Effect of the Heart Rate Variability Representations on the Quantification of the Cardiorespiratory Interactions during Autonomic Nervous System Blockade

Morales, J.F. ; Bolea, J. ; Van Huffel, S. ; Bailón, R. (Universidad de Zaragoza) ; Varon, C.

Resumen: The 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.

Idioma: Inglés

DOI: 10.23919/CinC49843.2019.9005818

Año: 2019

Publicado en: Computing in Cardiology 46 (2019), [4 pp]
ISSN: 2325-8861

Factor impacto SCIMAGO: 0.296 - Computer Science (miscellaneous) - Cardiology and Cardiovascular Medicine

Financiación: info:eu-repo/grantAgreement/ES/DGA-CIBER/LMP44-18
Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS
Financiación: info:eu-repo/grantAgreement/EC/FP7/339804/EU/Biomedical Data Fusion using Tensor based Blind Source Separation/BIOTENSORS
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/RTI2018-097723-B-I00

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)