Role of editing of R–R intervals in the analysis of heart rate variability

Mirja A Peltola

doi:10.3389/fphys.2012.00148

Abstract

This paper reviews the methods used for editing of the R–R interval time series and how this editing can influence the results of heart rate (HR) variability analyses. Measurement of HR variability from short and long-term electrocardiographic (ECG) recordings is a non-invasive method for evaluating cardiac autonomic regulation. HR variability provides information about the sympathetic-parasympathetic autonomic balance. One important clinical application is the measurement of HR variability in patients suffering from acute myocardial infarction. However, HR variability signals extracted from R–R interval time series from ambulatory ECG recordings often contain different amounts of artifact. These false beats can be either of physiological or technical origin. For instance, technical artifact may result from poorly fastened electrodes or be due to motion of the subject. Ectopic beats and atrial fibrillation are examples of physiological artifact. Since ectopic and other false beats are common in the R–R interval time series, they complicate the reliable analysis of HR variability sometimes making it impossible. In conjunction with the increased usage of HR variability analyses, several studies have confirmed the need for different approaches for handling false beats present in the R–R interval time series. The editing process for the R–R interval time series has become an integral part of these analyses. However, the published literature does not contain detailed reviews of editing methods and their impact on HR variability analyses. Several different editing and HR variability signal pre-processing methods have been introduced and tested for the artifact correction. There are several approaches available, i.e., use of methods involving deletion, interpolation or filtering systems. However, these editing methods can have different effects on HR variability measures. The effects of editing are dependent on the study setting, editing method, parameters used to assess HR variability, type of study population, and the length of R–R interval time series. The purpose of this paper is to summarize these pre-processing methods for HR variability signal, focusing especially on the editing of the R–R interval time series.

Highlights

Heart rate (HR) variability quantifies the fluctuations in the time intervals between individual heart beats
Sinus rhythm oscillates around the mean heart rate (HR), which is dependent on continuous regulation by the autonomic nervous system (ANS)
This was confirmed by Tarkiainen et al (2007) who did not detect significant differences between the performance of deletion and linear interpolation in the short-term DFA analysis and other non-linear HR dynamics when the number of ectopic beats was less than 10%

Summary

INTRODUCTION

Heart rate (HR) variability quantifies the fluctuations in the time intervals between individual heart beats. Ectopic beats introduce a bias into HR variability results and represent a significant problem in the interpretation of these results (Task Force of ESC and NASPE, 1996; Berntson et al, 1997). Ectopic beats in R–R interval time series impair the reliability of the HR variability power spectrum by introducing false frequency components into the power spectrum. Several pre-processing methods for HR variability signal have been introduced. Pre-processing can involve editing of artifact by deletion, interpolations, or filtering These different editing methods may have their own distinct effects on HR variability results and one could end up with different values if the R–R interval time series have been edited by deletion or interpolation. This paper reviews the pre-processing methods for R–R interval time series as part of HR variability analysis. Common false beats occurring in R–R interval time series are described

BACKGROUND

Findings

CONCLUSION AND FUTURE WORK