000069982 001__ 69982
000069982 005__ 20221020131653.0
000069982 0247_ $$2doi$$a10.1109/JBHI.2018.2797982
000069982 0248_ $$2sideral$$a105486
000069982 037__ $$aART-2019-105486
000069982 041__ $$aeng
000069982 100__ $$0(orcid)0000-0003-2596-7237$$aHernando, Alberto
000069982 245__ $$aAutonomic nervous system measurement in hyperbaric environments using ECG and PPG signals
000069982 260__ $$c2019
000069982 5060_ $$aAccess copy available to the general public$$fUnrestricted
000069982 5203_ $$aThe main aim of this work was to characterise the Autonomic Nervous System (ANS) response in hyper- baric environments using electrocardiogram (ECG) and pulse- photoplethysmogram (PPG) signals. To that end, 26 subjects were introduced into a hyperbaric chamber and five stages with different atmospheric pressures (1 atm; descent to 3 and 5 atm; ascent to 3 and 1 atm) were recorded. Respiratory information was extracted from the ECG and PPG signals and a combined respiratory rate was studied. This information was also used to analyse Heart Rate Variability (HRV) and Pulse Rate Variability (PRV). The database was cleaned by eliminating those cases where the respiratory rate dropped into the low frequency band (LF: 0.04-0.15 Hz) and those in which there was a discrepancy between the respiratory rates estimated using the ECG and PPG signals. Classical temporal and frequency indices were calculated in such cases. The ECG results showed a time-related depen- dency, with the heart rate and sympathetic markers (normalised power in LF and LF/HF ratio) decreasing as more time was spent inside the hyperbaric environment. A dependency between the atmospheric pressure and the parasympathetic response, as reflected in the high frequency band power (HF: 0.15-0.40 Hz), was also found, with power increasing with atmospheric pressure. The combined respiratory rate also reached a maximum in the deepest stage, thus highlighting a significant difference between this stage and the first one. The PPG data gave similar findings and also allowed the oxygen saturation to be computed, therefore we propose the use of this signal for future studies in hyperbaric environments.
000069982 536__ $$9info:eu-repo/grantAgreement/ES/UZ/CUD2016-18$$9info:eu-repo/grantAgreement/ES/UZ/CUD2016-TEC-04$$9info:eu-repo/grantAgreement/ES/UZ/CUD2016-TEC-03$$9info:eu-repo/grantAgreement/ES/UZ/CUD2013-11$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2014-5356-R$$9info:eu-repo/grantAgreement/ES/MINECO/TEC2014-54143-P$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 745755-WECARMON$$9info:eu-repo/grantAgreement/EC/H2020/745755/EU/Wearable Cardiorespiratory Monitor/WECARMON$$9info:eu-repo/grantAgreement/ES/DGA/T04
000069982 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000069982 590__ $$a5.223$$b2019
000069982 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b15 / 156 = 0.096$$c2019$$dQ1$$eT1
000069982 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b5 / 59 = 0.085$$c2019$$dQ1$$eT1
000069982 591__ $$aMEDICAL INFORMATICS$$b1 / 27 = 0.037$$c2019$$dQ1$$eT1
000069982 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b12 / 109 = 0.11$$c2019$$dQ1$$eT1
000069982 592__ $$a1.306$$b2019
000069982 593__ $$aBiotechnology$$c2019$$dQ1
000069982 593__ $$aHealth Information Management$$c2019$$dQ1
000069982 593__ $$aElectrical and Electronic Engineering$$c2019$$dQ1
000069982 593__ $$aComputer Science Applications$$c2019$$dQ1
000069982 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000069982 700__ $$0(orcid)0000-0002-0690-3193$$aPeláez-Coca, María Dolores
000069982 700__ $$0(orcid)0000-0003-0630-4366$$aLozano, María Teresa
000069982 700__ $$0(orcid)0000-0002-8953-0600$$aAiger, Monserrat$$uUniversidad de Zaragoza
000069982 700__ $$0(orcid)0000-0002-4746-3139$$aIzquierdo, David
000069982 700__ $$0(orcid)0000-0002-4434-2630$$aSánchez, Alberto
000069982 700__ $$aLópez-Jurado, María Isabel
000069982 700__ $$aMoura, Ignacio
000069982 700__ $$aFidalgo, Joaquín
000069982 700__ $$0(orcid)0000-0001-8742-0072$$aLázaro, Jesús$$uUniversidad de Zaragoza
000069982 700__ $$0(orcid)0000-0001-7285-0715$$aGil, Eduardo$$uUniversidad de Zaragoza
000069982 7102_ $$14009$$2740$$aUniversidad de Zaragoza$$bDpto. Psicología y Sociología$$cÁrea Psicología Social
000069982 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000069982 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000069982 773__ $$g23, 1 (2019), 132-142$$pIEEE j. biomed. health inform.$$tIEEE journal of biomedical and health informatics$$x2168-2194
000069982 8564_ $$s298725$$uhttps://zaguan.unizar.es/record/69982/files/texto_completo.pdf$$yPostprint
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000069982 951__ $$a2022-10-20-13:05:22
000069982 980__ $$aARTICLE