{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T18:45:06Z","timestamp":1784400306137,"version":"3.55.0"},"reference-count":41,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61501070"],"award-info":[{"award-number":["No. 61501070"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 \u00b1 5.36 mmHg for SBP and 2.39 \u00b1 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal.<\/jats:p>","DOI":"10.3390\/s21217207","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:24:22Z","timestamp":1635805462000},"page":"7207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model"],"prefix":"10.3390","volume":"21","author":[{"given":"Zheming","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102594","DOI":"10.1016\/j.bspc.2021.102594","article-title":"An approach to early stage detection of atherosclerosis using arterial blood pressure measurements","volume":"68","author":"Jain","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.1161\/HYPERTENSIONAHA.119.13393","article-title":"Resistant hypertension and atherosclerotic renal artery stenosis: Effects of angioplasty on ambulatory blood pressure. A retrospective uncontrolled single-center study","volume":"74","author":"Courand","year":"2019","journal-title":"Hypertension"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111261","DOI":"10.1016\/j.nut.2021.111261","article-title":"Improvement of nutritional status enhances cognitive and physical functions in older adults with orthostatic hypotension","volume":"90","author":"Kocyigit","year":"2021","journal-title":"Nutrition"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1594","DOI":"10.1213\/01.ANE.0000152392.26910.5E","article-title":"Systolic anterior motion of the mitral valve with left ventricular outflow tract obstruction: Three cases of acute perioperative hypotension in noncardiac surgery","volume":"100","author":"Luckner","year":"2005","journal-title":"Anesth. Analg."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1006\/cbmr.1997.1450","article-title":"A system for analysis of arterial blood pressure waveforms in humans","volume":"30","author":"Karamanoglu","year":"1997","journal-title":"Comput. Biomed. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1097\/MBP.0000000000000412","article-title":"Can an automatic oscillometric device replace a mercury sphygmomanometer on blood pressure measurement? A systematic review and meta-analysis","volume":"24","author":"Park","year":"2019","journal-title":"Blood Press. Monit."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"194","DOI":"10.5152\/FNJN.2021.19103","article-title":"Comparing Intra-Arterial, Auscultatory, and Oscillometric Measurement Methods for Arterial Blood Pressurem","volume":"29","author":"Zaybak","year":"2021","journal-title":"Florence Nightingale J. Nurs."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5779","DOI":"10.1016\/j.aej.2021.04.035","article-title":"Enhancement of blood pressure estimation method via machine learning","volume":"60","author":"Maher","year":"2021","journal-title":"Alex. Eng. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0967-3334\/28\/3\/R01","article-title":"Photoplethysmography and its application in clinical physiological measurement","volume":"28","author":"Allen","year":"2007","journal-title":"Physiol. Meas."},{"key":"ref_10","unstructured":"Fiorini, L., Cavallo, F., Martinelli, M., and Rovini, E. (2019). Characterization of a PPG wearable sensor to be embedded into an innovative ring-shaped device for healthcare monitoring. Italian Forum of Ambient Assisted Living, Springer."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Harfiya, L.N., Chang, C.C., and Li, Y.H. (2021). Continuous Blood Pressure Estimation Using Exclusively Photopletysmography by LSTM-Based Signal-to-Signal Translation. Sensors, 21.","DOI":"10.3390\/s21092952"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"11554","DOI":"10.1038\/s41598-017-11507-3","article-title":"Pulse transit time based continuous cuffless blood pressure estimation: A new extension and a comprehensive evaluation","volume":"7","author":"Ding","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.artmed.2018.12.005","article-title":"A novel dynamical approach in continuous cuffless blood pressure estimation based on ECG and PPG signals","volume":"97","author":"Sharifi","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1109\/JBHI.2020.3004032","article-title":"PCA-based multi-wavelength photoplethysmography algorithm for cuffless blood pressure measurement on elderly subjects","volume":"25","author":"Liu","year":"2020","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, Y.H., Harfiya, L.N., Purwandari, K., and Lin, Y.D. (2020). Real-time cuffless continuous blood pressure estimation using deep learning model. Sensors, 20.","DOI":"10.3390\/s20195606"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1078251","DOI":"10.1155\/2020\/1078251","article-title":"An Optimization Study of Estimating Blood Pressure Models Based on Pulse Arrival Time for Continuous Monitoring","volume":"2020","author":"Shao","year":"2020","journal-title":"J. Healthc. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.inffus.2019.07.001","article-title":"A chest-based continuous cuffless blood pressure method: Estimation and evaluation using multiple body sensors","volume":"54","author":"Heydari","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1109\/TIM.2018.2800539","article-title":"Arterial blood pressure estimation from local pulse wave velocity using dual-element photoplethysmograph probe","volume":"67","author":"Nabeel","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"101942","DOI":"10.1016\/j.bspc.2020.101942","article-title":"Investigation on the effect of Womersley number, ECG and PPG features for cuff less blood pressure estimation using machine learning","volume":"60","author":"Thambiraj","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2810","DOI":"10.1109\/TBME.2021.3055154","article-title":"Pulse Arrival Time Segmentation into Cardiac and Vascular Intervals\u2013Implications for Pulse Wave Velocity and Blood Pressure Estimation","volume":"68","author":"Beutel","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hsu, Y.C., Li, Y.H., Chang, C.C., and Harfiya, L.N. (2020). Generalized deep neural network model for cuffless blood pressure estimation with photoplethysmogram signal only. Sensors, 20.","DOI":"10.3390\/s20195668"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107534","DOI":"10.1016\/j.apacoust.2020.107534","article-title":"A non-invasive continuous cuffless blood pressure estimation using dynamic recurrent neural networks","volume":"170","author":"Senturk","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"El Hajj, C., and Kyriacou, P.A. (2020, January 20\u201324). Cuffless and continuous blood pressure estimation from ppg signals using recurrent neural networks. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175699"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Slapni\u010dar, G., Mlakar, N., and Lu\u0161trek, M. (2019). Blood pressure estimation from photoplethysmogram using a spectro-temporal deep neural network. Sensors, 19.","DOI":"10.3390\/s19153420"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103719","DOI":"10.1016\/j.compbiomed.2020.103719","article-title":"A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals","volume":"120","author":"Jamal","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"012089","DOI":"10.1088\/1742-6596\/1325\/1\/012089","article-title":"OGRU: An optimized gated recurrent unit neural network","volume":"1325","author":"Wang","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104877","DOI":"10.1016\/j.compbiomed.2021.104877","article-title":"Prediction of Arterial Blood Pressure Waveforms from Photoplethysmogram Signals via Fully Convolutional Neural Networks","volume":"138","author":"Cheng","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_29","unstructured":"Zhang, H. (2006). A Novel Frequency Domain Arterial Tree Model: Distributed Wave Reflections and Potential Clinical Applications. [Ph.D. Thesis, Rutgers, The State University of New Jersey]."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s13721-021-00290-x","article-title":"Stenosis diagnosis based on peripheral arterial and artificial neural network","volume":"10","author":"Li","year":"2021","journal-title":"Netw. Model. Anal. Health Inform. Bioinform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1750034","DOI":"10.4015\/S101623721750034X","article-title":"Classification of cardiac arrhythmias using arterial blood pressure based on discrete wavelet transform","volume":"29","author":"Arvanaghi","year":"2017","journal-title":"Biomed. Eng. Appl. Basis Commun."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1109\/TBME.2016.2580904","article-title":"Cuffless blood pressure estimation algorithms for continuous health-care monitoring","volume":"64","author":"Kachuee","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_34","unstructured":"van Gent, P., Farah, H., Nes, N., and van Arem, B. (2018, January 13\u201314). Heart rate analysis for human factors: Development and validation of an open source toolkit for noisy naturalistic heart rate data. Proceedings of the 6th HUMANIST Conference, Hague, The Netherlands."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez, G., Howard, N., Abbott, D., Lim, K., Ward, R., and Elgendi, M. (2018). Can photoplethysmography replace arterial blood pressure in the assessment of blood pressure?. J. Clin. Med., 7.","DOI":"10.3390\/jcm7100316"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/72.97934","article-title":"A general regression neural network","volume":"2","author":"Specht","year":"1991","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"202","DOI":"10.7763\/IJCTE.2017.V9.1138","article-title":"Cuffless blood pressure estimation based on photoplethysmography signal and its second derivative","volume":"9","author":"Liu","year":"2017","journal-title":"Int. J. Comput. Theory Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Eom, H., Lee, D., Han, S., Hariyani, Y.S., Lim, Y., Sohn, I., Park, K., and Park, C. (2020). End-to-end deep learning architecture for continuous blood pressure estimation using attention mechanism. Sensors, 20.","DOI":"10.3390\/s20082338"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Athaya, T., and Choi, S. (2021). An estimation method of continuous non-invasive arterial blood pressure waveform using photoplethysmography: A U-Net architecture-based approach. Sensors, 21.","DOI":"10.3390\/s21051867"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1161\/HYPERTENSIONAHA.117.10237","article-title":"A universal standard for the validation of blood pressure measuring devices: Association for the Advancement of Medical Instrumentation\/European Society of Hypertension\/International Organization for Standardization (AAMI\/ESH\/ISO) Collaboration Statement","volume":"71","author":"Stergiou","year":"2018","journal-title":"J. Hypertens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7207\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:23:02Z","timestamp":1760167382000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,29]]},"references-count":41,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21217207"],"URL":"https:\/\/doi.org\/10.3390\/s21217207","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,29]]}}}