000121299 001__ 121299
000121299 005__ 20240319080949.0
000121299 0247_ $$2doi$$a10.1016/j.cmpb.2021.106527
000121299 0248_ $$2sideral$$a125984
000121299 037__ $$aART-2022-125984
000121299 041__ $$aeng
000121299 100__ $$0(orcid)0000-0003-2596-7237$$aHernando, A.
000121299 245__ $$aAutonomic Nervous System characterization in hyperbaric environments considering respiratory component and non-linear analysis of Heart Rate Variability
000121299 260__ $$c2022
000121299 5060_ $$aAccess copy available to the general public$$fUnrestricted
000121299 5203_ $$aObjectives: an evaluation of Principal Dynamic Mode (PDM) and Orthogonal Subspace Projection (OSP) methods to characterize the Autonomic Nervous System (ANS) response in three different hyperbaric environments was performed. Methods: ECG signals were recorded in two different stages (baseline and immersion) in three different hyperbaric environments: (a) inside a hyperbaric chamber, (b) in a controlled sea immersion, (c) in a real reservoir immersion. Time-domain parameters were extracted from the RR series of the ECG. From the Heart Rate Variability signal (HRV), classic Power Spectral Density (PSD), PDM (a non-linear analysis of HRV which is able to separate sympathetic and parasympathetic activities) and OSP (an analysis of HRV which is able to extract the respiratory component) methods were used to assess the ANS response. Results: PDM and OSP parameters follows the same trend when compared to the PSD ones for the hyperbaric chamber dataset. Comparing the three hyperbaric scenarios, significant differences were found: i) heart rate decreased and RMSSD increased in the hyperbaric chamber and the controlled dive, but they had the opposite behavior during the uncontrolled dive; ii) power in the OSP respiratory component was lower than power in the OSP residual component in cases a and c; iii) PDM and OSP methods showed a significant increase in sympathetic activity during both dives, but parasympathetic activity increased only during the uncontrolled dive. Conclusions: PDM and OSP methods could be used as an alternative measurement of ANS response instead of the PSD method. OSP results indicate that most of the variation in the heart rate variability cannot be described by changes in the respiration, so changes in ANS response can be assigned to other factors. Time-domain parameters reflect vagal activation in the hyperbaric chamber and in the controlled dive because of the effect of pressure. In the uncontrolled dive, sympathetic activity seems to be dominant, due to the effects of other factors such as physical activity, the challenging environment, and the influence of breathing through the scuba mask during immersion. In sum, a careful description of the changes in all the possible factors that could affect the ANS response between baseline and immersion stages in hyperbaric environments is needed for better interpretation of the results.
000121299 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T39-20R$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/PGC2018-095936-B-I00$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/RTI2018-097723-B-I00$$9info:eu-repo/grantAgreement/ES/UZ/CUD2019-10$$9info:eu-repo/grantAgreement/ES/UZ/UZCUD2019-TEC-01
000121299 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000121299 590__ $$a6.1$$b2022
000121299 592__ $$a1.118$$b2022
000121299 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b25 / 110 = 0.227$$c2022$$dQ1$$eT1
000121299 593__ $$aComputer Science Applications$$c2022$$dQ1
000121299 591__ $$aMEDICAL INFORMATICS$$b7 / 31 = 0.226$$c2022$$dQ1$$eT1
000121299 593__ $$aSoftware$$c2022$$dQ1
000121299 591__ $$aENGINEERING, BIOMEDICAL$$b22 / 96 = 0.229$$c2022$$dQ1$$eT1
000121299 593__ $$aHealth Informatics$$c2022$$dQ1
000121299 591__ $$aCOMPUTER SCIENCE, THEORY & METHODS$$b15 / 111 = 0.135$$c2022$$dQ1$$eT1
000121299 594__ $$a10.1$$b2022
000121299 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000121299 700__ $$aPosada Quintero, H.
000121299 700__ $$0(orcid)0000-0002-0690-3193$$aPeláez Coca, M. D.
000121299 700__ $$0(orcid)0000-0001-7285-0715$$aGil, E.$$uUniversidad de Zaragoza
000121299 700__ $$aChon, K. H.
000121299 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000121299 773__ $$g214 (2022), 106527 [13 pp.]$$pComput. methods programs biomed.$$tComputer Methods and Programs in Biomedicine$$x0169-2607
000121299 8564_ $$s1164095$$uhttps://zaguan.unizar.es/record/121299/files/texto_completo.pdf$$yPostprint
000121299 8564_ $$s3561512$$uhttps://zaguan.unizar.es/record/121299/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000121299 909CO $$ooai:zaguan.unizar.es:121299$$particulos$$pdriver
000121299 951__ $$a2024-03-18-12:52:35
000121299 980__ $$aARTICLE