Benchmarking heart rate variability toolboxes

Publication guidelines for human heart rate and heart rate variability studies in psychophysiology-Part 1: Physiological underpinnings and foundations of measurement

This Committee Report provides methodological, interpretive, and reporting guidance for researchers who use measures of heart rate (HR) and heart rate variability (HRV) in psychophysiological research. We provide brief summaries of best practices in measuring HR and HRV via electrocardiographic and photoplethysmographic signals in laboratory, field (ambulatory), and brain-imaging contexts to address research questions incorporating measures of HR and HRV. The Report emphasizes evidence for the strengths and weaknesses of different recording and derivation methods for measures of HR and HRV. Along with this guidance, the Report reviews what is known about the origin of the heartbeat and its neural control, including factors that produce and influence HRV metrics. The Report concludes with checklists to guide authors in study design and analysis considerations, as well as guidance on the reporting of key methodological details and characteristics of the samples under study. It is expected that rigorous and transparent recording and reporting of HR and HRV measures will strengthen inferences across the many applications of these metrics in psychophysiology. The prior Committee Reports on HR and HRV are several decades old. Since their appearance, technologies for human cardiac and vascular monitoring in laboratory and daily life (i.e., ambulatory) contexts have greatly expanded. This Committee Report was prepared for the Society for Psychophysiological Research to provide updated methodological and interpretive guidance, as well as to summarize best practices for reporting HR and HRV studies in humans.

Digital oximetry biomarkers for assessing respiratory function: standards of measurement, physiological interpretation, and clinical use

Pulse oximetry is routinely used to non-invasively monitor oxygen saturation levels. A low oxygen level in the blood means low oxygen in the tissues, which can ultimately lead to organ failure. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous oxygen saturation time series variability analysis. The primary objective of this research was to identify, implement and validate key digital oximetry biomarkers (OBMs) for the purpose of creating a standard and associated reference toolbox for continuous oximetry time series analysis. We review the sleep medicine literature to identify clinically relevant OBMs. We implement these biomarkers and demonstrate their clinical value within the context of obstructive sleep apnea (OSA) diagnosis on a total of n = 3806 individual polysomnography recordings totaling 26,686 h of continuous data. A total of 44 digital oximetry biomarkers were implemented. Reference ranges for each biomarker are provided for individuals with mild, moderate, and severe OSA and for non-OSA recordings. Linear regression analysis between biomarkers and the apnea hypopnea index (AHI) showed a high correlation, which reached [Formula: see text]. The resulting python OBM toolbox, denoted "pobm", was contributed to the open software PhysioZoo ( physiozoo.org ). Studying the variability of the continuous oxygen saturation time series using pbom may provide information on the underlying physiological control systems and enhance our understanding of the manifestations and etiology of diseases, with emphasis on respiratory diseases.

An open source benchmarked toolbox for cardiovascular waveform and interval analysis

OBJECTIVE

This work aims to validate a set of data processing methods for variability metrics, which hold promise as potential indicators for autonomic function, prediction of adverse cardiovascular outcomes, psychophysiological status, and general wellness. Although the investigation of heart rate variability (HRV) has been prevalent for several decades, the methods used for preprocessing, windowing, and choosing appropriate parameters lacks consensus among academic and clinical investigators. Moreover, many of the important steps are omitted from publications, preventing reproducibility.

APPROACH

To address this, we have compiled a comprehensive and open-source modular toolbox for calculating HRV metrics and other related variability indices, on both raw cardiovascular time series and RR intervals. The software, known as the PhysioNet Cardiovascular Signal Toolbox, is implemented in the MATLAB programming language, with standard (open) input and output formats, and requires no external libraries. The functioning of our software is compared with other widely used and referenced HRV toolboxes to identify important differences.

MAIN RESULTS

Our findings demonstrate how modest differences in the approach to HRV analysis can lead to divergent results, a factor that might have contributed to the lack of repeatability of studies and clinical applicability of HRV metrics.

SIGNIFICANCE

Existing HRV toolboxes do not include standardized preprocessing, signal quality indices (for noisy segment removal), and abnormal rhythm detection and are therefore likely to lead to significant errors in the presence of moderate to high noise or arrhythmias. We therefore describe the inclusion of validated tools to address these issues. We also make recommendations for default values and testing/reporting.

Hypoglossal Nerve Stimulation and Heart Rate Variability: Analysis of STAR Trial Responders

OBJECTIVE

Hypoglossal nerve stimulation represents a novel therapy for the treatment of moderate-severe obstructive sleep apnea; nonetheless, its cardiovascular effects are not known. We examine the effects of hypoglossal nerve stimulation on heart rate variability, a measure of autonomic function.

STUDY DESIGN

Substudy of the STAR trial (Stimulation Therapy for Apnea Reduction): a multicenter prospective single-group cohort.

SETTING

Academic and private practice centers in the United States and Europe.

SUBJECTS AND METHODS

A subset of responder participants (n = 46) from the STAR trial was randomized to therapy withdrawal or therapy maintenance 12 months after surgery. Heart rate variability analysis included standard deviation of the R-R interval (SDNN), low-frequency power of the R-R interval, and high-frequency power of the R-R interval. Analysis was performed by sleep with 5-minute sliding window epochs during baseline, 12 months, and the maintenance/withdrawal period.

RESULTS

A significant improvement from baseline to 12 months in heart rate variability was seen for SDNN and low frequency across all sleep stages. SDNN analysis demonstrated no change in the wake period (mean ± SD: 0.042 ± 0.01 vs 0.077 ± 0.07, P = .19). Reduction in SDNN was correlated to improvement in apnea-hypopnea index ( r = 0.39, P = .03). In the therapy withdrawal group, no significant changes in SDNN were seen for N1/N2, N3, or rapid eye movement sleep.

CONCLUSION

Hypoglossal nerve stimulation therapy appears to reduce heart rate variability during sleep. This reduction was not affected by a 1-week withdrawal period. Larger prospective studies are required to better understand the effect of hypoglossal nerve stimulation on autonomic dysfunction in obstructive sleep apnea.

Electrocardiogram Sampling Frequency Range Acceptable for Heart Rate Variability Analysis

Objectives

Heart rate variability (HRV) has gained recognition as a noninvasive marker of autonomic activity. HRV is considered a promising tool in various clinical scenarios. The optimal electrocardiogram (ECG) sampling frequency required to ensure sufficient precision of R–R intervals for HRV analysis has not yet been determined. Here, we aimed to determine the acceptable ECG sampling frequency range by analyzing ECG signals from patients who visited an emergency department with the chief complaint of acute intoxication or overdose.

Methods

The study included 83 adult patients who visited an emergency department with the chief complaint of acute poisoning. The original 1,000-Hz ECG signals were down-sampled to 500-, 250-, 100-, and 50-Hz sampling frequencies with linear interpolation. R–R interval data were analyzed for time-domain, frequency-domain, and nonlinear HRV parameters. Parameters derived from the data on down-sampled frequencies were compared with those derived from the data on 1,000-Hz signals, and Lin's concordance correlation coefficients were calculated.

Results

Down-sampling to 500 or 250 Hz resulted in excellent concordance. Signals down-sampled to 100 Hz produced acceptable results for time-domain analysis and Poincaré plots, but not for frequency-domain analysis. Down-sampling to 50 Hz proved to be unacceptable for both time- and frequency-domain analyses. At 50 Hz, the root-mean-squared successive differences and the power of high frequency tended to have high values and random errors.

Conclusions

A 250-Hz sampling frequency would be acceptable for HRV analysis. When frequency-domain analysis is not required, a 100-Hz sampling frequency would also be acceptable.