000098283 001__ 98283
000098283 005__ 20230914083306.0
000098283 0247_ $$2doi$$a10.23919/CinC49843.2019.9005818
000098283 0248_ $$2sideral$$a122001
000098283 037__ $$aART-2019-122001
000098283 041__ $$aeng
000098283 100__ $$aMorales, J.F.
000098283 245__ $$aEffect of the Heart Rate Variability Representations on the Quantification of the Cardiorespiratory Interactions during Autonomic Nervous System Blockade
000098283 260__ $$c2019
000098283 5060_ $$aAccess copy available to the general public$$fUnrestricted
000098283 5203_ $$aThe Heart Rate Variability (HRV) is a noninvasive tool to evaluate the activity of the autonomic nervous system. To study the HRV, different mathematical representations can be used. The selection of a representation might have an effect on the evaluation of the mechanisms that modulate the Heart Rate (HR). One of these mechanisms is the Respiratory Sinus Arrhythmia (RSA), i.e. an increased HR during inhalation and a decreased HR during exhalation. Different methods exist to quantify the RSA. A common approach is to calculate the power in the High Frequency (HF, 0.15 - 0.4 Hz) band of the spectrum of the HRV representation. More recently proposed methods use the respiratory signals to estimate the strength of the RSA.This paper studies the effect of the HRV representations on the quantification of the RSA. To this end, an experiment is used in which the sympathetic and parasympathetic branches of the autonomic nervous system are selectively blocked. Three different HRV representations are considered. Afterwards, the strength of the RSA is estimated using three approaches, namely the spectral content in the HF band of the HRV representations, orthogonal subspace projections and a time-frequency representation.The results suggest that the selection of an HRV representation does not have a significant impact on the RSA estimates in a healthy population.
000098283 536__ $$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/RTI2018-097723-B-I00$$9info:eu-repo/grantAgreement/EC/FP7/339804/EU/Biomedical Data Fusion using Tensor based Blind Source Separation/BIOTENSORS$$9info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS$$9info:eu-repo/grantAgreement/ES/DGA-CIBER/LMP44-18
000098283 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000098283 592__ $$a0.296$$b2019
000098283 593__ $$aComputer Science (miscellaneous)$$c2019
000098283 593__ $$aCardiology and Cardiovascular Medicine$$c2019
000098283 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000098283 700__ $$aBolea, J.
000098283 700__ $$aVan Huffel, S.
000098283 700__ $$0(orcid)0000-0003-1272-0550$$aBailón, R.$$uUniversidad de Zaragoza
000098283 700__ $$aVaron, C.
000098283 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000098283 773__ $$g46 (2019), [4 pp]$$pComput. cardiol.$$tComputing in Cardiology$$x2325-8861
000098283 8564_ $$s190014$$uhttps://zaguan.unizar.es/record/98283/files/texto_completo.pdf$$yVersión publicada
000098283 8564_ $$s2779947$$uhttps://zaguan.unizar.es/record/98283/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000098283 909CO $$ooai:zaguan.unizar.es:98283$$particulos$$pdriver
000098283 951__ $$a2023-09-13-10:56:09
000098283 980__ $$aARTICLE