{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T02:51:21Z","timestamp":1777949481812,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T00:00:00Z","timestamp":1616112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["ML-CARDYN"],"award-info":[{"award-number":["ML-CARDYN"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Universidad Tecnologica Nacional","award":["ICUTIBA7647"],"award-info":[{"award-number":["ICUTIBA7647"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable information about the subject\u2019s health. After studying its morphology it is possible to diagnose cardiovascular diseases such as hypertension, so ABP routine control is recommended. The most common method of controlling ABP is the cuff-based method, from which it is obtained only the systolic and diastolic blood pressure (SBP and DBP, respectively). This paper proposes a cuff-free method to estimate the morphology of the average ABP pulse (ABPM\u00af) through a deep learning model based on a seq2seq architecture with attention mechanism. It only needs raw photoplethysmogram signals (PPG) from the finger and includes the capacity to integrate both categorical and continuous demographic information (DI). The experiments were performed on more than 1100 subjects from the MIMIC database for which their corresponding age and gender were consulted. Without allowing the use of data from the same subjects to train and test, the mean absolute errors (MAE) were 6.57 \u00b1 0.20 and 14.39 \u00b1 0.42 mmHg for DBP and SBP, respectively. For ABPM\u00af, R correlation coefficient and the MAE were 0.98 \u00b1 0.001 and 8.89 \u00b1 0.10 mmHg. In summary, this methodology is capable of transforming PPG into an ABP pulse, which obtains better results when DI of the subjects is used, potentially useful in times when wireless devices are becoming more popular.<\/jats:p>","DOI":"10.3390\/s21062167","type":"journal-article","created":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T23:47:41Z","timestamp":1616370461000},"page":"2167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":98,"title":["Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5036-699X","authenticated-orcid":false,"given":"Nicolas","family":"Aguirre","sequence":"first","affiliation":[{"name":"GIBIO, Facultad Regional Buenos Aires, Universidad Tecnol\u00f3gica Nacional, Ciudad Aut\u00f3noma Buenos Aires C1179AAQ, Argentina"},{"name":"LIST3N, Universit\u00e9 de Technologie de Troyes, 10004 Troyes, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8995-035X","authenticated-orcid":false,"given":"Edith","family":"Grall-Ma\u00ebs","sequence":"additional","affiliation":[{"name":"LIST3N, Universit\u00e9 de Technologie de Troyes, 10004 Troyes, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0217-1239","authenticated-orcid":false,"given":"Leandro J.","family":"Cymberknop","sequence":"additional","affiliation":[{"name":"GIBIO, Facultad Regional Buenos Aires, Universidad Tecnol\u00f3gica Nacional, Ciudad Aut\u00f3noma Buenos Aires C1179AAQ, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3455-2033","authenticated-orcid":false,"given":"Ricardo L.","family":"Armentano","sequence":"additional","affiliation":[{"name":"GIBIO, Facultad Regional Buenos Aires, Universidad Tecnol\u00f3gica Nacional, Ciudad Aut\u00f3noma Buenos Aires C1179AAQ, Argentina"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,19]]},"reference":[{"key":"ref_1","first-page":"e139","article-title":"Heart Disease and Stroke Statistics\u20142020 Update: A Report From the American Heart Association","volume":"141","author":"Alvaro","year":"2020","journal-title":"Circulation"},{"key":"ref_2","unstructured":"World Health Organization (2020, October 20). Hypertension. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/hypertension."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0967-3334\/31\/1\/R01","article-title":"Arterial blood pressure measurement and pulse wave analysis\u2014Theitr role in enhancing cardiovascular assessment","volume":"31","author":"Avolio","year":"2010","journal-title":"Physiol. Meas."},{"key":"ref_4","unstructured":"Salvi, P. (2012). Pulse Waves: How Vascular Hemodynamics Affects Blood Pressure, Springer International Publishing. [2nd ed.]."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/S0895-7061(01)02319-6","article-title":"Clinical applications of arterial stiffness; definitions and reference values","volume":"15","author":"Staessen","year":"2002","journal-title":"Am. J. Hypertens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1161\/hy1001.096106","article-title":"Prospective Evaluation of a Method for Estimating Ascending Aortic Pressure From the Radial Artery Pressure Waveform","volume":"38","year":"2001","journal-title":"Hypertension"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1161\/CIRCULATIONAHA.105.595496","article-title":"Differential Impact of Blood Pressure\u2014Lowering Drugs on Central Aortic Pressure and Clinical Outcomes","volume":"113","author":"Bryan","year":"2006","journal-title":"Circulation"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.amjhyper.2006.09.019","article-title":"Indices of Pulse Wave Analysis Are Better Predictors of Left Ventricular Mass Reduction Than Cuff Pressure","volume":"20","author":"Hashimoto","year":"2007","journal-title":"Am. J. Hypertens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"460","DOI":"10.4065\/mcp.2009.0336","article-title":"Noninvasive Measurement of Central Vascular Pressures With Arterial Tonometry: Clinical Revival of the Pulse Pressure Waveform?","volume":"Volume 85","author":"Nelson","year":"2010","journal-title":"Mayo Clinic Proceedings"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1136\/hrt.2005.084145","article-title":"Prolonged mechanical systole and increased arterial wave reflections in diastolic dysfunction","volume":"92","author":"Weber","year":"2006","journal-title":"Heart"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1291\/hypres.31.1109","article-title":"Aortic Pressure Augmentation as a Marker of Cardiovascular Risk in Obstructive Sleep Apnea Syndrome","volume":"31","author":"Noda","year":"2008","journal-title":"Hypertens. Res."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1109\/TBME.2015.2441951","article-title":"Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice","volume":"62","author":"Mukkamala","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1177\/016173467900100406","article-title":"Measurements of Young\u2019s modulus of elasticity of the canine aorta with ultrasound","volume":"1","author":"Hughes","year":"1979","journal-title":"Ultrason. Imaging"},{"key":"ref_15","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_16","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_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-019-0136-7","article-title":"The use of photoplethysmography for assessing hypertension","volume":"2","author":"Elgendi","year":"2019","journal-title":"NPJ Digit. Med."},{"key":"ref_18","unstructured":"Chan, K., Hung, K., and Zhang, Y. (2001, January 25\u201328). Noninvasive and cuffless measurements of blood pressure for telemedicine. Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey."},{"key":"ref_19","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_20","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 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Minneapolis, MN, USA.","DOI":"10.1109\/I2MTC.2013.6555424"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chowdhury, M.H., Shuzan, M.N.I., Chowdhury, M.E.H., Mahbub, Z.B., Uddin, M.M., Khandakar, A., and Reaz, M.B.I. (2020). Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques. Sensors, 20.","DOI":"10.3390\/s20113127"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.artmed.2011.05.001","article-title":"Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques","volume":"53","year":"2011","journal-title":"Artif. Intell. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.1007\/s00134-013-2964-2","article-title":"Innovative continuous non-invasive cuffless blood pressure monitoring based on photoplethysmography technology","volume":"39","author":"Ribas","year":"2013","journal-title":"Intensive Care Med."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Liang, Y., Chen, Z., Ward, R., and Elgendi, M. (2018). Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification. Biosensors, 8.","DOI":"10.3390\/bios8040101"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Sideris, C., Kalantarian, H., Nemati, E., and Sarrafzadeh, M. (2016, January 18\u201320). Building Continuous Arterial Blood Pressure Prediction Models Using Recurrent Networks. Proceedings of the 2016 IEEE International Conference on Smart Computing (SMARTCOMP), St. Louis, MO, USA.","DOI":"10.1109\/SMARTCOMP.2016.7501681"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sadrawi, M., Lin, Y.T., Lin, C.H., Mathunjwa, B., Fan, S.Z., Abbod, M.F., and Shieh, J.S. (2020). Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography. Sensors, 20.","DOI":"10.3390\/s20143829"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hosanee, M., Chan, G., Welykholowa, K., Cooper, R., Kyriacou, P.A., Zheng, D., Allen, J., Abbott, D., Menon, C., and Lovell, N.H. (2020). Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring. J. Clin. Med., 9.","DOI":"10.3390\/jcm9030723"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"101870","DOI":"10.1016\/j.bspc.2020.101870","article-title":"A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure","volume":"58","author":"Kyriacou","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"160035","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci. Data"},{"key":"ref_32","first-page":"e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet","volume":"101","author":"Leon","year":"2020","journal-title":"Circulation"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Elgendi, M. (2016). Optimal Signal Quality Index for Photoplethysmogram Signals. Bioengineering, 3.","DOI":"10.3390\/bioengineering3040021"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.bspc.2009.06.002","article-title":"On an automatic delineator for arterial blood pressure waveforms","volume":"5","author":"Li","year":"2010","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_37","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Luong, M.T., Pham, H., and Manning, C.D. (2015). Effective Approaches to Attention-based Neural Machine Translation. arXiv.","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017). Densely Connected Convolutional Networks. arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_40","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_41","unstructured":"Clevert, D.A., Unterthiner, T., and Hochreiter, S. (2015). Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). arXiv."},{"key":"ref_42","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_43","unstructured":"Saxe, A.M., McClelland, J.L., and Ganguli, S. (2013). Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv."},{"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":"180076","DOI":"10.1038\/sdata.2018.76","article-title":"An optimal filter for short photoplethysmogram signals","volume":"5","author":"Liang","year":"2018","journal-title":"Sci. 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