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Journal ArticleDOI

An Overview of Heart Rate Variability Metrics and Norms.

TL;DR: Current perspectives on the mechanisms that generate 24 h, short-term (<5 min), and ultra-short-term HRV are reviewed, and the importance of HRV, and its implications for health and performance are reviewed.
Abstract: Healthy biological systems exhibit complex patterns of variability that can be described by mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis. This article briefly reviews current perspectives on the mechanisms that generate 24 h, short-term (~5 min), and ultra-short-term (<5 min) HRV, the importance of HRV, and its implications for health and performance. The authors provide an overview of widely-used HRV time-domain, frequency-domain, and non-linear metrics. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. Non-linear measurements quantify the unpredictability and complexity of a series of IBIs. The authors survey published normative values for clinical, healthy, and optimal performance populations. They stress the importance of measurement context, including recording period length, subject age, and sex, on baseline HRV values. They caution that 24 h, short-term, and ultra-short-term normative values are not interchangeable. They encourage professionals to supplement published norms with findings from their own specialized populations. Finally, the authors provide an overview of HRV assessment strategies for clinical and optimal performance interventions.

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Citations
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Journal ArticleDOI
10 Feb 2020
TL;DR: Different wearables are all reasonably accurate at resting and prolonged elevated heart rate, but that differences exist between devices in responding to changes in activity, and this has implications for researchers, clinicians, and consumers.
Abstract: As wearable technologies are being increasingly used for clinical research and healthcare, it is critical to understand their accuracy and determine how measurement errors may affect research conclusions and impact healthcare decision-making. Accuracy of wearable technologies has been a hotly debated topic in both the research and popular science literature. Currently, wearable technology companies are responsible for assessing and reporting the accuracy of their products, but little information about the evaluation method is made publicly available. Heart rate measurements from wearables are derived from photoplethysmography (PPG), an optical method for measuring changes in blood volume under the skin. Potential inaccuracies in PPG stem from three major areas, includes (1) diverse skin types, (2) motion artifacts, and (3) signal crossover. To date, no study has systematically explored the accuracy of wearables across the full range of skin tones. Here, we explored heart rate and PPG data from consumer- and research-grade wearables under multiple circumstances to test whether and to what extent these inaccuracies exist. We saw no statistically significant difference in accuracy across skin tones, but we saw significant differences between devices, and between activity types, notably, that absolute error during activity was, on average, 30% higher than during rest. Our conclusions indicate that different wearables are all reasonably accurate at resting and prolonged elevated heart rate, but that differences exist between devices in responding to changes in activity. This has implications for researchers, clinicians, and consumers in drawing study conclusions, combining study results, and making health-related decisions using these devices.

276 citations


Cites methods from "An Overview of Heart Rate Variabili..."

  • ...In addition to HR, we examined HR variability (HRV), a clinically relevant diagnostic metric that can be derived from PPG signals and is a widely used metric of autonomic nervous system function.(43) The standard HRV metrics we examined, included the time-domain metrics, included mean HRV, minimum HRV, maximum HRV, RMSSD, SDNN, and pNN50 (Supplementary Fig....

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Journal ArticleDOI
TL;DR: It is demonstrated that all HRV-measures were lower in MD than in healthy controls and thus strengthens evidence for lower HRV as a potential cardiovascular risk factor in these patients.
Abstract: BackgroundMajor depression (MD) is a risk factor for cardiovascular disease. Reduced heart rate variability (HRV) has been observed in MD. Given the predictive value of HRV for cardiovascular health, reduced HRV might be one physiological factor that mediates this association.MethodsThe purpose of this study was to provide up-to-date random-effects meta-analyses of studies which compare resting-state measures of HRV between unmedicated adults with MD and controls. Database search considered English and German literature to July 2018.ResultsA total of 21 studies including 2250 patients and 1982 controls were extracted. Significant differences between patients and controls were found for (i) frequency domains such as HF-HRV [Hedges' g = −0.318; 95% CI (−0.388 to −0.247)], LF-HRV (Hedges' g = −0.195; 95% CI (−0.332 to −0.059)], LF/HF-HRV (Hedges' g = 0.195; 95% CI (0.086–0.303)] and VLF-HRV (Hedges' g = −0.096; 95% CI (−0.179 to −0.013)), and for (ii) time-domains such as IBI (Hedges' g = −0.163; 95% CI (−0.304 to −0.022)], RMSSD (Hedges' g = −0.462; 95% CI (−0.612 to −0.312)] and SDNN (Hedges' g = −0.266; 95% CI (−0.431 to −0.100)].ConclusionsOur findings demonstrate that all HRV-measures were lower in MD than in healthy controls and thus strengthens evidence for lower HRV as a potential cardiovascular risk factor in these patients.

196 citations


Cites background or methods from "An Overview of Heart Rate Variabili..."

  • ...A third category of HRV is respiratory sinus arrhythmia (RSA), which reflects heart rate variations via the vagus nerve related to the respiratory cycle (Task Force of The European Society of Cardiology and The North American & Society of Pacing and Electrophysiology, 1996; Shaffer et al., 2014; Shaffer and Ginsberg, 2017)....

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  • ...…respiratory sinus arrhythmia (RSA), which reflects heart rate variations via the vagus nerve related to the respiratory cycle (Task Force of The European Society of Cardiology and The North American & Society of Pacing and Electrophysiology, 1996; Shaffer et al., 2014; Shaffer and Ginsberg, 2017)....

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  • ...Nevertheless, HRV might be an important mediator between depression and CVD (Sgoifo et al., 2015; Shaffer and Ginsberg, 2017)....

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  • ...…interrelated HRV measures equivalent, in particular when measures derived from shortterm recordings (Task Force of The European Society of Cardiology and The North American & Society of Pacing and Electrophysiology, 1996; Berntson et al., 2005; Kemp et al., 2010; Shaffer and Ginsberg, 2017)....

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  • ...Further, the literature is inconclusive regarding equivalence and the approach of treating interrelated HRV measures equivalent, in particular when measures derived from shortterm recordings (Task Force of The European Society of Cardiology and The North American & Society of Pacing and Electrophysiology, 1996; Berntson et al., 2005; Kemp et al., 2010; Shaffer and Ginsberg, 2017)....

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Journal ArticleDOI
TL;DR: The neuroanatomy of the ABVN is explored with reference to clinical surveys examining Arnold’s reflex, cadaveric studies, fMRI studies, electrophysiological studies, acupuncture studies, retrograde tracing studies, and studies measuring changes in autonomic parameters in response to auricular tVNS.
Abstract: The array of end organ innervations of the vagus nerve, coupled with increased basic science evidence, has led to vagus nerve stimulation (VNS) being explored as a management option in a number of clinical disorders, such as heart failure, migraine and inflammatory bowel disease. Both invasive (surgically implanted) and non-invasive (transcutaneous) techniques of VNS exist. Transcutaneous VNS (tVNS) delivery systems rely on the cutaneous distribution of vagal afferents, either at the external ear (auricular branch of the vagus nerve) or at the neck (cervical branch of the vagus nerve), thus obviating the need for surgical implantation of a VNS delivery device and facilitating further investigations across a wide range of uses. The concept of electrically stimulating the auricular branch of the vagus nerve (ABVN), which provides somatosensory innervation to several aspects of the external ear, is relatively more recent compared with cervical VNS; thus, there is a relative paucity of literature surrounding its operation and functionality. Despite the increasing body of research exploring the therapeutic uses of auricular transcutaneous VNS (tVNS), a comprehensive review of the cutaneous, intracranial and central distribution of ABVN fibres has not been conducted to date. A review of the literature exploring the neuroanatomical basis of this neuromodulatory therapy is therefore timely. Our review article explores the neuroanatomy of the ABVN with reference to (1) clinical surveys examining Arnold's reflex, (2) cadaveric studies, (3) fMRI studies, (4) electrophysiological studies, (5) acupuncture studies, (6) retrograde tracing studies and (7) studies measuring changes in autonomic (cardiovascular) parameters in response to auricular tVNS. We also provide an overview of the fibre composition of the ABVN and the effects of auricular tVNS on the central nervous system. Cadaveric studies, of which a limited number exist in the literature, would be the 'gold-standard' approach to studying the cutaneous map of the ABVN; thus, there is a need for more such studies to be conducted. Functional magnetic resonance imaging (fMRI) represents a useful surrogate modality for discerning the auricular sites most likely innervated by the ABVN and the most promising locations for auricular tVNS. However, given the heterogeneity in the results of such investigations and the various limitations of using fMRI, the current literature lacks a clear consensus on the auricular sites that are most densely innervated by the ABVN and whether the brain regions secondarily activated by electrical auricular tVNS depend on specific parameters. At present, it is reasonable to surmise that the concha and inner tragus are suitable locations for vagal modulation. Given the therapeutic potential of auricular tVNS, there remains a need for the cutaneous map of the ABVN to be further refined and the effects of various stimulation parameters and stimulation sites to be determined.

180 citations


Cites background from "An Overview of Heart Rate Variabili..."

  • ...…sympathetic nerve activation, and the non-linear interactions between sympathetic and parasympathetic activity that are confounded by the mechanical effects of respiration and heart rate mean that the LF: HF sympatho-vagal balance hypothesis is inaccurate (Billman, 2013; Shaffer & Ginsberg, 2017)....

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Journal ArticleDOI
TL;DR: The experimental results on the AMIGOS dataset show that the method proposed in this paper achieves a better precision of the classification of the emotional states, in comparison with the originally obtained by the authors of this dataset.
Abstract: Recommender systems have been based on context and content, and now the technological challenge of making personalized recommendations based on the user emotional state arises through physiological signals that are obtained from devices or sensors. This paper applies the deep learning approach using a deep convolutional neural network on a dataset of physiological signals (electrocardiogram and galvanic skin response), in this case, the AMIGOS dataset. The detection of emotions is done by correlating these physiological signals with the data of arousal and valence of this dataset, to classify the affective state of a person. In addition, an application for emotion recognition based on classic machine learning algorithms is proposed to extract the features of physiological signals in the domain of time, frequency, and non-linear. This application uses a convolutional neural network for the automatic feature extraction of the physiological signals, and through fully connected network layers, the emotion prediction is made. The experimental results on the AMIGOS dataset show that the method proposed in this paper achieves a better precision of the classification of the emotional states, in comparison with the originally obtained by the authors of this dataset.

178 citations


Additional excerpts

  • ...dictability of a series IBI is quantified in the non-linear according to [44] and [45]....

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Journal ArticleDOI
TL;DR: A selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments to advance the development of effective human-machine driving interfaces and driver support systems is provided.
Abstract: As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.

174 citations


Cites background or methods from "An Overview of Heart Rate Variabili..."

  • ...Detailed discussions can be found elsewhere (Task Force of the European Society of Cardiology, 1996; Berntson et al., 2007; Laborde et al., 2017; Shaffer and Ginsberg, 2017)....

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  • ...Further information on heart activity related metrics can be found in detailed reviews (Jennings et al., 1981; Task Force of the European Society of Cardiology, 1996; Berntson et al., 2007; Laborde et al., 2017; Shaffer and Ginsberg, 2017)....

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  • ...For instance, some metrics require minimum duration of data and falling short of such requirements will lead to misrepresentative findings (e.g., standard deviation of R-R heart beats or SDRR is considered more accurate when calculated over 24 h vs. 5min or shorter intervals; Shaffer and Ginsberg, 2017)....

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  • ...24 h) can also lead to uncorrelated findings and some metrics are better for short term recordings than others (Shaffer and Ginsberg, 2017)....

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  • ...Non-linear measures of HRV are useful in capturing the unpredictability and dynamic nature of heart rate time-series data (Shaffer and Ginsberg, 2017)....

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References
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Journal ArticleDOI
TL;DR: The 95% limits of agreement, estimated by mean difference 1.96 standard deviation of the differences, provide an interval within which 95% of differences between measurements by the two methods are expected to lie.
Abstract: Agreement between two methods of clinical measurement can be quantified using the differences between observations made using the two methods on the same subjects. The 95% limits of agreement, estimated by mean difference +/- 1.96 standard deviation of the differences, provide an interval within which 95% of differences between measurements by the two methods are expected to lie. We describe how graphical methods can be used to investigate the assumptions of the method and we also give confidence intervals. We extend the basic approach to data where there is a relationship between difference and magnitude, both with a simple logarithmic transformation approach and a new, more general, regression approach. We discuss the importance of the repeatability of each method separately and compare an estimate of this to the limits of agreement. We extend the limits of agreement approach to data with repeated measurements, proposing new estimates for equal numbers of replicates by each method on each subject, for unequal numbers of replicates, and for replicated data collected in pairs, where the underlying value of the quantity being measured is changing. Finally, we describe a nonparametric approach to comparing methods.

7,976 citations


"An Overview of Heart Rate Variabili..." refers methods in this paper

  • ...Since correlation between measurements doesn’t ensure agreement, the authors recommend that investigators utilize the more rigorous Bland-Altman Limits of Agreement (LoA) method (131, 132) like Munoz et al....

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Journal ArticleDOI
10 Jul 1981-Science
TL;DR: It is shown that sympathetic and parasympathetic nervous activity make frequency-specific contributions to the heart rate power spectrum, and that renin-angiotensin system activity strongly modulates the amplitude of the spectral peak located at 0.04 hertz.
Abstract: Power spectrum analysis of heart rate fluctuations provides a quantitative noninvasive means of assessing the functioning of the short-term cardiovascular control systems. We show that sympathetic and parasympathetic nervous activity make frequency-specific contributions to the heart rate power spectrum, and that renin-angiotensin system activity strongly modulates the amplitude of the spectral peak located at 0.04 hertz. Our data therefore provide evidence that the renin-angiotensin system plays a significant role in short-term cardiovascular control in the time scale of seconds to minutes.

5,088 citations

Journal ArticleDOI
TL;DR: The spontaneous beat-to-beat oscillation in R-R interval during control recumbent position, 90° upright tilt, controlled respiration and acute and chronic β-adrenergic receptor blockade was analyzed, indicating that sympathetic nerves to the heart are instrumental in the genesis of low-frequency oscillations in R -R interval.
Abstract: In 57 normal subjects (age 20-60 years), we analyzed the spontaneous beat-to-beat oscillation in R-R interval during control recumbent position, 90 degrees upright tilt, controlled respiration (n = 16) and acute (n = 10) and chronic (n = 12) beta-adrenergic receptor blockade. Automatic computer analysis provided the autoregressive power spectral density, as well as the number and relative power of the individual components. The power spectral density of R-R interval variability contained two major components in power, a high frequency at approximately 0.25 Hz and a low frequency at approximately 0.1 Hz, with a normalized low frequency:high frequency ratio of 3.6 +/- 0.7. With tilt, the low-frequency component became largely predominant (90 +/- 1%) with a low frequency:high frequency ratio of 21 +/- 4. Acute beta-adrenergic receptor blockade (0.2 mg/kg IV propranolol) increased variance at rest and markedly blunted the increase in low frequency and low frequency:high frequency ratio induced by tilt. Chronic beta-adrenergic receptor blockade (0.6 mg/kg p.o. propranolol, t.i.d.), in addition, reduced low frequency and increased high frequency at rest, while limiting the low frequency:high frequency ratio increase produced by tilt. Controlled respiration produced at rest a marked increase in the high-frequency component, with a reduction of the low-frequency component and of the low frequency:high frequency ratio (0.7 +/- 0.1); during tilt, the increase in the low frequency:high frequency ratio (8.3 +/- 1.6) was significantly smaller. In seven additional subjects in whom direct high-fidelity arterial pressure was recorded, simultaneous R-R interval and arterial pressure variabilities were examined at rest and during tilt. Also, the power spectral density of arterial pressure variability contained two major components, with a relative low frequency:high frequency ratio at rest of 2.8 +/- 0.7, which became 17 +/- 5 with tilt. These power spectral density components were numerically similar to those observed in R-R variability. Thus, invasive and noninvasive studies provided similar results. More direct information on the role of cardiac sympathetic nerves on R-R and arterial pressure variabilities was derived from a group of experiments in conscious dogs before and after bilateral stellectomy. Under control conditions, high frequency was predominant and low frequency was very small or absent, owing to a predominant vagal tone. During a 9% decrease in arterial pressure obtained with IV nitroglycerin, there was a marked increase in low frequency, as a result of reflex sympathetic activation.(ABSTRACT TRUNCATED AT 400 WORDS)

4,134 citations


"An Overview of Heart Rate Variabili..." refers background in this paper

  • ...Billman (21) challenged the belief that the LF/HF ratio measures “sympatho-vagal balance” (78, 79)....

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Related Papers (5)
Trending Questions (1)
What are healthy ranges for heartrate variability?

The paper provides an overview of HRV metrics and norms but does not specifically mention healthy ranges for heart rate variability.