000130183 001__ 130183
000130183 005__ 20241015105722.0
000130183 0247_ $$2doi$$a10.1016/j.bspc.2023.104814
000130183 0248_ $$2sideral$$a133470
000130183 037__ $$aART-2024-133470
000130183 041__ $$aeng
000130183 100__ $$0(orcid)0000-0003-2596-7237$$aHernando, Alberto
000130183 245__ $$aDecomposing photoplethysmogram waveforms into systolic and diastolic waves, with application to hyperbaric environments
000130183 260__ $$c2024
000130183 5060_ $$aAccess copy available to the general public$$fUnrestricted
000130183 5203_ $$aObjective: A new algorithm to decompose the photoplethysmogram (PPG) pulse in two waves related with the systolic and diastolic parts, was applied to identify alterations in the morphology of the PPG pulse due to the pressure.
Approach: Each pulse was decomposed into two waves: the first one a Gaussian related with the systolic peak, and the second one was modelled as a lognormal curve associated with the diastolic part. From these two waves, 13 parameters related with the width (W1, W2 and W2/W1), the time instant (T1, T2, T21, TBB), the amplitude (A1, A2, A2/A1) and the areas (D1, D2, D2/D1) were estimated. These parameters were computed from subjects inside a hyperbaric chamber, involving five stages at different pressure: 1 atm, 3 atm, 5 atm, 3 atm and 1 atm.
Main results: There was a significant increase in the values of A1, A2, W1, T1, and D1, and a decrease in the ratios when the pressure increased, that suggest a vasoconstriction when the pressure increased. There is also an increase in the values of
, T2, T12, and W2 along the protocol, that implies a dependency of some parameters with the pulse-to-pulse interval.
Significance: This methodology allows extracting a large set of parameters related with the PPG morphology that are affected by the change of pressure inside hyperbaric environments.
000130183 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T39-20R-BSICoS group$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/PGC2018-095936-B-I00$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/RTI2018-097723-B-I00$$9info:eu-repo/grantAgreement/ES/UZ/CUD2020-TEC-03$$9info:eu-repo/grantAgreement/ES/UZ/CUD2020-11
000130183 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000130183 590__ $$a4.9$$b2023
000130183 592__ $$a1.284$$b2023
000130183 591__ $$aENGINEERING, BIOMEDICAL$$b30 / 122 = 0.246$$c2023$$dQ1$$eT1
000130183 593__ $$aBiomedical Engineering$$c2023$$dQ1
000130183 593__ $$aSignal Processing$$c2023$$dQ1
000130183 593__ $$aHealth Informatics$$c2023$$dQ1
000130183 594__ $$a9.8$$b2023
000130183 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000130183 700__ $$0(orcid)0000-0002-0690-3193$$aPeláez-Coca, María Dolores
000130183 700__ $$0(orcid)0000-0001-7285-0715$$aGil, Eduardo$$uUniversidad de Zaragoza
000130183 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000130183 773__ $$g88, C (2024), 104814 [9 pp.]$$pBiomed. signal proces. control$$tBiomedical Signal Processing and Control$$x1746-8094
000130183 8564_ $$s1502994$$uhttps://zaguan.unizar.es/record/130183/files/texto_completo.pdf$$yPostprint$$zinfo:eu-repo/semantics/openAccess
000130183 8564_ $$s3433874$$uhttps://zaguan.unizar.es/record/130183/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint$$zinfo:eu-repo/semantics/openAccess
000130183 909CO $$ooai:zaguan.unizar.es:130183$$particulos$$pdriver
000130183 951__ $$a2024-10-15-10:56:34
000130183 980__ $$aARTICLE