Heart rate turbulence after atrial premature beats before spontaneous onset of atrial fibrillation

Vikman, Saila; Lindgren, Kai S.; Mäkikallio, Timo H.; Yli‐Mäyry, Sinikka; Airaksinen, Juhani; Huikuri, Heikki V.

doi:10.1016/j.jacc.2004.10.033

Cited by 37 publications

(29 citation statements)

References 39 publications

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“…Multiple studies in patients with paroxysmal AF have shown increased APC activity right before the onset of AF imputing them as likely triggers. [7][8][9] Pulmonary vein isolation and ablation performed for treatment of AF has been shown to significantly reduce APC frequency. 14,15 In contrast, incomplete APC suppression in these patients was associated with higher AF recurrence.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

See 1 more Smart Citation

Frequent Atrial Premature Complexes and Their Association With Risk of Atrial Fibrillation

Acharya

Tringali

Bhullar

et al. 2015

The American Journal of Cardiology

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Section: Accepted Manuscriptmentioning

confidence: 99%

“…6 Though shown to trigger paroxysmal AF, they are, at present, considered innocuous and generally left untreated. [7][8][9] Our hypothesis is that patients with frequent and recurrent APCs are more likely to develop AF. …”

Section: Introductionmentioning

confidence: 99%

Frequent Atrial Premature Complexes and Their Association With Risk of Atrial Fibrillation

Acharya

Tringali

Bhullar

et al. 2015

The American Journal of Cardiology

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“…Furthermore, the values of ApEn before the onset of AF were lower compared with the values obtained from matched healthy control subjects. In a more recent study [Vikman et al, 2005] these results could be confirmed, however a more predictive parameter for AF was found: the turbulence onset (TO). TO was significantly less negative during the 1 h preceding AF than during the non-AF hours (0.71 ± 1.76 versus −0.35±1.46, p < 0.00001).…”

Section: Further Applicationsmentioning

confidence: 78%

Nonlinear Methods of Cardiovascular Physics and Their Clinical Applicability

Wessel

Malberg²,

Bauernschmitt

et al. 2007

Int. J. Bifurcation Chaos

102

107

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In this tutorial we present recently developed nonlinear methods of cardiovascular physics and show their potentials to clinically relevant problems in cardiology. The first part describes methods of cardiovascular physics, especially data analysis and modeling of noninvasively measured biosignals, with the aim to improve clinical diagnostics and to improve the understanding of cardiovascular regulation. Applications of nonlinear data analysis and modeling tools are various and outlined in the second part of this tutorial: monitoring-, diagnosis-, course and mortality prognoses as well as early detection of heart diseases. We show, that these data analyses and modeling methods lead to significant improvements in different medical fields.Keywords: Cardiovascular physics; nonlinear data analysis and modeling; heart rate and blood pressure variability; baroreflex sensitivity. List of Abbreviations

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“…Also, increasing age is associated with a decrease in the HRT index [60]. It is reported in [67] that TO is significantly less According to the discussion above, we use PLFME for the cardiac signal. Specifically, in PLFME, the original time series is filtered by the piecewise linear filter A defined in (2.1) recursively at multiple scales.…”

Section: Application To Human Heartbeat Interval Time Seriesmentioning

confidence: 97%

Filter-based multiscale entropy analysis of complex physiological time series

Zhao

2013

Phys. Rev. E

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Zhao, Liang, "Filter-based multiscale entropy analysis of complex physiological time series" (2013). Dissertations -ALL. Paper 3. ABSTRACTThe multiscale entropy (MSE) has been widely and successfully used in analyzing the complexity of physiologic time series. In this thesis, we re-interpret the averaging process in MSE as filtering a time series by a filter of a piecewise constant type. From this viewpoint, we introduce the filter-based multiscale entropy (FME) which filters a time series by filters to generate its multiple frequency components and then compute the blockwise entropy of the resulting components. By choosing filters adapted to the feature of a given time series, FME is able to better capture its multiscale information and to provide more flexibility for studying its complexity. Motivated by the heart rate turbulence theory which suggests that the human heartbeat interval time series (HHITS) can be described in piecewise linear patterns, we propose the piecewise linear filter multiscale entropy (PLFME) for the complexity analysis of the time series. Numerical results from PLFME are more robust to data of various lengths than those from MSE. We then propose wavelet packet transform entropy (WPTE) analysis. We apply WPTE analysis to HHITS using lower and higher piecewise linear filters. Numerical results show that WPTE using piecewise linear filters gives us the highest classification rates discriminating different cardiac systems among other multiscale entropy analysis. At the end, we discuss the application of FME on discrete time series. We introduce an 'eliminating' algorithm to examine and compare the complexity of coding and noncoding DNA sequences.

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Heart rate turbulence after atrial premature beats before spontaneous onset of atrial fibrillation

Abstract: Altered heart rate dynamics, suggesting transient enhancement of vagal outflow after premature atrial excitation, are temporally related to spontaneous onset of clinical AF.

Cited by 37 publications

References 39 publications

Frequent Atrial Premature Complexes and Their Association With Risk of Atrial Fibrillation

Frequent Atrial Premature Complexes and Their Association With Risk of Atrial Fibrillation

Nonlinear Methods of Cardiovascular Physics and Their Clinical Applicability

Filter-based multiscale entropy analysis of complex physiological time series

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