Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial

Abstract

The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiograms (ECG) in research and advancements in sensor technology, the analytical approach and steps applied to obtain HRV measures can be seen as complex. Thus, this poses a challenge to users who may not have the adequate background knowledge to obtain the HRV indices reliably. To maximize the impact of HRV-related research and its reproducibility, parallel advances in users’ understanding of the indices and the standardization of analysis pipelines in its utility will be crucial. This paper addresses this gap and aims to provide an overview of the most up-to-date and commonly used HRV indices, as well as common research areas in which these indices have proven to be very useful, particularly in psychology. In addition, we also provide a step-by-step guide on how to perform HRV analysis using an integrative neurophysiological toolkit, NeuroKit2.

Analytic and Holistic Thinkers: Differences in the Dynamics of Heart Rate Complexity When Solving a Cognitive Task in Field-Dependent and Field-Independent Conditions

“…Our previous studies (Bakhchina et al, 2018;Arutyunova et al, 2020) demonstrate that, out of existing and widely used HRV indexes, non-linear metrics, such as entropy measures, most accurately reflect changes in behavior and cognitive performance. Other authors also reported that non-linear HRV metrics (e.g., entropy and fractal dimension) are more informative for the studies of behavior than statistical and frequency measures of HRV (Pham et al, 2021). In accordance with the system-evolutionary theory, entropy measures of HRV reflect the system characteristics of individual experience actualized in current behavior.…”

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

“…In spite of this wide availability of techniques and approaches for FHRV assessment, only weak predictive indications about fetal hypoxia and neonatal injuries can be achieved through the algorithms and methodologies developed so far [17]. In addition, while there are multiple, and also recent, examples of literature studies that review and discuss the available methodologies and metrics used both in research and in clinical practice to analyze the heart rate variability (HRV) of adult subjects [31][32][33][34], the field of fetal heart monitoring, despite a few examples of works focused on the neonatal and perinatal medicine [35,36], still lacks a comprehensive overview of the techniques employed in the analysis of FHRV.…”

Heart Rate fractality disruption as a footprint of subthreshold depressive symptoms

“…In fact, the HRV predictive power for depression is comparable to that of other tools like the PHQ-9 (Economides et al, 2020;Pizzoli et al, 2021), the most accurate DSM-V based screening questionnaire (Kroenke et al, 2010). When exploring the association between heart rate variability and mood symptoms, the use of non-linear calculations seems crucial to the description of mind-heart-body interactions -in term of affect and cognition -that are not captured by linear analyses of time and frequency (Jung et al, 2019;Pham et al, 2021).…”