Physiological dynamics of heart rate variability: a statistical modeling approach in vasovagal syncope

Authors

  • Maria Alexandra Seco
  • Rui Pinto

DOI:

https://doi.org/10.29352/mill0201.03.00049

Keywords:

vasovagal syncope, heart rate, mixed models

Abstract

Introduction: The transitory loss of conscience and postural tone followed by rapid recovery is defined as syncope. Recently has been given attention to a central mediated syncope with drop of systemic pressure, a condition known as vasovagal syncope (VVS).

Objectives: The analysis of Heart Rate Variability (HRV) is one of the main strategies to study VVS during standard protocols (e.g. Tilt Test). The main objective in this work is to understand the relative power of several physiological variables - Diastolic and Systolic Blood Pressure, (dBP) and (sBP), Stroke Volume (SV) and Total Peripheral Resistance (TPR) in Heart Rate Variability (HRV) signal.

Methods: Statistical mixed models were used to model the behavior of the above variables in HRV. Data with more than one thousand and five hundred observations from four patients with VVS were used and previously tested with classical spectral analysis for basal (LF/HF=3.01) and tilt phases (LF/HF=0.64), indicating a vagal predominance in the tilt period.

Results: Statistical models reveal, in Model 1, a major role in dBP and a low influence from SV, in the tilt phase, concerning HRV. In Model 2 TPR disclose a low HRV influence in the tilt phase among VVS patients.

Conclusions: HRV is influenced by a set of physiological variables, whose individual contribution can be assessed to understand heart rate fluctuations. In this work, the use of statistical models put forward the importance of studying the role of dBP and SV in VVS.

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How to Cite

Seco, M. A., & Pinto, R. (2016). Physiological dynamics of heart rate variability: a statistical modeling approach in vasovagal syncope. Millenium - Journal of Education, Technologies, and Health, (1), 39–47. https://doi.org/10.29352/mill0201.03.00049

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Section

Life and Healthcare Sciences