Nonlinear analysis of electrodermal activity signals for healthy subjects and patients with chronic obstructive pulmonary disease

Nonlinear analysis of electrodermal activity signals for healthy subjects and patients with... It is known that signals recorded from physiological systems represent nonlinear features. Several recent studies report that quantitative information about signal complexity is obtained by using nonlinear analysis algorithms. Chronic obstructive pulmonary disease (COPD) is one of the causes of mortality worldwide with an increasing prevalence. This study aims to investigate nonlinear parameters such as largest Lyapunov exponent (LLE) and correlation dimension of electrodermal activity signals recorded from healthy subjects and patients with COPD. Electrodermal activity signals recorded from 14 healthy subjects and 24 patients with COPD were analysed. Auditory and tactile stimuli were applied at different time intervals during the recording process. Signals were reconstructed in the phase space compatible with theory and LLE and correlation dimension values were calculated. Statistical analysis was performed by using Shapiro–Wilk normality test, one-way analysis of variance (ANOVA) with Bonferroni post-test and Kruskal–Wallis non-parametric test. It was determined that the chaoticity and the complexity of the system increased in the presence of COPD. The systematic auditory stimuli increases chaoticity more than random auditory stimuli. Furthermore it was observed that participants develop habituation to the same auditory stimuli in time. There is no significant difference between COPD groups. Different results were found for the tactile stimuli applied to right or left ear. The results revealed that the nonlinear analysis of physiological data can be used for the development of new strategies for the diagnosis of chronic diseases. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Australasian Physical & Engineering Sciences in Medicine Springer Journals

Nonlinear analysis of electrodermal activity signals for healthy subjects and patients with chronic obstructive pulmonary disease

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Australasian College of Physical Scientists and Engineers in Medicine
Subject
Biomedicine; Biomedicine, general; Biological and Medical Physics, Biophysics; Medical and Radiation Physics; Biomedical Engineering
ISSN
0158-9938
eISSN
1879-5447
D.O.I.
10.1007/s13246-018-0649-4
Publisher site
See Article on Publisher Site

Abstract

It is known that signals recorded from physiological systems represent nonlinear features. Several recent studies report that quantitative information about signal complexity is obtained by using nonlinear analysis algorithms. Chronic obstructive pulmonary disease (COPD) is one of the causes of mortality worldwide with an increasing prevalence. This study aims to investigate nonlinear parameters such as largest Lyapunov exponent (LLE) and correlation dimension of electrodermal activity signals recorded from healthy subjects and patients with COPD. Electrodermal activity signals recorded from 14 healthy subjects and 24 patients with COPD were analysed. Auditory and tactile stimuli were applied at different time intervals during the recording process. Signals were reconstructed in the phase space compatible with theory and LLE and correlation dimension values were calculated. Statistical analysis was performed by using Shapiro–Wilk normality test, one-way analysis of variance (ANOVA) with Bonferroni post-test and Kruskal–Wallis non-parametric test. It was determined that the chaoticity and the complexity of the system increased in the presence of COPD. The systematic auditory stimuli increases chaoticity more than random auditory stimuli. Furthermore it was observed that participants develop habituation to the same auditory stimuli in time. There is no significant difference between COPD groups. Different results were found for the tactile stimuli applied to right or left ear. The results revealed that the nonlinear analysis of physiological data can be used for the development of new strategies for the diagnosis of chronic diseases.

Journal

Australasian Physical & Engineering Sciences in Medicine – Springer Journals

Published: May 17, 2018

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