year":"2019","journal-title":"Anesth. Analg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1111\/anae.14433","article-title":"Non-invasive blood pressure measurement displays","volume":"73","author":"Scott","year":"2018","journal-title":"Anaesthesia"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"180","DOI":"10.4103\/0971-9784.97973","article-title":"A comparison of a continuous noninvasive arterial pressure (CNAP?) monitor with an invasive arterial blood pressure monitor in the cardiac surgical ICU","volume":"15","author":"Singh","year":"2012","journal-title":"Ann. Card Anaesth."},{"key":"ref_9","first-page":"73","article-title":"A TRANSDUCER FOR THE CONTINUOUS EXTERNAL MEASUREMENT OF ARTERIAL BLOOD PRESSURE","volume":"10","author":"Pressman","year":"1963","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/0021-9290(83)90037-4","article-title":"Arterial tonometry: Review and analysis","volume":"16","author":"Drzewiecki","year":"1983","journal-title":"J. Biomech."},{"key":"ref_11","unstructured":"Penaz, J. (1973, January 13\u201317). Photoelectric measurement of blood pressure, volume and flow in the finger. Proceedings of the Digest of the 10th international conference on medical and biological engineering, Dresden, Germany."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1111\/j.1469-8986.1976.tb03344.x","article-title":"Pulse wave velocity as a measure of blood pressure change","volume":"13","author":"Gribbin","year":"1976","journal-title":"Psychophysiology"},{"key":"ref_13","first-page":"13","article-title":"Indirect blood pressure measurement by the pulse wave velocity method","volume":"22","author":"Tanaka","year":"1984","journal-title":"Iyodenshi Seitai Kogaku"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1152\/japplphysiol.00657.2005","article-title":"Pulse transit time measured from the ECG: An unreliable marker of beat-to-beat blood pressure","volume":"100","author":"Payne","year":"2006","journal-title":"J. Appl. Physiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1161\/01.RES.5.6.594","article-title":"A method using induced waves to study pressure propagation in human arteries","volume":"5","author":"LANDOWNE","year":"1957","journal-title":"Circ. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1111\/j.1469-8986.1981.tb01545.x","article-title":"Pulse transit time as an indicator of arterial blood pressure","volume":"18","author":"Geddes","year":"1981","journal-title":"Psychophysiology"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1109\/IEMBS.2006.260590","article-title":"Adaptive blood pressure estimation from wearable PPG sensors using peripheral artery pulse wave velocity measurements and multi-channel blind identification of local arterial dynamics","volume":"1","author":"McCombie","year":"2006","journal-title":"Conf. Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"12060","DOI":"10.1088\/1742-6596\/307\/1\/012060","article-title":"An Investigation of Pulse Transit Time as a Non-Invasive Blood Pressure Measurement Method","volume":"307","author":"McCarthy","year":"2011","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Samria, R., Jain, R., Jha, A., Saini, S., and Chowdhury, S.R. (2014, January 14\u201316). Noninvasive Cuffless Estimation of Blood Pressure using photoplethysmography without electrocardiograph measurement. Proceedings of the 2014 IEEE Region 10 Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/TENCONSpring.2014.6863037"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shen, Z., Miao, F., Meng, Q., and Li, Y. (2015, January 24\u201326). Cuffless and continuous blood pressure estimation based on multiple regression analysis. Proceedings of the 2015 5th International Conference on Information Science and Technology (ICIST), Changsha, China.","DOI":"10.1109\/ICIST.2015.7288952"},{"key":"ref_21","first-page":"1","article-title":"Design of a Continuous Blood Pressure Measurement System Based on Pulse Wave and ECG Signals","volume":"6","author":"Li","year":"2018","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_22","unstructured":"Teng, X.F., and Zhang, Y.T. (2003, January 17\u201321). Continuous and noninvasive estimation of arterial blood pressure using a photoplethysmographic approach. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Cancun, Mexico."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3007","DOI":"10.1364\/BOE.7.003007","article-title":"Optical blood pressure estimation with photoplethysmography and FFT-based neural networks","volume":"7","author":"Xing","year":"2016","journal-title":"Biomed. Opt. Express."},{"key":"ref_24","first-page":"1","article-title":"A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram","volume":"2018","author":"Wang","year":"2018","journal-title":"J. Healthc. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rundo, F., Ortis, A., Battiato, S., and Conoci, S. (2018). Advanced Bio-Inspired System for Noninvasive Cuff-Less Blood Pressure Estimation from Physiological Signal Analysis. Computation, 6.","DOI":"10.3390\/computation6030046"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rundo, F., Conoci, S., Ortis, A., and Battiato, S. (2018). An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment. Sensors, 18.","DOI":"10.3390\/s18020405"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Maher, N., Elsheikh, G.A., Anis, W.R., and Emara, T. (2019, January 18\u201330). Non-invasive Calibration-Free Blood Pressure Estimation Based on Artificial Neural Network. Proceedings of the International Conference on Advanced Machine Learning Technologies and Applications, Cairo, Egypt.","DOI":"10.1007\/978-3-030-14118-9_69"},{"key":"ref_28","first-page":"6942","article-title":"Comparative study on artificial neural network with multiple regressions for continuous estimation of blood pressure","volume":"7","author":"Yi","year":"2005","journal-title":"Conf Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kurylyak, Y., Lamonaca, F., and Grimaldi, D. (2013, January 6\u20139). A Neural Network-based method for continuous blood pressure estimation from a PPG signal. Proceedings of the IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Minneapolis, MN, USA.","DOI":"10.1109\/I2MTC.2013.6555424"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Feng, Z. (2017, January 24\u201326). A SVM Method for Continuous Blood Pressure Estimation from a PPG Signal. Proceedings of the ACM Press the 9th International Conference, Singapore.","DOI":"10.1145\/3055635.3056634"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.bspc.2018.08.022","article-title":"Blood pressure estimation from appropriate and inappropriate PPG signals using A whole-based method","volume":"47","author":"Mousavi","year":"2019","journal-title":"Biomed. Signal. Process. Control."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, R., Huang, Z.P., Ji, L.Y., and Wu, J.K. (2016, January 14\u201317). Beat-to-beat ambulatory blood pressure estimation based on random forest. Proceedings of the 13th International Conference on Wearable and Implantable Body Sensor Networks, San Francisco, CA, USA.","DOI":"10.1109\/BSN.2016.7516258"},{"key":"ref_33","unstructured":"Association for the Advancement of Medical Instrumentation (2019, June 22). Manual, Electronic, or Automated Sphygmomanometers. SNSI\/AAMI SP10:2002. Available online: https:\/\/webstore.ansi.org\/standards\/aami\/ansiaamisp102002a12003."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sharma, M., Barbosa, K., Ho, V., Griggs, D., Ghirmai, T., Krishnan, S., Hsiai, T., Chiao, J., and Cao, H. (2017). Cuff-Less and Continuous Blood Pressure Monitoring: A Methodological Review. Technologies, 5.","DOI":"10.3390\/technologies5020021"},{"key":"ref_35","first-page":"445","article-title":"Study on Wavelet Packet Denoising Technique and its Application in Mechanical Fault Diagnosis","volume":"713\u2013715","author":"Xiao","year":"2014","journal-title":"App. Mechan. Mat."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1080\/07373937.2016.1260031","article-title":"Desiccant wheel system modeling improvement using multiple population genetic algorithm training of neural network","volume":"35","author":"Yang","year":"2017","journal-title":"Dry Technol."},{"key":"ref_37","first-page":"593","article-title":"Theory of the backpropagation neural network","volume":"1","year":"1988","journal-title":"Neural Netw."},{"key":"ref_38","first-page":"87","article-title":"Non-invasive continuous blood pressure measurement based on mean impact value method, BP neural network, and genetic algorithm","volume":"26","author":"Tan","year":"2018","journal-title":"Technol. Health Care Off. J. Eur. Soc. Eng. Med."},{"key":"ref_39","unstructured":"Fu, Z.G., Qi, M.F., and Jing, Y. (2012, January 27\u201329). Regression forecast of main steam flow based on mean impact value and support vector regression. Proceedings of the 2012 Asia-Pacific Power and Energy Engineering Conference, Shanghai, China."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, S., Ji, Z., Wu, H., and Xu, Y. (2019). A Non-Invasive Continuous Blood Pressure Estimation Approach Based on Machine Learning. Sensors, 19.","DOI":"10.3390\/s19112585"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"840","DOI":"10.4028\/www.scientific.net\/AMM.743.840","article-title":"Calibration a Magnetometer Based on Multiple Population Genetic Algorithm","volume":"743","author":"Pang","year":"2015","journal-title":"Appl. Mech. Mater."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1002\/(SICI)1099-128X(199801\/02)12:1<41::AID-CEM500>3.0.CO;2-F","article-title":"The objective function of partial least-squares regression","volume":"12","author":"Braak","year":"1998","journal-title":"J. Chemom."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.aca.2007.03.024","article-title":"Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton\u2013viscose textiles","volume":"595","author":"Durand","year":"2007","journal-title":"Anal. Chim. Acta."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1097\/00004872-199007000-00004","article-title":"The British Hypertension Society protocol for the evaluation of automated and semi-automated blood pressure measuring devices with special reference to ambulatory systems","volume":"8","author":"Petrie","year":"1990","journal-title":"J. Hypertens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1097\/MBP.0b013e32833c8b39","article-title":"Validation of the Tiba Medical Ambulo 2400 ambulatory blood pressure monitor to the ISO Standard and BHS protocol","volume":"15","author":"Alpert","year":"2010","journal-title":"Blood Press. Monit."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"8","DOI":"10.2307\/2685263","article-title":"Thirteen Ways to Look at the Correlation Coefficient","volume":"42","author":"Rodgers","year":"1988","journal-title":"Am. Stat."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/24\/5543\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:42:32Z","timestamp":1760190152000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/24\/5543"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,15]]},"references-count":46,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19245543"],"URL":"https:\/\/doi.org\/10.3390\/s19245543","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,15]]}}}