Demonstration of circadian rhythm in heart rate turbulence using novel application of correlator functions

Watanabe, Mari; Alford, Mark; Schneider, Raphaël; Bauer, Axel; Barthel, Petra; Stein, Phyllis K.; Schmidt, Georg

doi:10.1016/j.hrthm.2006.11.016

Cited by 10 publications

(7 citation statements)

References 35 publications

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“…• The central tendency, CTMdRR, of the second order difference plots described the tightness of the distribution with different thresholds of radius to characterize dispersion [19]; here the inverse of normalized area was used. • Turbulence slope, a measure originally used within 20 beats of a ventricular premature beat [20,21], was used in isolation, grad5max. • Many of the Poincaré plot measures (length, breadth, SD1, SD2, SD1/SD2, area, and lagged plots) are linear measures that are scaled versions of SDNN and RMSSD [22] so the time domain equivalents were used in their place.…”

Section: Resultsmentioning

confidence: 99%

Heart rate variability indices for very short-term (30 beat) analysis. Part 1: survey and toolbox

Smith

Owen

Reynolds

2013

J Clin Monit Comput

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Heart rate variability (HRV) analysis over very short (<60 s) periods may be useful for monitoring dynamic changes in autonomic nervous system activity where steady-state conditions are not maintained (e.g. during drug administration, or the start or end of exercise). From the 1980s there has been a wealth of HRV indices produced in the quest for better measures of the change in parasympathetic and sympathetic activity. Many of the indices have been sparingly used and have not been investigated for application to short-term use. This study surveyed published methods of HRV analysis searching for indices that could be applied to very short time HRV analysis. The survey included measures of time domain, frequency domain, respiratory sinus arrhythmia, Poincaré plot, and heart rate characteristics. Indices were tested with short segments of archived data to remove those that produced invalid results, or were mathematically equivalent to, but less well known than other indices. The survey identified a comprehensive list of 115 indices that were subsequently coded and screened. Of these, 70 were unique and produced a finite number with 60 s data, so are included in the Toolbox. These indices require validation against physiological data before they can be applied to short-term HRV analysis of cardiac autonomic nervous system activity.

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Section: Resultsmentioning

confidence: 99%

Heart rate variability indices for very short-term (30 beat) analysis. Part 1: survey and toolbox

Smith

Owen

Reynolds

2013

J Clin Monit Comput

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“…Several physiological factors modulate the HRT pattern [6], [7], [8]. The dependence of HRT on HR is attributed to a shared sympathovagal modulation, i.e.…”

Section: Discussionmentioning

confidence: 99%

On the Influence of Heart Rate and Coupling Interval Prematurity on Heart Rate Turbulence

Barquero-Pérez

Figuera

Goya–Esteban

et al. 2017

IEEE Trans. Biomed. Eng.

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“…In contrast to the cosinor method, this method does not impose a functional structure on the variation, but only establishes whether or not circadian variation is present in x m (n) (Watanabe et al 2007). Evidently, the autocorrelation function r m (k) indicates to what degree two samples are correlated with each other, being k samples apart.…”

Section: The Autocorrelation Methodsmentioning

confidence: 99%

“…When x m (n) exhibits circadian variation, r m (k) has a U-shaped appearance, i.e. the autocorrelation is positive-valued at 0 and 23 h lags and negative-valued at some intermediate lags (Watanabe et al 2007).…”

Section: The Autocorrelation Methodsmentioning

confidence: 99%

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Circadian variation in dominant atrial fibrillation frequency in persistent atrial fibrillation

Sandberg¹,

Bollmann²,

Husser³

et al. 2010

Physiol. Meas.

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Circadian variation in atrial fibrillation (AF) frequency is explored in this paper by employing recent advances in signal processing. Once the AF frequency has been estimated and tracked by a hidden Markov model approach, the resulting trend is analyzed for the purpose of detecting and characterizing the presence of circadian variation. With cosinor analysis, the results show that the short-term variations in the AF frequency exceed the variation that may be attributed to circadian. Using the autocorrelation method, circadian variation was found in 13 of 18 ambulatory ECG recordings (Holter) acquired from patients with long-standing persistent AF. Using the ensemble correlation method, the highest AF frequency usually occurred during the afternoon, whereas the lowest usually occurred during late night. It is concluded that circadian variation is present in most patients with long-standing persistent AF though the short-term variation in the AF frequency is considerable and should be taken into account.

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Demonstration of circadian rhythm in heart rate turbulence using novel application of correlator functions

Abstract: Background: It has been difficult to demonstrate circadian rhythm in the two parameters of heart rate

Cited by 10 publications

References 35 publications

Heart rate variability indices for very short-term (30 beat) analysis. Part 1: survey and toolbox

Heart rate variability indices for very short-term (30 beat) analysis. Part 1: survey and toolbox

On the Influence of Heart Rate and Coupling Interval Prematurity on Heart Rate Turbulence

Circadian variation in dominant atrial fibrillation frequency in persistent atrial fibrillation

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