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. Keywords: heart rate variability, artifact, editing, R-R intervals Heart rate (HR) variability quantifies the fluctuations in the time intervals between individual heart beats. HR variability can be easily obtained, e.g., from Holter recordings, which is a non-invasive technique and has a relatively good reproducibility. The analysis of HR variability can provide insights into autonomic nervous function and provide information about the sympathetic-parasympathetic autonomic balance and cardiovascular health (Malik and Camm, 1995; Task Force of ESC and NASPE, 1996) . Numerous studies have been published during the last three decades about HR variability in many pathological conditions or during exercise or different physiological conditions. Analyses of HR variability have been considered as a way of quantifying the risk of different arrhythmic events or even death in conjunction with cardiac and non-cardiac disorders (Kleiger et al., 1987 Saul et al., 1988; Bigger et al., 1992; Nolan et al., 1998; Yoshikawa et al., 1999; Aronson and Burger, 2000; Huikuri et al., 2000; Malik et al., 2000; La Rovere et al., 2003; Aronson et al., 2004; Camm et al., 2004; Kataoka et al., 2004; Mäkikallio et al., 2005; Stein et al., 2005) . However, in most cases HR variability signals are imperfect, since they contain disturbances of technical or physiological origins. For instance, technical artifact may result from poorly fastened electrodes or be due to movement of the subject resulting missing beats or beats whose onset cannot be clearly identified. Premature beats (ectopic beats) and atrial fibrillation are examples of physiological artifact. 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) . For example, 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. As HR variability analyses have become more popular, the need for and importance of artifact correction have been emphasized (Laguna et al., 1996; Task Force of ESC and NASPE, 1996; Salo et al., 2001; Mateo and Laguna, 2003; Thuraisingham, 2006; Tarkiainen et al., 2007; Sassi and Mainardi, 2008; Colak, www.frontiersin.org Kumaravel and Santhi, 2010) . Several pre-processing methods for HR variability signal have been introduced. For example, pre-processing can involve editing of artifact by deletion, interpolations, or filtering. However, 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. The main focus is to describe the common editing methods for R-R interval time series and how they influence the results of the analysis. In addition, common false beats occurring in R-R interval time series are described. BACKGROUND OF HR VARIABILITY
However, these preprocessing methods have their own distinct effect on HRV analysis yielding different results REF .
Role of Editing of R–R Intervals in the Analysis of Heart Rate Variability
Front. Physio.
Medicine