Heart rate variability: Standards of measurement, physiological interpretation, and clinical use

Malik, Marek; Bigger, J. Thomas; Camm, A. John; Kleiger, Robert E.; Malliani, Alberto; Moss, Arthur J.; Schwartz, Peter J.

doi:10.1093/oxfordjournals.eurheartj.a014868

Cited by 7,521 publications

(2,772 citation statements)

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“…Under physiological conditions and without a significant change in respiratory signal power within the HF power band (confirmed in our data), HF power of the HR signal is an expedient marker of parasympathetic modulation (Malik et al 1996). The SA node responds very quickly to vagal (~150msec latency, steady state at 1-2sec), in contrast to sympathetic (~1-2sec latency, steady state at 30-60sec), influence (Spear et al 1979;Levy 1997).…”

Section: Discussionsupporting

confidence: 81%

“…Of the different instantaneous HRV indices computed by the point process technique, we selected the instantaneous high frequency power (HF) as our representative HRV metric. Heart rate HF power has garnered the greatest consensus for reflecting respiratory sinus arrhythmia (RSA) modulation by parasympathetic activity (Malik et al 1996;Lane et al 2001;Thayer et al 2002). Our choice was further validated by spectral analysis of the respiratory signal, which confirmed general stationarity of the respiratory variance in the HF range, in particular with no significant difference between ON and OFF epochs (Students' paired t-test; p=0.3162).…”

Section: Hrv Analysismentioning

confidence: 79%

“…The model also represents the dependence of the R -R interval length on the recent history of parasympathetic and sympathetic inputs to the SA node by modeling the mean as a linear function of the last p R -R intervals. This set of p coefficients allows for decomposition of the spectral power (HRV) into classic spectral components: Very Low frequency (VLF, 0 to 0.04 Hz), Low Frequency (LF, 0.04 to 0.15 Hz), and High Frequency (HF, 0.15 to 0.5 Hz) (Malik et al 1996). The point process recursive algorithm is able to estimate the dynamics of the model parameters, and consequently the time-varying behavior of each spectral index, at any time resolution.…”

Section: Hrv Analysismentioning

confidence: 99%

“…Tachycardic response is mediated via sympathetic outflow from the rVLM (Dampney et al 2002). Interestingly, these efferent outflows can be estimated non-invasively by analysis of heart rate variability (HRV) (Luczak et al 1973;Sayers 1973;Akselrod et al 1981;Malik et al 1996). This method typically involves deriving the frequency spectrum of a continuous estimate of heart rate (HR).…”

Section: Introductionmentioning

confidence: 99%

“…This method typically involves deriving the frequency spectrum of a continuous estimate of heart rate (HR). While some controversy in interpretation remains, the spectral peak in a low frequency band (LF, 0.01-0.15Hz) is thought to be influenced by both cardiovagal and sympathetic activity, while the peak in a high frequency band (HF, 0.15-0.40Hz) is influenced solely by cardiovagal activity (Malik et al 1996). Cardiovagal and sympathetic activity are not necessarily reciprocal or antagonistic (Berntson et al 1991;Paton et al 2005) and cannot be inferred from heart rate changes alone.…”

Section: Introductionmentioning

confidence: 99%

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Brain correlates of autonomic modulation: Combining heart rate variability with fMRI

Napadow¹,

Dhond²,

Conti³

et al. 2008

NeuroImage

303

229

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The central autonomic network (CAN) has been described in animal models but has been difficult to elucidate in humans. Potential confounds include physiological noise artifacts affecting brainstem neuroimaging data, and difficulty in deriving non-invasive continuous assessments of autonomic modulation. We have developed and implemented a new method which relates cardiac-gated fMRI timeseries with continuous-time heart rate variability (HRV) to estimate central autonomic processing. As many autonomic structures of interest are in brain regions strongly affected by cardiogenic pulsatility, we chose to cardiac-gate our fMRI acquisition to increase sensitivity. Cardiac-gating introduces T1-variability, which was corrected by transforming fMRI data to a fixed TR using a previously published method (Guimaraes et al. 1998). The electrocardiogram was analyzed with a novel point process adaptive-filter algorithm for computation of the high-frequency (HF) index, reflecting the time-varying dynamics of efferent cardiovagal modulation. Central command of cardiovagal outflow was inferred by using the HF timeseries resampled at as a regressor to the fMRI data. A grip task was used to perturb the autonomic nervous system. Our combined HRV-fMRI approach demonstrated HF correlation with fMRI activity in the hypothalamus, cerebellum, parabrachial nucleus/locus ceruleus, periaqueductal gray, amygdala, hippocampus, thalamus, and dorsomedial/dorsolateral prefrontal, posterior insular, and middle temporal cortices. While some regions consistent with central cardiovagal control in animal models gave corroborative evidence for our methodology, other mostly higher cortical or limbic-related brain regions may be unique to humans. Our approach should be optimized and applied to study the human brain correlates of autonomic modulation for various stimuli in both physiological and pathological states.

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Section: Discussionsupporting

confidence: 81%

Section: Hrv Analysismentioning

confidence: 79%

Section: Hrv Analysismentioning

confidence: 99%