How to Use Heart Rate Variability: Quantification of Vagal Activity in Toddlers and Adults in Long-Term ECG

Abstract

Recent developments in noninvasive electrocardiogram (ECG) monitoring with small, wearable sensors open the opportunity to record high-quality ECG over many hours in an easy and non-burdening way. However, while their recording has been tremendously simplified, the interpretation of heart rate variability (HRV) data is a more delicate matter. The aim of this paper is to supply detailed methodological discussion and new data material in order to provide a helpful notice of HRV monitoring issues depending on recording conditions and study populations. Special consideration is given to the monitoring over long periods, across periods with different levels of activity, and in adults versus children. Specifically, the paper aims at making users aware of neglected methodological limitations and at providing substantiated recommendations for the selection of appropriate HRV variables and their interpretation. To this end, 30-h HRV data of 48 healthy adults (18–40 years) and 47 healthy toddlers (16–37 months) were analyzed in detail. Time-domain, frequency-domain, and nonlinear HRV variables were calculated after strict signal preprocessing, using six different high-frequency band definitions including frequency bands dynamically adjusted for the individual respiration rate. The major conclusion of the in-depth analyses is that for most applications that implicate long-term monitoring across varying circumstances and activity levels in healthy individuals, the time-domain variables are adequate to gain an impression of an individual’s HRV and, thus, the dynamic adaptation of an organism’s behavior in response to the ever-changing demands of daily life. The sound selection and interpretation of frequency-domain variables requires considerably more consideration of physiological and mathematical principles. For those who prefer using frequency-domain variables, the paper provides detailed guidance and recommendations for the definition of appropriate frequency bands in compliance with their specific recording conditions and study populations.

Immersive Virtual Reality Influences Physiologic Responses to Submaximal Exercise: A Randomized, Crossover Trial

“…The following spectral parameters were computed: low frequency (LF, 0.04–0.15Hz), high frequency (HF, 0.15–0.8Hz), and total power (TP, 0.04–0.8Hz). The wider frequency range for HF and TP was chosen because of the high respiratory rate seen during exercise and, consequently, a shift of the respiratory peak toward higher frequencies in the HRV spectrum ( Lackner et al, 2020 ).…”

Effects of Touchscreen Media Use on Toddlers’ Sleep: Insights from Longtime ECG Monitoring

“…For the analysis, all raw data were converted from EDF to a MATLAB ® data format. In brief, to check the quality of the ECG data and to calculate the interbeat interval time series, we used a semi-automatic artifact handling protocol developed and used by our research group for several years [38,41]. The respiratory rate was determined offline using ECG derived respiration [42].…”

“…The respiratory rate was determined offline using ECG derived respiration [42]. Details of our data preprocessing routine are reported in Lackner et al [38].…”

“…At the same time, the little burdensome attachment of the equipment and unrestricted mobility while wearing it, increases participant outreach. ECG data, while not the most common physiological signals used in the analysis of sleep behaviors, provide information about the interaction between sympathetic and parasympathetic activities in the autonomic nervous system that reveal important information about a range of physiological states [38], and, as such, can deliver additional relevant information that may not be available from central nervous system variables. Finally, wearable sensing technology keeps costs and efforts manageable.…”

Special Issue: ECG Monitoring System

“…In the second paper, Lackner and colleagues [ 2 ] provide detailed guidance and recommendations on the interpretation of heart rate variability (HRV) analysis from ECG data monitored over long periods. The main findings reveal that time-domain variables are mostly adequate to describe individual’s HRV from long-term ECG monitoring.…”