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Effects of Age, BMI, anxiety and stress on the parameters of a stochastic model for heart rate variability including respiratory information

Anderson, Rachele LU ; Jönsson, Peter LU and Sandsten, Maria LU (2018) 11th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2018 - Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018 4. p.17-25
Abstract

Recent studies have focused on investigating different factors that may affect heart rate variability (HRV), pointing especially to the effects of age, gender and stress level. Other findings raise the importance of considering the respiratory frequency in the analysis of HRV signals. In this study, we evaluate the effect of several covariates on the parameters of a stochastic model for HRV. The data was recorded from 47 test participants, whose breathing was controlled by following a metronome with increasing frequency. This setup allows for a controlled acquisition of respiratory related HRV data covering the frequency range in which adults breathe in different everyday situations. A stochastic model, known as Locally Stationary Chirp... (More)

Recent studies have focused on investigating different factors that may affect heart rate variability (HRV), pointing especially to the effects of age, gender and stress level. Other findings raise the importance of considering the respiratory frequency in the analysis of HRV signals. In this study, we evaluate the effect of several covariates on the parameters of a stochastic model for HRV. The data was recorded from 47 test participants, whose breathing was controlled by following a metronome with increasing frequency. This setup allows for a controlled acquisition of respiratory related HRV data covering the frequency range in which adults breathe in different everyday situations. A stochastic model, known as Locally Stationary Chirp Process, accounts for the respiratory signal information and models the HRV data. The model parameters are estimated with a novel inference method based on the separability features possessed by the process covariance function. Least square regression analysis using several available covariates is used to investigate the correlation with the stochastic model parameters. The results show statistically significant correlation of the model parameters with age, BMI, State and Trait Anxiety as well as stress level.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Chirp Respiratory Frequency, HRV, Linear, Locally Stationary Chirp Processes, Logistic Regression, Time-series Modelling, Time-varying Signals
host publication
BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018
volume
4
pages
9 pages
publisher
SciTePress
conference name
11th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2018 - Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018
conference location
Funchal, Madeira, Portugal
conference dates
2018-01-19 - 2018-01-21
external identifiers
  • scopus:85051735230
ISBN
9789897582790
DOI
10.5220/0006512900170025
language
English
LU publication?
yes
id
72a72793-2b0d-48d6-87b7-9e511c525742
date added to LUP
2018-09-13 08:53:56
date last changed
2020-01-22 07:13:06
@inproceedings{72a72793-2b0d-48d6-87b7-9e511c525742,
  abstract     = {<p>Recent studies have focused on investigating different factors that may affect heart rate variability (HRV), pointing especially to the effects of age, gender and stress level. Other findings raise the importance of considering the respiratory frequency in the analysis of HRV signals. In this study, we evaluate the effect of several covariates on the parameters of a stochastic model for HRV. The data was recorded from 47 test participants, whose breathing was controlled by following a metronome with increasing frequency. This setup allows for a controlled acquisition of respiratory related HRV data covering the frequency range in which adults breathe in different everyday situations. A stochastic model, known as Locally Stationary Chirp Process, accounts for the respiratory signal information and models the HRV data. The model parameters are estimated with a novel inference method based on the separability features possessed by the process covariance function. Least square regression analysis using several available covariates is used to investigate the correlation with the stochastic model parameters. The results show statistically significant correlation of the model parameters with age, BMI, State and Trait Anxiety as well as stress level.</p>},
  author       = {Anderson, Rachele and Jönsson, Peter and Sandsten, Maria},
  booktitle    = {BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018},
  isbn         = {9789897582790},
  language     = {eng},
  pages        = {17--25},
  publisher    = {SciTePress},
  title        = {Effects of Age, BMI, anxiety and stress on the parameters of a stochastic model for heart rate variability including respiratory information},
  url          = {http://dx.doi.org/10.5220/0006512900170025},
  doi          = {10.5220/0006512900170025},
  volume       = {4},
  year         = {2018},
}