TY - JOUR
T1 - Removal of respiratory influences from heart rate during emotional stress
AU - Varon, Carolina
AU - Lázaro, Jesús
AU - Hernando, Alberto
AU - Caicedo, Alexander
AU - Van Huffel, Sabine
AU - Bailón, Raquel
N1 - Funding Information:
This work is supported by: OSA+. iMinds Medical Information Technologies: ICON HBC.2016.0167. European Research Council: The research leading to these results has received funding from the European Research Table 1. Median±MAD of the frequency domain parameters for each stress phase, and derived from the respiratory component YX and the residual component YX⊥.
Funding Information:
Council under the European Union’s Seventh Frame-work Programme (FP7/2007-2013) / ERC Advanced Grant: BIOTENSORS (n 339804). TIN2014-53567-R (MINECO, Spain), T96 (Government of Aragon and European Social Fund), CIBER-BBN (Instituto de Salud Carlos III and FEDER). This paper reflects only the authors’ views and the Union is not liable for any use that may be made of the contained information. Carolina Varon is a postdoctoral fellow of the Research Foundation-Flanders (FWO).
Publisher Copyright:
© 2017 IEEE Computer Society. All rights reserved.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017
Y1 - 2017
N2 - Heart rate variability (HRV) has been proposed as an indicator of stress. However, respiratory changes affect the spectral content of the HRV, resulting in a misleading estimation of stress, especially when the respiratory rate falls into the classical low frequency band. To overcome this limitation of the classical HRV analysis, this study decomposes the HRV signal, recorded during different phases of acute emotional stress, into two components using orthogonal subspace projections (OSP). One component describes all linear respiratory influences, and the other one contains all residual HRV dynamics. Two subspace definitions are compared here, on the one hand, the original respiration signal, and on the other hand, its wavelet decomposition. After a multicomparison test, no difference was found between the respiratory components derived using both subspaces, hence, no added value is achieved by the wavelet decomposition. Furthermore, the HRV variations that are linearly related to respiration are significantly different (p < 0.008) between relax and emotional stress. This suggests that respiratory dynamics are enough to detect emotional stress, which might result in an improved assessment of stress.
AB - Heart rate variability (HRV) has been proposed as an indicator of stress. However, respiratory changes affect the spectral content of the HRV, resulting in a misleading estimation of stress, especially when the respiratory rate falls into the classical low frequency band. To overcome this limitation of the classical HRV analysis, this study decomposes the HRV signal, recorded during different phases of acute emotional stress, into two components using orthogonal subspace projections (OSP). One component describes all linear respiratory influences, and the other one contains all residual HRV dynamics. Two subspace definitions are compared here, on the one hand, the original respiration signal, and on the other hand, its wavelet decomposition. After a multicomparison test, no difference was found between the respiratory components derived using both subspaces, hence, no added value is achieved by the wavelet decomposition. Furthermore, the HRV variations that are linearly related to respiration are significantly different (p < 0.008) between relax and emotional stress. This suggests that respiratory dynamics are enough to detect emotional stress, which might result in an improved assessment of stress.
UR - http://www.scopus.com/inward/record.url?scp=85045089302&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045089302&partnerID=8YFLogxK
U2 - 10.22489/CinC.2017.264-160
DO - 10.22489/CinC.2017.264-160
M3 - Conference article
AN - SCOPUS:85045089302
SN - 2325-8861
VL - 44
SP - 1
EP - 4
JO - Computing in Cardiology
JF - Computing in Cardiology
T2 - 44th Computing in Cardiology Conference, CinC 2017
Y2 - 24 September 2017 through 27 September 2017
ER -