Autonomic nervous system measurement in hyperbaric environments using ECG and PPG signals

Hernando, Alberto ; Peláez-Coca, María Dolores ; Lozano, María Teresa ; Aiger, Monserrat (Universidad de Zaragoza) ; Izquierdo, David ; Sánchez, Alberto ; López-Jurado, María Isabel ; Moura, Ignacio ; Fidalgo, Joaquín ; Lázaro, Jesús (Universidad de Zaragoza) ; Gil, Eduardo (Universidad de Zaragoza)

Resumen: The main aim of this work was to characterise the Autonomic Nervous System (ANS) response in hyper- baric environments using electrocardiogram (ECG) and pulse- photoplethysmogram (PPG) signals. To that end, 26 subjects were introduced into a hyperbaric chamber and five stages with different atmospheric pressures (1 atm; descent to 3 and 5 atm; ascent to 3 and 1 atm) were recorded. Respiratory information was extracted from the ECG and PPG signals and a combined respiratory rate was studied. This information was also used to analyse Heart Rate Variability (HRV) and Pulse Rate Variability (PRV). The database was cleaned by eliminating those cases where the respiratory rate dropped into the low frequency band (LF: 0.04-0.15 Hz) and those in which there was a discrepancy between the respiratory rates estimated using the ECG and PPG signals. Classical temporal and frequency indices were calculated in such cases. The ECG results showed a time-related depen- dency, with the heart rate and sympathetic markers (normalised power in LF and LF/HF ratio) decreasing as more time was spent inside the hyperbaric environment. A dependency between the atmospheric pressure and the parasympathetic response, as reflected in the high frequency band power (HF: 0.15-0.40 Hz), was also found, with power increasing with atmospheric pressure. The combined respiratory rate also reached a maximum in the deepest stage, thus highlighting a significant difference between this stage and the first one. The PPG data gave similar findings and also allowed the oxygen saturation to be computed, therefore we propose the use of this signal for future studies in hyperbaric environments.

Idioma: Inglés

DOI: 10.1109/JBHI.2018.2797982

Año: 2019

Publicado en: IEEE journal of biomedical and health informatics 23, 1 (2019), 132-142
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
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 12 / 109 = 0.11 (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/T04
Financiación: info:eu-repo/grantAgreement/EC/H2020/745755/EU/Wearable Cardiorespiratory Monitor/WECARMON
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TEC2014-54143-P
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2014-5356-R
Financiación: info:eu-repo/grantAgreement/ES/UZ/CUD2013-11
Financiación: info:eu-repo/grantAgreement/ES/UZ/CUD2016-TEC-03
Financiación: info:eu-repo/grantAgreement/ES/UZ/CUD2016-TEC-04
Financiación: info:eu-repo/grantAgreement/ES/UZ/CUD2016-18

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Psicología Social (Dpto. Psicología y Sociología)
Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)
Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)