Fractal Dimension of Heart Rate and Blood Pressure in Healthy Subjects and in Diabetic Subjects

Chau, Nguyen Phong; Chanudet, X.; Bauduceau, B.; Gautier, D.; Larroque, P

doi:10.3109/08037059309077536

Cited by 13 publications

(9 citation statements)

References 13 publications

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“…, 1995), approximate entropy (ApEn) (Pincus & Goldberger, 1994), symbolic dynamics (SymDyn) (Voss et al. , 1996) in terms of forbidden words (SymDyn FW) and entropy (SymDyn En), fractal dimension by curve length (FD‐L) (Chau et al. , 1993), return map (RM, also called Poincaré plot) in terms of SD1 and SD2 (Huikuri et al.…”

Section: Methodsmentioning

confidence: 99%

“…In the FD‐L analysis the number of sticks of various lengths needed to follow the time series curve is counted (Chau et al. , 1993).…”

Section: Methodsmentioning

confidence: 99%

“…A set of non-linear HR dynamics methods (Table 1), i.e. detrended fluctuation analyses (DFA) (Peng et al, 1995), approximate entropy (ApEn) (Pincus & Goldberger, 1994), symbolic dynamics (SymDyn) (Voss et al, 1996) in terms of forbidden words (SymDyn FW) and entropy (SymDyn En), fractal dimension by curve length (FD-L) (Chau et al, 1993), return map (RM, also called Poincaré plot) in terms of SD1 and SD2 (Huikuri et al, 1996), were analysed from the R-R interval data with a custom software package (WinCPRS; Absolute Aliens Oy, Turku, Finland). All the methods are suitable for analysis of time series of variable length.…”

Section: Analyses Of Non-linear Hr Dynamicsmentioning

confidence: 99%

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Comparison of methods for editing of ectopic beats in measurements of short‐term non‐linear heart rate dynamics

Tarkiainen

Kuusela

Tahvanainen

et al. 2007

Clin Physio Funct Imaging

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Non-linear heart rate (HR) dynamics characterizes the fractal properties and complexity of the variations in HR. Ventricular and supraventricular ectopic beats might introduce a mathematical artefact to the analyses on sinus rhythm. We therefore evaluated the effects of different editing practices for ectopic beats such that 753 40-min ECG recordings were (i) not edited for the ectopic beats, or the ectopic beats were edited with (ii) an interpolation or with (iii) a deletion method before the analyses of non-linear HR dynamics. The non-linear HR dynamics analyses included detrended fluctuation analysis (DFA), approximate entropy, symbolic dynamics (SymDyn), fractal dimension and return map (RM). We found that the short-term scaling exponent (alpha1) of DFA, forbidden words of SymDyn and RM were sensitive measurements to the ectopic beats and there were strong correlations between these measurements and the number of ectopic beats. In addition, the unedited ectopic beats significantly lowered the stability of these measurements. However, the editing either with interpolation or deletion method corrected the measurements for the bias caused by the ectopic beats. On the contrary, the entropy measurements were not as sensitive to the ectopic beats. In conclusion, the ectopic beats affect the non-linear HR dynamics of sinus rhythm differently, causing a more marked bias in fractal than in complexity measurements of non-linear HR dynamics. This erroneous effect of ectopic beats can be corrected with a proper editing of these measurements. Therefore, there is an obvious need for standardized editing practices for ectopic beats before the analysis of non-linear HR dynamics.

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Section: Methodsmentioning

confidence: 99%

“…In the FD‐L analysis the number of sticks of various lengths needed to follow the time series curve is counted (Chau et al. , 1993).…”

Section: Methodsmentioning

confidence: 99%

Section: Analyses Of Non-linear Hr Dynamicsmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of methods for editing of ectopic beats in measurements of short‐term non‐linear heart rate dynamics

Tarkiainen

Kuusela

Tahvanainen

et al. 2007

Clin Physio Funct Imaging

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“…The following time domain, frequency domain and non-linear variables were used as heart rate variability measures: 1) The timedomain measures were standard deviation of IBI variability (SD), and the square root of the mean of the sum of the squares of differences between adjacent IBI's (RMSSD); 2) the frequencydomain measures were low frequency power (LF power, 0.04e0.15 Hz), and high frequency power (HF power, 0.15e1.00 Hz), and 3) the non-linear measures were sample entropy (SampEN) (Richman and Moorman, 2000) and fractal dimension based on the curve length (FD-L) (Chau et al, 1993) of IBI variability. RMSSD and HF power are the measures of parasympathetic cardiac modulation (Task Force, 1996).…”

Section: Physical Activity Monitoringmentioning

confidence: 99%

Heart rate variability related to effort at work

et al. 2011

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“…This is done by counting the number of sticks of various lengths needed to follow the curve. When the length of the stick is decreased, the shape of the curve can be followed better and, naturally, more sticks are needed (3,9). If the curve has fractal nature the number of sticks increases exponentially.…”

Section: Appendixmentioning

confidence: 99%

Nonlinear methods of biosignal analysis in assessing terbutaline-induced heart rate and blood pressure changes

Kuusela

Jartti

Tahvanainen

et al. 2002

American Journal of Physiology-Heart and Circulatory Physiology

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of biosignal analysis in assessing terbutaline-induced heart rate and blood pressure changes. Am J Physiol Heart Circ Physiol 282: H773-H781, 2002; 10.1152/ajpheart. 00559.2001.-The aim of this study was to characterize how different nonlinear methods characterize heart rate and blood pressure dynamics in healthy subjects at rest. The randomized, placebo-controlled crossover study with intravenous terbutaline was designed to induce four different stationary states of cardiovascular regulation system. The R-R interval, systolic arterial blood pressure, and heart rate time series were analyzed with a set of methods including approximate entropy, sample entropy, Lempel-Ziv entropy, symbol dynamic entropy, cross-entropy, correlation dimension, fractal dimensions, and stationarity test. Results indicate that R-R interval and systolic arterial pressure subsystems are mutually connected but have different dynamic properties. In the drug-free state the subsystems share many common features. When the strength of the baroreflex feedback loop is modified with terbutaline, R-R interval and systolic blood pressure lose mutual synchrony and drift toward their inherent state of operation. In this state the R-R interval system is rather complex and irregular, but the blood pressure system is much simpler than in the drug-free state. nonlinear dynamics; complexity; dimensionality; entropy TRADITIONAL LINEAR ANALYSIS methods of heart rate and blood pressure time series data, such as the time-and frequency-domain methods, measure the strength of oscillations in heart rate and blood pressure within a specific frequency range, for example, in the low-frequency (0.04-0.15 Hz) and high-frequency (0.15-0.4 Hz) bands. The spectral powers obtained can be used, for example, to estimate sympathetic and parasympathetic nervous activity and, with cross-spectral approaches, to characterize arterial baroreflex functions. Generally, the linear methods (time-and frequencydomain methods) are useful and have been widely adopted in studies of health and disease because results from linear methods are quite easy to interpret in physiological terms. But they also have limitations, and criticisms have been raised against their use (5).Multiple feedback loops in cardiovascular regulation systems make rapid adaptations possible under a large variety of physiological and environmental conditions. In analyzing heart rate and blood pressure time series with traditional linear methods, we lose a lot of information on the dynamic patterns used by the cardiovascular regulation systems to adjust heart rate and blood pressure. Linear methods have not been designed to yield information on the systems' inherent dynamic properties. Nonlinear methods of signal analysis can be more useful when characterizing complex dynamics. Thus the idea of using nonlinear statistics in the analysis of heart rate and blood pressure time series data is theoretically very sound and is a challenging objective for both cardiovascular physiologists and system theoreticians. Nonlinear ...

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Fractal Dimension of Heart Rate and Blood Pressure in Healthy Subjects and in Diabetic Subjects

Cited by 13 publications

References 13 publications

Comparison of methods for editing of ectopic beats in measurements of short‐term non‐linear heart rate dynamics

Comparison of methods for editing of ectopic beats in measurements of short‐term non‐linear heart rate dynamics

Heart rate variability related to effort at work

Nonlinear methods of biosignal analysis in assessing terbutaline-induced heart rate and blood pressure changes

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