Fractal dimension of heart rate and blood pressure in healthy subjects and in diabetic subjects

Complexity-Based Decoding of the Coupling Among Heart Rate Variability (HRV) and Walking Path

Walking is an everyday activity in our daily life. Because walking affects heart rate variability, in this research, for the first time, we analyzed the coupling among the alterations of the complexity of walking paths and heart rate. We benefited from the fractal theory and sample entropy to evaluate the influence of the complexity of paths on the complexity of heart rate variability (HRV) during walking. We calculated the fractal exponent and sample entropy of the R-R time series for nine participants who walked on four paths with various complexities. The findings showed a strong coupling among the alterations of fractal dimension (an indicator of complexity) of HRV and the walking paths. Besides, the result of the analysis of sample entropy also verified the obtained results from the fractal analysis. In further studies, we can analyze the coupling among the alterations of the complexities of other physiological signals and walking paths.

Heart rate variability related to effort at work

Changes in autonomic nervous system function have been related to work stress induced increases in cardiovascular morbidity and mortality. Our purpose was to examine whether various heart rate variability (HRV) measures and new HRV-based relaxation measures are related to self-reported chronic work stress and daily emotions. The relaxation measures are based on neural network modelling of individual baseline heart rate and HRV information. Nineteen healthy hospital workers were studied during two work days during the same work period. Daytime, work time and night time heart rate, as well as physical activity were recorded. An effort-reward imbalance (ERI) questionnaire was used to assess chronic work stress. The emotions of stress, irritation and satisfaction were assessed six times during both days. Seventeen subjects had an ERI ratio over 1, indicating imbalance between effort and reward, that is, chronic work stress. Of the daily emotions, satisfaction was the predominant emotion. The daytime relaxation percentage was higher on Day 2 than on Day 1 (4 ± 6% vs. 2 ± 3%, p < 0.05) and the night time relaxation (43 ± 30%) was significantly higher than daytime or work time relaxation on the both Days. Chronic work stress correlated with the vagal activity index of HRV. However, effort at work had many HRV correlates: the higher the work effort the lower daytime HRV and relaxation time. Emotions at work were also correlated with work time (stress and satisfaction) and night time (irritation) HRV. These results indicate that daily emotions at work and chronic work stress, especially effort, is associated with cardiac autonomic function. Neural network modelling of individual heart rate and HRV information may provide additional information in stress research in field conditions.

Sympathetic nerve activity and heart rate variability during severe hemorrhagic shock in sheep

INTRODUCTION

In this study we explored direct and indirect measures of autonomic nervous system function, as well as changes in cardiovascular complexity, during hemorrhagic shock (HS).

METHODS

HS was induced in anesthetized sheep (n=8) by removing 40 ml/kg of blood in four 10 ml/kg steps over 40 min. Resuscitation was performed with lactated Ringer's and re-infusion of shed blood. Renal sympathetic nerve activity (RSNA) was measured by microneurography. Spectral analysis of heart rate variability (HRV) employed fast-Fourier transformation of the R-to-R interval (RRI) of the EKG. This generated the normalized high-frequency (HFnu) and low-frequency (LFnu) powers of the RRI, and their ratio (LFnu/HFnu, a proposed index of sympatho-vagal balance). Additionally, non-linear methods were applied: RRI complexity was measured by approximate (ApEn) and sample (SampEn) entropy methods; RRI fractal dimension was measured by curve lengths (FDCL). Plasma catecholamines were determined by HPLC.

RESULTS

The model caused profound HS; 2/8 animals survived till the end of resuscitation. RSNA increased in 7/8 sheep and, as HS progressed, multiple burst complexes were identified followed by sympathetic withdrawal. Concomitant decreases in HFnu and increases in LFnu/HFnu occurred after 20 ml/kg blood loss. ApEn and FDCL decreased after withdrawal of 40 ml/kg of blood. Catecholamine concentrations increased throughout HS. LFnu/HFnu and RSNA were not linearly correlated.

CONCLUSIONS

HS led to an increase in RSNA with subsequent withdrawal. LFnu/HFnu increased during HS in association with vagal withdrawal and loss of RRI complexity. RRI complexity may in part reflect vagal modulation of the heart rate. Changes in directly measured tonic sympathetic traffic do not correlate with non-invasive measures of autonomic modulation of the heart.

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

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.

Weaning From Mechanical Ventilation: A Retrospective Analysis Leading to a Multimodal Perspective

Practitioners' decision for mechanical aid discontinuation is a challenging task that involves a complete knowledge of a great number of clinical parameters, as well as its evolution in time. Recently, an increasing interest on respiratory pattern variability as an extubation readiness indicator has appeared. Reliable assessment of this variability involves a set of signal processing and pattern recognition techniques. This paper presents a suitability analysis of different methods used for breathing pattern complexity assessment. The contribution of this analysis is threefold: 1) to serve as a review of the state of the art on the so-called weaning problem from a signal processing point of view; 2) to provide insight into the applied processing techniques and how they fit into the problem; 3) to propose additional methods and further processing in order to improve breathing pattern regularity assessment and weaning readiness decision. Results on experimental data show that sample entropy outperforms other complexity assessment methods and that multidimensional classification does improve weaning prediction. However, the obtained performance may be objectionable for real clinical practice, a fact that paves the way for a multimodal signal processing framework, including additional high-quality signals and more reliable statistical methods.

Stochastic model for heart-rate fluctuations

A normal human heart rate shows complex fluctuations in time, which is natural, because the heart rate is controlled by a large number of different feedback control loops. These unpredictable fluctuations have been shown to display fractal dynamics, long-term correlations, and 1/f noise. These characterizations are statistical and they have been widely studied and used, but much less is known about the detailed time evolution (dynamics) of the heart-rate control mechanism. Here we show that a simple one-dimensional Langevin-type stochastic difference equation can accurately model the heart-rate fluctuations in a time scale from minutes to hours. The model consists of a deterministic nonlinear part and a stochastic part typical to Gaussian noise, and both parts can be directly determined from the measured heart-rate data. Studies of 27 healthy subjects reveal that in most cases, the deterministic part has a form typically seen in bistable systems: there are two stable fixed points and one unstable one.

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

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.