Heart rate variability analysis in healthy subjects, patients suffering from congestive heart failure and heart transplanted patients

<p class="ResumoAbstract"><span lang="EN-US">This study aimed to find parameters to characterize heart rate variability (HRV) and discriminate healthy subjects and patients with heart diseases. The parameters used for discrimination characterize the different componen...

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Bibliographic Details
Main Authors: Argentina Leite, Maria Eduarda Silva, Ana Paula Rocha
Format: Article
Language:English
Published: Desafio Singular 2013-12-01
Series:Motricidade
Online Access:http://revistas.rcaap.pt/motricidade/article/view/1139
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Summary:<p class="ResumoAbstract"><span lang="EN-US">This study aimed to find parameters to characterize heart rate variability (HRV) and discriminate healthy subjects and patients with heart diseases. The parameters used for discrimination characterize the different components of HRV memory (short and long) and are extracted from HRV recordings using parametric as well as non parametric methods. Thus, the parameters are: spectral components at low frequencies (LH) and high frequencies (HF) which are associated with the short memory of HRV and the long memory parameter (<em>d</em>) obtained from autoregressive fractionally integrated moving average (ARFIMA) models. In the non parametric context, short memory (</span><span>α</span><sub><span lang="EN-US">1</span></sub><span lang="EN-US">) and long memory (</span><span>α</span><sub><span lang="EN-US">2</span></sub><span lang="EN-US">) parameters are obtained from detrended fluctuation analysis (DFA). The sample used in this study contains 24-hour Holter HRV recordings of 30 subjects: 10 healthy individuals, 10 patients suffering from congestive heart failure and 10 heart transplanted patients from the Noltisalis database. It was found that short memory parameters present higher values for the healthy individuals whereas long memory parameters present higher values for the diseased individuals. Moreover, there is evidence that ARFIMA modeling allows the discrimination between the 3 groups under study, being advantageous over DFA.</span></p>
ISSN:1646-107X
2182-2972