Quantifying the Autonomic Nervous System Influence on Heart Rate Turbulence Using Partial Least Squares Path Modeling.

H. Puente-Díaz,Rafael García-Carretero,Rebeca Goya-Esteban,Óscar Barquero-Pérez

CinC（2022）

Department of Signal Theory and Communications | Department of Internal Medicine

Cited 0|Views18

Abstract

Heart rate turbulence (HRT) is a physiological phenomenon used for cardiac risk stratification. Its alteration or absence indicates a significantly increased likelihood of mortality. However, the influence of the autonomic nervous system (ANS) on HRT needs to be further investigated. We propose a cause-effect relationship model to quantify the influence of the ANS. A set of 481 Holter-monitor recordings from different medical conditions were used, from THEW· acute myocardial infarction, coronary artery disease and end-stage renal disease. We proposed to model the relationship between HRT and ANS using Partial Least Squares Path Modeling (PLS-PM), a method for structural equation modeling that allows analyzing the relationships between the observed data and the latent variables. HRT parameters were estimated on individual ventricular premature complex (VPC) tachograms. ANS was assessed by heart rate variability indices computed from 3-min before VPC tachograms. The data set was split into low-risk and high-risk subgroups. SDN N, P LP , TS and TO were the most relevant variables. In low-risk, ANS activity was negatively related to HRT, while correlation between coupling interval and HRT on high-risk depends on the pathology. PLS-PM suggests that the influence of physiological variables on HRT is broken on high-risk. Results of the model are in agreement with the baroreflex hypothesis.

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Key words

acute myocardial infarction,autonomic nervous system influence,cardiac risk stratification,coronary artery disease,end-stage renal disease,heart rate turbulence,heart rate variability indices,Holter-monitor recordings,HRT parameters,partial least squares path modeling,physiological phenomenon,physiological variables,structural equation modeling,ventricular premature complex tachograms

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