Modeling volatility in heat rate variability.

Clicks: 212
ID: 69882
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #105 of 127 articles by views in conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 127 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Modeling Heart Rate Variability (HRV) data has become important for clinical applications and as a research tool. These data exhibit long memory and time-varying conditional variance (volatility). In HRV, volatility is traditionally estimated by recursive least squares combined with short memory AutoRegressive (AR) models. This work considers a parametric approach based on long memory Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with heteroscedastic errors. To model the heteroscedasticity nonlinear Generalized Autoregressive Conditionally Heteroscedastic (GARCH) and Exponential Generalized Autoregressive Conditionally Heteroscedastic (EGARCH) models are considered. The latter are necessary to model empirical characteristics of conditional volatility such as clustering and asymmetry in the response, usually called leverage in time series literature. The ARFIMA-EGARCH models are used to capture and remove long memory and characterize conditional volatility in 24 hour HRV recordings from the Noltisalis database.
Reference Key
leite2016modelingconference Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Leite, Argentina;Silva, Maria Eduarda;Rocha, Ana Paula;
Journal conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference
Year 2016
DOI
10.1109/EMBC.2016.7591502
URL
Keywords
ambient fine particles bayesian mcmc method generalized poisson integer-valued garch over-dispersion posterior odds ratio

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.