Unconstrained estimation of HRV indices after removing respiratory influences from heart rate
Financiación H2020 / H2020 FundsFinanciación FP7 / Fp7 Funds
Resumen: Objective: This paper proposes an approach to better estimate the sympathovagal balance (SB) and the respiratory sinus arrhythmia (RSA) after separating respiratory influences from the heart rate (HR).
Methods: The separation is performed using orthogonal subspace projections and the approach is first tested using simulated HR and respiratory signals with different spectral properties. Then, RSA and SB are estimated during autonomic blockade and stress using the proposed approach and the classical heart rate variability (HRV) analysis. Both real and ECG-derived respiration (EDR) are used and the reliability of the EDR is evaluated.
Results: Mean absolute percentage errors lower than 1% were obtained after removing previously known respiratory signals from simulated HR. The proposed indices were able to improve the quantification of SB during autonomic withdrawal. In the stress data, differences ( $p < 0.003 ) among relaxed and stressful phases were found with the proposed approach, using both the real respiration and the EDR, but they disappeared when using the classical HRV.
Conclusion: A better assessment of the autonomic nervous system' response to pharmacological blockade and stress can be achieved after removing respiratory influences from HR, and this can be done using either the real respiration or the EDR. Significance: This work can be used to better identify vagal withdrawal and increased sympathetic activation when the classical HRV analysis fails due to the respiratory influences on HR. Furthermore, it can be computed using only the ECG, which is an advantage when developing wearable systems with limited number of sensors.

Idioma: Inglés
DOI: 10.1109/JBHI.2018.2884644
Año: 2019
Publicado en: IEEE journal of biomedical and health informatics 23, 6 (2019), 2386-2397
ISSN: 2168-2194

Factor impacto JCR: 5.223 (2019)
Categ. JCR: COMPUTER SCIENCE, INFORMATION SYSTEMS rank: 15 / 156 = 0.096 (2019) - Q1 - T1
Categ. JCR: MATHEMATICAL & COMPUTATIONAL BIOLOGY rank: 5 / 59 = 0.085 (2019) - Q1 - T1
Categ. JCR: MEDICAL INFORMATICS rank: 1 / 27 = 0.037 (2019) - Q1 - T1

Factor impacto SCIMAGO: 1.306 - Biotechnology (Q1) - Health Information Management (Q1) - Electrical and Electronic Engineering (Q1) - Computer Science Applications (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/T96
Financiación: info:eu-repo/grantAgreement/EC/FP7/339804/EU/Biomedical Data Fusion using Tensor based Blind Source Separation/BIOTENSORS
Financiación: info:eu-repo/grantAgreement/EC/H2020/745755/EU/Wearable Cardiorespiratory Monitor/WECARMON
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/CIBER-BBN
Financiación: info:eu-repo/grantAgreement/ES/UZ/UZ2018-TEC-05
Tipo y forma: Article (PostPrint)
Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)
Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)

Rights Reserved All rights reserved by journal editor

Exportado de SIDERAL (2020-07-16-09:07:05)

Este artículo se encuentra en las siguientes colecciones:

 Record created 2019-12-12, last modified 2020-07-16

Rate this document:

Rate this document:
(Not yet reviewed)