{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:03:23Z","timestamp":1767650603044,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad Tecnol\u00f3gica Nacional","award":["ICUTIBA7647","ICTCABA8443"],"award-info":[{"award-number":["ICUTIBA7647","ICTCABA8443"]}]},{"name":"ML-Cardyn project","award":["ICUTIBA7647","ICTCABA8443"],"award-info":[{"award-number":["ICUTIBA7647","ICTCABA8443"]}]},{"name":"European Union","award":["ICUTIBA7647","ICTCABA8443"],"award-info":[{"award-number":["ICUTIBA7647","ICTCABA8443"]}]},{"name":"European Regional Development Fund (FEDER)","award":["ICUTIBA7647","ICTCABA8443"],"award-info":[{"award-number":["ICUTIBA7647","ICTCABA8443"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Arterial stiffness is a major condition related to many cardiovascular diseases. Traditional approaches in the assessment of arterial stiffness supported by machine learning techniques are limited to the pulse wave velocity (PWV) estimation based on pressure signals from the peripheral arteries. Nevertheless, arterial stiffness can be assessed based on the pressure\u2013strain relationship by analyzing its hysteresis loop. In this work, the capacity of deep learning models based on generative adversarial networks (GANs) to transfer pressure signals from the peripheral arterial region to pressure and area signals located in the central arterial region is explored. The studied signals are from a public and validated virtual database. Compared to other works in which the assessment of arterial stiffness was performed via PWV, in the present work the pressure\u2013strain hysteresis loop is reconstructed and evaluated in terms of classical machine learning metrics and clinical parameters. Least-square GAN (LSGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) adversarial losses are compared, yielding better results with LSGAN. LSGAN mean \u00b1 standard deviation of error for pressure and area pulse waveforms are 0.8 \u00b1 0.4 mmHg and 0.1 \u00b1 0.1 cm2, respectively. Regarding the pressure\u2013strain elastic modulus, it is achieved a mean absolute percentage error of 6.5 \u00b1 5.1%. GAN-based deep learning models can recover the pressure\u2013strain loop of central arteries while observing pressure signals from peripheral arteries.<\/jats:p>","DOI":"10.3390\/s23031559","type":"journal-article","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T01:36:59Z","timestamp":1675215419000},"page":"1559","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach"],"prefix":"10.3390","volume":"23","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, Buenos Aires C1179AAQ, Argentina"},{"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, Buenos Aires C1179AAQ, Argentina"}]},{"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\/0000-0002-7835-2845","authenticated-orcid":false,"given":"Eugenia","family":"Ipar","sequence":"additional","affiliation":[{"name":"GIBIO, Facultad Regional Buenos Aires, Universidad Tecnol\u00f3gica Nacional, 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, Buenos Aires C1179AAQ, Argentina"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,1]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2022, May 19). 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