000101182 001__ 101182
000101182 005__ 20210407143739.0
000101182 0247_ $$2doi$$a10.1109/EMBC.2019.8856374
000101182 0248_ $$2sideral$$a122970
000101182 037__ $$aART-2019-122970
000101182 041__ $$aeng
000101182 100__ $$0(orcid)0000-0002-8334-4786$$aPerez-Martinez, C.$$uUniversidad de Zaragoza
000101182 245__ $$aMultivariable relationships between autonomic nervous system related indices in hyperbaric environments
000101182 260__ $$c2019
000101182 5060_ $$aAccess copy available to the general public$$fUnrestricted
000101182 5203_ $$aThe main aim of this work is to model the relationships between parameters extracted from the heart rate variability (HRV) signal, which is derived from the electrocardiogram (ECG), at different stages of a simulated immersion in a hyperbaric chamber. The response of the Autonomic Nervous System is known to be affected by changes in atmospheric pressure, reflected in changes in the HRV signal. A dataset consisting of ECG signals from 17 subjects exposed to a controlled hyperbaric environment, simulating depths from 0 m to 40 m, was used. Both linear and nonlinear dependences of HRV parameters were analysed using linear regression and Mutual Information (entropy-based) techniques. Furthermore, relationships between parameters of the HRV signals, biophysical variables of the subjects, and atmospheric pressure changes were characterized by artificial neural networks. In particular, self-organizing maps (SOM) were trained for modelling and clustering all the data. In the mid-term, these models could be the basis to create predictive models of HRV parameters at high depths in order to increase the safety for divers by warning them if some abnormal body response could be expected just by processing the ECG signal at sea level before immersion.
000101182 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/RTI2018-097723-B-I00$$9info:eu-repo/grantAgreement/ES/UZ/CUD2018-08$$9info:eu-repo/grantAgreement/ES/UZ/UZCUD2017-TEC-04
000101182 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000101182 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000101182 700__ $$0(orcid)0000-0002-0690-3193$$aPelaez-Coca, M.D.
000101182 700__ $$0(orcid)0000-0003-2596-7237$$aHernando, A.
000101182 700__ $$0(orcid)0000-0001-7285-0715$$aGil, E.$$uUniversidad de Zaragoza
000101182 700__ $$0(orcid)0000-0002-8236-825X$$aSanchez, C.$$uUniversidad de Zaragoza
000101182 7102_ $$12002$$2385$$aUniversidad de Zaragoza$$bDpto. Física Aplicada$$cÁrea Física Aplicada
000101182 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000101182 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000101182 773__ $$g(2019), 6789-6793$$pConf. proc. (IEEE Eng. Med. Biol. Soc., Conf.)$$tConference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.)$$x1557-170X
000101182 8564_ $$s219944$$uhttps://zaguan.unizar.es/record/101182/files/texto_completo.pdf$$yPostprint
000101182 8564_ $$s3442447$$uhttps://zaguan.unizar.es/record/101182/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000101182 909CO $$ooai:zaguan.unizar.es:101182$$particulos$$pdriver
000101182 951__ $$a2021-04-07-12:58:22
000101182 980__ $$aARTICLE