Short-term heart rate complexity is reduced in patients with type 1 diabetes mellitus

Javorka, Michal; Trunkvalterova, Zuzana; Tonhajzerová, Ingrid; Javorková, J; Javorka, K; Baumert, Mathias

doi:10.1016/j.clinph.2007.12.017

Cited by 110 publications

(92 citation statements)

References 42 publications

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“…These results are in accordance with previous works where it has been reported that: (i) there are different dynamical patterns for LSCI and LDF data [26]; (ii) when the ROI size increases in LSCI data and when LSCI pixel values are averaged in ROI and followed with time, the patterns of the resulting time series approach the patterns of LDF signals [26,31,32,38]. Several studies have been carried on compression entropy [34,37,[39][40][41][42][43][44][45][46]. Through these works, it has been shown that compression entropy is sensitive to vagal heart rate modulation [40].…”

Section: Discussionsupporting

confidence: 81%

“…Furthermore, through compression entropy analyses, some authors have shown a significant reduction in the complexity of heart rate modulation in acute, untreated patients suffering from schizophrenia compared to control subjects [41,46]. Studies on HRV from patients with chronic heart failure and type 1 diabetes mellitus have also been conducted [43,44]. Thus, it has been shown that compression entropy is significantly lower in high risk heart failure subjects than in low risk heart failure subjects and that compression entropy is significantly reduced in patients with type 1 diabetes mellitus compared to healthy controls [44].…”

Section: Discussionmentioning

confidence: 99%

“…Studies on HRV from patients with chronic heart failure and type 1 diabetes mellitus have also been conducted [43,44]. Thus, it has been shown that compression entropy is significantly lower in high risk heart failure subjects than in low risk heart failure subjects and that compression entropy is significantly reduced in patients with type 1 diabetes mellitus compared to healthy controls [44]. The influence of negative mood on heart rate compression entropy in healthy subjects has also been investigated [45].…”

Section: Discussionmentioning

confidence: 99%

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Multiscale Compression Entropy of Microvascular Blood FlowSignals: Comparison of Results from Laser Speckle Contrastand Laser Doppler Flowmetry Data in Healthy Subjects

Humeau-Heurtier

Baumert

Ábrahám

2014

Entropy

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Microvascular perfusion is commonly used to study the peripheral cardiovascular system. Microvascular blood flow can be continuously and non-invasively monitored with laser speckle contrast imaging (LSCI) or with laser Doppler flowmetry (LDF). These two optical-based techniques give perfusion values in arbitrary units. Our goal is to better understand the perfusion time series given by each technique. For this purpose, we propose a nonlinear complexity analysis of LSCI and LDF time series recorded simultaneously in nine healthy subjects. This is performed through the computation of their multiscale compression entropy. The results obtained with LSCI time series computed from different regions of interest (ROI) sizes are examined. Our findings show that, for LSCI and LDF time series, compression entropy values are less than one for all of the scales analyzed. This suggests that, for all scales, there are repetitive structures within the data fluctuations. Moreover, at the largest scales studied, LDF signals seem to have structures that are different from those Entropy 2014, 16 5778 of Gaussian white noise. By opposition, this is not observed for LSCI time series computed from small ROI sizes.

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Section: Discussionsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Multiscale Compression Entropy of Microvascular Blood FlowSignals: Comparison of Results from Laser Speckle Contrastand Laser Doppler Flowmetry Data in Healthy Subjects

Humeau-Heurtier

Baumert

Ábrahám

2014

Entropy

Self Cite

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“…Javorka et al have revealed that many frequency and time domain linear indexes of HRV such as root-mean-square of successive differences of R-R intervals (RMSSD) are significantly reduced in patients with diabetes [11]. Costa et al proposed the nonlinear index of multiscale entropy (MSE) to reflect the complexity of physiological signals [12], which has been utilized additionally to the linear parameters.…”

Section: Introductionmentioning

confidence: 99%

Glycemic Control, Hand Activity, and Complexity of Biological Signals in Diabetes Mellitus

Lin

Haryadi

et al. 2017

Complexity

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Both glycemic control and handgrip strength affect microvascular function. Multiscale entropy (MSE) of photoplethysmographic (PPG) pulse amplitudes may differ by diabetes status and hand activity. Of a middle-to-old aged and right-handed cohort without clinical cardiovascular disease, we controlled age, sex, and weight to select the unaffected (no type 2 diabetes, = 36), the wellcontrolled diabetes (HbA1c < 8%, = 22), and the poorly controlled diabetes (HbA1c ≥ 8%, = 22) groups. MSEs were calculated from consecutive 1,500 PPG pulse amplitudes of bilateral index fingertips. The small-, medium-, and large-scale MSEs were defined as the average of scale 1 (MSE 1 ), scales 2-4 (MSE 2-4 ), and scales 5-10 (MSE 5-10 ), respectively. Intra-and intergroups were compared by one-and two-sample -tests, respectively. The dominant hand MSE 5-10 was lower in the poorly controlled diabetes group than the well-controlled diabetes and the unaffected (1.28 versus 1.52 and 1.56, = 0.019 and 0.001, resp.) groups, whereas the nondominant hand MSE 5-10 was lower in the well-and poorly controlled diabetes groups than the unaffected group (1.35 and 1.29 versus 1.58, = 0.008 and 0.005, resp.). The MSE 1 of dominant hand was higher than that of nondominant hand in the well-controlled diabetes (1.35 versus 1.10, = 0.048). In conclusion, diabetes status and hand dominance may affect the MSE of PPG pulse amplitudes.

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“…LZ77 was modified by Baumert et al in 2004 [37] to analysis heart rate time series called the compression entropy (H c ). in the field of spontaneous fluctuations of cardiovascular oscillations entropy based methods were applied to investigate fetal development [38], to determine age effects on the autonomic system [39][40][41], in differentiating pathological states from healthy states [42][43][44][45][46], for monitoring cardiac autonomic function [34,47], in typifying the effects of a pharmacological treatment [48][49][50][51], and in predicting risk [37,52].…”

Section: Introductionmentioning

confidence: 99%

Complex and Nonlinear Analysis of Heart Rate Variability in the Assessment of Fetal and Neonatal Wellbeing

Signorini

Magenes

Ferrario

et al. 2017

Complexity and Nonlinearity in Cardiovascular Signals

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Methods from nonlinear dynamics have shown new insights into alterations of the cardiovascular system under various physiological and pathological conditions, and thus providing additional prognostic information. In this chapter prominent complexity methods of non-linear dynamics as symbolic dynamics, Poincaré plot analyses, and compression entropy are introduced and their algorithmic implementations and application examples in clinical trials are provided. Especially, we will give their basic theoretical background, their main features and demonstrate their usefulness in different applications in the field of cardiovascular and cardiorespiratory time series analyses. IntroductionLinear time and frequency domain measures are often not sufficient enough to quantify the complex dynamics of physiological systems and their related time series. Therefore, various efforts have been made to apply nonlinear complexity measures to analyze, e.g. the heart rate variability (HRV) [1]. These approaches differ from the traditional time-and frequency domain HRV analyses because they quantify the signal properties instead of assessing only the magnitude or the frequency power of the heart rate time series. They assess the self-affinity of heartbeat fluctuations over multiple time scales (fractal measures); the regularity/irregularity or randomness of heartbeat fluctuations (entropy measures); the coarse-grained dynamics of HR fluctuations based on symbols (symbolic dynamics) and the heartbeat dynamics based on a simplified phase-space embedding [1].Symbolic dynamics is based on a coarse graining of the dynamics of a signal. The time series (in our cases the ECG or the noninvasively recorded blood pressure curve) are transformed into symbol sequences with symbols of a given alphabet. Some detail information is lost but the coarse dynamic behavior retains and

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Short-term heart rate complexity is reduced in patients with type 1 diabetes mellitus

Cited by 110 publications

References 42 publications

Multiscale Compression Entropy of Microvascular Blood FlowSignals: Comparison of Results from Laser Speckle Contrastand Laser Doppler Flowmetry Data in Healthy Subjects

Multiscale Compression Entropy of Microvascular Blood FlowSignals: Comparison of Results from Laser Speckle Contrastand Laser Doppler Flowmetry Data in Healthy Subjects

Glycemic Control, Hand Activity, and Complexity of Biological Signals in Diabetes Mellitus

Complex and Nonlinear Analysis of Heart Rate Variability in the Assessment of Fetal and Neonatal Wellbeing

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