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Results for “sample entropy”

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At least 19 recordsLinked to original sources
PubMed2000

Physiological time-series analysis using approximate entropy and sample entropy.

Entropy, as it relates to dynamical systems, is the rate of information production. Methods for estimation of the entropy of a system represented by a time series are not, however, well suited to analysis of the short and noisy data sets encountered in cardiovascular and other biological studies. Pincus introduced approximate entropy (ApEn), a set of measures of system complexity closely related to entropy, which is easily applied to clinical cardiovascular and other time series. ApEn statistics, however, lead to inconsistent results. We have developed a new and related complexity measure, sample entropy (SampEn), and have compared ApEn and SampEn by using them to analyze sets of random numbers with known probabilistic character. We have also evaluated cross-ApEn and cross-SampEn, which use cardiovascular data sets to measure the similarity of two distinct time series. SampEn agreed with theory much more closely than ApEn over a broad range of conditions. The improved accuracy of SampEn statistics should make them useful in the study of experimental clinical cardiovascular and other biological time series.

Entropy↗
PubMed2004

Complexity quantification of dense array EEG using sample entropy analysis.

In this paper, a time series complexity analysis of dense array electroencephalogram signals is carried out using the recently introduced Sample Entropy (SampEn) measure. This statistic quantifies the regularity in signals recorded from systems that can vary from the purely deterministic to purely stochastic realm. The present analysis is conducted with an objective of gaining insight into complexity variations related to changing brain dynamics for EEG recorded from the three cases of passive, eyes closed condition, a mental arithmetic task and the same mental task carried out after a physical exertion task. It is observed that the statistic is a robust quantifier of complexity suited for short physiological signals such as the EEG and it points to the specific brain regions that exhibit lowered complexity during the mental task state as compared to a passive, relaxed state. In the case of mental tasks carried out before and after the performance of a physical exercise, the statistic can detect the variations brought in by the intermediate fatigue inducing exercise period. This enhances its utility in detecting subtle changes in the brain state that can find wider scope for applications in EEG based brain studies.

Adult↗
PubMed2002

Sample entropy analysis of neonatal heart rate variability.

Abnormal heart rate characteristics of reduced variability and transient decelerations are present early in the course of neonatal sepsis. To investigate the dynamics, we calculated sample entropy, a similar but less biased measure than the popular approximate entropy. Both calculate the probability that epochs of window length m that are similar within a tolerance r remain similar at the next point. We studied 89 consecutive admissions to a tertiary care neonatal intensive care unit, among whom there were 21 episodes of sepsis, and we performed numerical simulations. We addressed the fundamental issues of optimal selection of m and r and the impact of missing data. The major findings are that entropy falls before clinical signs of neonatal sepsis and that missing points are well tolerated. The major mechanism, surprisingly, is unrelated to the regularity of the data: entropy estimates inevitably fall in any record with spikes. We propose more informed selection of parameters and reexamination of studies where approximate entropy was interpreted solely as a regularity measure.

Entropy↗
PubMed2004

Prediction of peptide conformation using a scale-transformed entropy sampling algorithm.

An algorithm for predicting the lowest energy structure of a peptide has been developed. High-energy barriers on an energy surface can be easily overcome by logarithmically transforming the energy space. For efficient optimizations, the energy space is searched using a scale-transformed entropy sampling method, and conformations specific to a primary structure of the peptide are sampled with large weights. The efficiency of the present method is demonstrated by calculations on cholecystokinin octapeptide (CCK-8).

Algorithms↗
PubMed2006

Sample entropy of ECG time series of fearful flyers: preliminary results.

Research within the framework of the nonlinear dynamical systems (NDS) in the field of anxiety disorders has shown that greater irregularity/complexity appears in the output from healthy systems. In this study we measured the Heart rate variability (HRV) and the sample sntropy (SampEn) of the ECG mV time series of fearful flyers (N = 15) and a matched control group (N = 15) when confronted with three combinations of feared stimuli (pictures, sounds, and pictures with sounds) as well as relaxing stimuli (pictures and sounds). Fearful flyers had lower SampEn than controls in all conditions, including baseline. Non-phobics showed significant entropy decreases from baseline in two out of three exposure conditions. No differences on HRV were found between groups, and HRV was not sensitive to condition changes. The main finding of the study is that the SampEn calculated on very short ECG mV recordings (10 to 60 seconds, easy to obtain in clinical settings) may be a useful diagnostic measure since it can distinguish fearful from non-fearful flyers.

Adult↗
PubMed2004

Sample entropy.

Explore the source record for details and available documents.

Journal Article↗
PubMed2007

Loss of complexity characterizes the heart rate response to experimental hemorrhagic shock in swine.

OBJECTIVE: To improve our ability to identify physiologic deterioration caused by critical illness, we applied nonlinear and frequency-domain analytical methods to R-to-R interval (RRI) and systolic arterial pressure (SAP) time series during hemorrhagic shock. DESIGN: Prospective, randomized, controlled trial. SETTING: Animal laboratory of a government research institute. SUBJECTS: Twenty swine (weight 36.4+/-0.11 kg). INTERVENTIONS: Fixed-volume hemorrhage followed by resuscitation; off-line analysis of RRI and SAP data. MEASUREMENTS AND MAIN RESULTS: Anesthetized swine (shock group, n=12) underwent withdrawal of 30 mL/kg blood in 10 mL/kg decrements. A control group (n=8) received maintenance fluids only. Electrocardiogram and arterial pressure waveforms were acquired at 500 Hz. Eight hundred-beat data sets were analyzed at six time points: at baseline, after each blood withdrawal, after lactated Ringer's resuscitation, and after infusion of shed blood. Nonlinear methods were used to estimate the complexity (approximate entropy, sample entropy, Lempel-Ziv entropy, normalized entropy of symbol dynamics), RRI bits per word, and fractal dimension by curve lengths and by dispersion analysis of the RRI and SAP time series. Fast Fourier transformation was used to measure the high-frequency and low-frequency powers of RRI and SAP. Baroreflex sensitivity was assessed in the time domain with the sequence method. Hemorrhagic shock caused decreases in RRI complexity as quantified by approximate entropy, sample entropy, and symbol dynamics; these changes were reversed by resuscitation. Similar but statistically insignificant changes in fractal dimension by curve lengths were seen. RRI high-frequency power decreased with hemorrhagic shock-indicating withdrawal of vagal cardiac input-and was restored by resuscitation. Similar changes in baroreflex sensitivity were seen. Hemorrhagic shock did not affect SAP complexity. CONCLUSIONS: Hemorrhagic shock caused a reversible decrease in RRI complexity; these changes may be mediated by changes in vagal cardiac control. Assessment of RRI complexity may permit identification of casualties with hemorrhagic shock.

Animals↗
PubMed2002

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.

Adult↗
PubMed2005

Nonlinear and chaos characteristics of heart period time series: healthy aging and postural change.

In this study we investigated nonlinear and linear characteristics of heart period variability with aging in supine and standing posture. Sixty healthy subjects (8-61 years) divided in three age groups participated in the study. Heart period variability was assessed by measurement of short-term scaling exponent, sample entropy, largest Lyapunov exponent and spectral low-frequency and high-frequency power. In standing, there was significant increase in short-term scaling exponent and largest Lyapunov exponent in all subjects, and significant decrease in sample entropy in children (<15 years) and young adults (15-39 years). Increasing age is associated with reduction in sample entropy in supine posture. Mutual effect of aging and postural change was reflected on heart rate and sample entropy. Correlation between low-frequency-to-high-frequency power ratio and short-term scaling exponent was found in supine posture. In standing both low-frequency and high-frequency powers are correlated with short-term scaling exponent and sample entropy. These results show that posture, standing compared to supine, has significant effect on nonlinear properties of heart period variability in healthy subjects while the influence of healthy aging is less pronounced. The findings indicate that intrinsic properties of heart period dynamics, reflected on nonlinear measures, are altered only by robust changes of autonomic modulation of heart rate.

Adolescent↗
PubMed2005

Mutual information function assesses autonomic information flow of heart rate dynamics at different time scales.

The autonomic information flow (AIF) represents the complex communication within the Autonomic Nervous System (ANS). It can be assessed by the mutual information function (MIF) of heart rate fluctuations (HRF). The complexity of HRF is based on several interacting physiological mechanisms operating at different time scales. Therefore one prominent time scale for HRF complexity analysis is not given a priori. The MIF reflects the information flow at different time scales. This approach is defined and evaluated in the present paper. In order to aggregate relevant physiological time scales, the MIF of HRF obtained from eight adult Lewis rats during the awake state, under general anesthesia, with additional vagotomy, and additional betal-adrenergic blockade are investigated. Physiologically relevant measures of the MIF were assessed with regard to the discrimination of these states. A simulation study of a periodically excited pendulum is performed to clarify the influence of the time scale of MIF in comparison to the Kolmogorov Sinai entropy (KSE) of that well defined system. The general relevance of the presented AIF approach was confirmed by comparing mutual information, approximate entropy, and sample entropy at their respective time scales.

Algorithms↗
PubMed2001

Temporal correlations and neural spike train entropy.

Sampling considerations limit the experimental conditions under which information theoretic analyses of neurophysiological data yield reliable results. We develop a procedure for computing the full temporal entropy and information of ensembles of neural spike trains, which performs reliably for limited samples of data. This approach also yields insight to the role of correlations between spikes in temporal coding mechanisms. The method, when applied to recordings from complex cells of the monkey primary visual cortex, results in lower rms error information estimates in comparison to a "brute force" approach.

Action Potentials↗
PubMed2006

Decrease in the heart rate complexity prior to the onset of atrial fibrillation.

AIMS: To assess heart rate complexity changes prior to the onset of atrial fibrillation (AF) using sample entropy. It has been proposed that the autonomic nervous system might have a role in the initiation of AF. METHODS AND RESULTS: The study included 25 patients with lone AF. Each record set contained two 30 min records from 25 subjects. Each patient had 30 min records containing the ECG immediately preceding an episode of AF (pre-AF) and 30 min of ECG during a period distant from any episode of AF (AFd). Sample entropy was used for complexity analysis. The sample entropy of R-R intervals was significantly reduced in the pre-AF period compared with the AFd period (0.45+/-0.25 vs. 0.78+/-0.46, P=0.003). The pre-AF periods were divided into three successive 10 min segments. There was a significant decreasing trend in entropy towards the onset of AF with linear mixed models (P=0.002). CONCLUSIONS: The heart rate complexity is reduced with a significant decreasing trend as assessed by R-R interval entropy prior to the onset of AF. There is a need for well-defined studies with larger patient groups in order to assess the entropy changes further and to look for possible changes, which might predict impending AF episodes.

Atrial Fibrillation↗
PubMed2006

Comparison of entropy-based regularity estimators: application to the fetal heart rate signal for the identification of fetal distress.

This paper considers the multiscale entropy (MSE) approach for estimating the regularity of time series at different scales. Sample entropy (SampEn) and approximate entropy (ApEn) are evaluated in MSE analysis on simulated data to enhance the main features of both estimators. We applied the approximate entropy and the sample entropy estimators to fetal heart rate signals on both single and multiple scales for an early identification of fetal sufferance antepartum. Our results show that the ApEn index significantly distinguishes suffering from normal fetuses between the 30th and the 35th week of gestation. Furthermore, our data shows that the MSE entropy values are reliable indicators of the fetal distress associated with the presence of a pathological condition at birth.

Algorithms↗
PubMed2005

[Analysis of heart rhythm variability by linear and non-linear dynamics methods].

INTRODUCTION: Spectral analysis of heart rhythm variability is a noninvasive method to study cardiovascular autonomic control. Nonlinear methods of analysis of heart rhythm variability may provide an additional information on properties of RR interval dynamics, which cannot be revealed by linear methods. The aim of this study was to investigate and to compare some parameters of deterministic chaos, tractal and spectral properties of heart rhythm variability in healthy subjects. METHODS: Sixty healthy subjects, who participated in this study, were divided in four different age groups: children (< 15 years old), young (15-24 years), adults (25-39 years) and middle-aged (40-61 years). We analyzed the heart period variability extracted from a 5-minute electrocardiograms recordings in supine rest. Short-term fractal scaling exponent, sample entropy, minimum embedding dimension and the largest Lyapunov exponent along with the spectral measures (low-frequency and high-frequency power) were determined. RESULTS: Heart frequency, short-term fractal scaling exponent, and the largest Lyapunov exponent did not differ between the tested groups (p > 0.05). Middle-aged subjects had the lower low-frequency power as compared to the children (p < 0.001) and the young (p < 0.05), and the lower high-frequency power as compared to the other three groups (p < 0.001). Middle-aged also had a significantly lower power of high frequency and sample entropy as compared to the other three groups (p < 0.001). Children had lower values of minimum embedding dimension compared to the middle aged (p < 0.001). Significant negative correlation between the age of the tested subjects, and the low-and, high-frequency power and sample entropy was found. CONCLUSION: The obtained results suggested that healthy aging was associated with significant alteration in heart rhythm dynamics reflected on a higher regularity and lower variability of heart rhythm time-series. Significant decrease in a high-frequency power with aging suggested that reduction in parasympathetic activity was the basic cause of these changes.

Adult↗
PubMed2006

Internal versus external intrapartum foetal heart rate monitoring: the effect on linear and nonlinear parameters.

The effect of foetal heart rate (FHR) acquisition mode on linear and nonlinear parameters is still largely unknown. In 33 normal labouring women, FHR signals were acquired simultaneously by an external ultrasound sensor applied to the maternal abdomen and an internal scalp electrode, in the minutes preceding delivery. For each case, the initial and final 5, 10 and 20 min segments were analysed, considering FHR signals at a frequency of 4 Hz (the frequency at which they are transmitted by the majority of commercialized foetal monitors). Several time and frequency domain linear and nonlinear FHR indices were computed in these segments, namely mean FHR, very low frequency (VLF), low frequency (LF), high frequency (HF), approximate entropy (ApEn) and sample entropy (SampEn). Parametric confidence intervals, statistical tests and correlation coefficients were calculated in order to evaluate the effect of internal versus external FHR monitoring modes on the considered indices. The whole evaluation was repeated using FHR signals at a frequency of 2 Hz. Most time domain linear indices were similar with external and internal monitoring in the initial and final segments of the tracings. However, linear frequency domain indices were poorly correlated in the final segments and had significantly different mean values in the initial segments. Nonlinear indices were significantly different in both initial and final segments. The correlation between 4 and 2 Hz sampled parameters was high for both linear and nonlinear indices (most correlation coefficient values ranging between 0.95 and 1) but nonlinear index values were significantly higher at 2 Hz. In conclusion, the mode used to acquire FHR signals and the sampling rate employed can significantly affect most FHR indices.

Algorithms↗