Quantitative analysis of heart rate variability

Indian Journal of Science and Technology, 2016

Heart Rate Variability (HRV) has been established as a vital index for diagnostics and prognosis of a number of pathological conditions. Moreover, HRV is a proven indicator of autonomic balance. As HRV is a result of multiple responses acting at various time scales, these interactions need to be quantified. In this paper, a complexity measure called ApEn is utilized to quantify the complexity of HRV. This method is tested on age stratified standard Fantasia database from Physionet. It is observed that young subjects show higher HRV complexity than the older ones. The effect of tolerance threshold 'r' is also evaluated on the HRV complexity estimation of young and old subjects. Further, for r≥0.10, the complexity of HRV is higher for young subjects but the trend is reverse for r<0.10. Therefore, it is concluded that the tolerance threshold 'r' should be carefully selected for the complexity analysis of HRV.

2000

Lecture Notes in Computer Science, 2000

Standard parameters of heart rate variability are restricted in measuring linear effects, whereas nonlinear descriptions often suffer from the curse of dimensionality. An approach which might be capable of assessing complex properties is the calculation of entropy measures from normalised periodograms. Two concepts, both based on autoregressive spectral estimations are introduced here. To test the hypothesis that these entropy measures may improve the result of high risk stratification, they were applied to a clinical pilot study and to the data of patients with different cardiac diseases. The study shows that the entropy measures discussed here are useful tools to estimate the individual risk of patients suffering from heart failure. Further, the results demonstrate that the combination of different heart rate variability parameters leads to a better classification of cardiac diseases than single parameters.

Entropy

The complexity of a heart rate variability (HRV) signal is considered an important nonlinear feature to detect cardiac abnormalities. This work aims at explaining the physiological meaning of a recently developed complexity measurement method, namely, distribution entropy (DistEn), in the context of HRV signal analysis. We thereby propose modified distribution entropy (mDistEn) to remove the physiological discrepancy involved in the computation of DistEn. The proposed method generates a distance matrix that is devoid of over-exerted multi-lag signal changes. Restricted element selection in the distance matrix makes “mDistEn” a computationally inexpensive and physiologically more relevant complexity measure in comparison to DistEn.

2010

In this work, Refined Multiscale Entropy (RMSE) was applied to characterize risk of cardiac death in ischemic cardiomyopathy patients, analyzing heart rate variability (HRV) by means of RR series during daytime and nighttime. RMSE approach measures an entropy rate in different time scales of a series, giving a multiscale characterization of complexity of that series. RMSE showed statistically significant differences (p&#60;0.05) during daytime and nighttime only in middle time scales (τ=4–15 and τ=3–16, respectively). For these scales, RMSE was higher in low risk (SV) than in high risk (CM) group of cardiac death, indicating a reduction of the entropy-based complexity in CM when it was compared with SV. No statistical differences between risk groups were presented at time scale τ=1 (unfiltered original RR series). It can be concluded that the dynamics in middle time scales should be considered to better describe the HRV of patients with cardiac death.

Entropy

The time series of interbeat intervals of the heart reveals much information about disease and disease progression. An area of intense research has been associated with cardiac autonomic neuropathy (CAN). In this work we have investigated the value of additional information derived from the magnitude, sign and acceleration of the RR intervals. When quantified using an entropy measure, these time series show statistically significant differences between disease classes of Normal, Early CAN and Definite CAN. In addition, pathophysiological characteristics of heartbeat dynamics provide information not only on the change in the system using the first difference but also the magnitude and direction of the change measured by the second difference (acceleration) with respect to sequence length. These additional measures provide disease categories to be discriminated and could prove useful for non-invasive diagnosis and understanding changes in heart rhythm associated with CAN.

IEEE Transactions on Biomedical Engineering, 2001

An integrated approach to the complexity analysis of short heart period variability series ( 300 cardiac beats) is proposed and applied to healthy subjects during the sympathetic activation induced by head-up tilt and during the driving action produced by controlled respiration (10, 15, and 20 breaths/min, CR10, CR15, and CR20 respectively). The approach relies on: 1) the calculation of Shannon entropy (SE) of the distribution of patterns lasting three beats; 2) the calculation of a regularity index based on an entropy rate (i.e., the conditional entropy); 3) the classification of frequent deterministic patterns (FDPs) lasting three beats. A redundancy reduction criterion is proposed to group FDPs in four categories according to the number and type or of heart period changes: a) no variation (0V); b) one variation (1V); and c) two like variations (2LV); 4) two unlike variations (2UV). We found that: 1) the SE decreased during tilt due to the increased percentage of missing patterns; 2) the regularity index increased during tilt and CR10 as patterns followed each other according to a more repetitive scheme; and 3) during CR10, SE and regularity index were not redundant as the regularity index significantly decreased while SE remained unchanged. Concerning pattern analysis we found that: a) at rest mainly three classes (0V, 1V, and 2LV) were detected; b) 0V patterns were more likely during tilt; c) 1V and 2LV patterns were more frequent during CR10; and d) 2UV patterns were more likely during CR20. The proposed approach based on quantification of complexity allows a full characterization of heart period dynamics and the identification of experimental conditions known to differently perturb cardiovascular regulation.

Bioengineering, 2022

Background: Several methods have been proposed to estimate complexity in physiological time series observed at different time scales, with a particular focus on heart rate variability (HRV) series. In this frame, while several complexity quantifiers defined in the multiscale domain have already been investigated, the effectiveness of a multiscale Kolmogorov–Sinai (K-S) entropy has not been evaluated yet for the characterization of heartbeat dynamics. Methods: The use of the algorithmic information content, which is estimated through an effective compression algorithm, is investigated to quantify multiscale partition-based K-S entropy on publicly available experimental HRV series gathered from young and elderly subjects undergoing a visual elicitation task (Fantasia). Moreover, publicly available HRV series gathered from healthy subjects, as well as patients with atrial fibrillation and congestive heart failure in unstructured conditions have been analyzed as well. Results: Elderly p...

Traitement du Signal

The dynamical fluctuations in the Heart Rate Variability (HRV) signals show structures at multiple time scales revealing that complexity of the autonomic nervous system control of the heart is multiscale and hierarchical. Multiscale Entropy (MSE) and its variant Composite MSE (CMSE) were proposed to quantify the complexity at multiple time scales, however, these measures failed to quantify complexity accurately for short duration signals at large temporal scales. To address the downsides of MSE and CMSE, Multiscale Permutation Entropy (MPE) and Improved MPE (IMPE) were proposed. The preliminary results reveal that MPE and IMPE were able to distinguish healthy and pathological subjects, however, further studies are needed to investigate the robustness of these measures. In this study, we investigate the robustness of scale based PE measures in terms of dynamical information, induction of undefined entropy estimates for short duration signals and to classify HRV signals under different physiological and pathological conditions. The results were compared with SE, PE, MSE and CMSE. The MPE and IMPE along with MSE and CMSE provided accurate dynamical information. The results revealed that MPE and IMPE resolved the issue of inducing undefined entropy estimates and are robust in classifying healthy and different pathological subjects.

Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019

Despite the widespread diffusion of nonlinear methods for heart rate variability (HRV) analysis, the presence and the extent to which nonlinear dynamics contribute to short-term HRV are still controversial. This work aims at testing the hypothesis that different types of nonlinearity can be observed in HRV depending on the method adopted and on the physiopathological state. Two entropy-based measures of time series complexity (normalized complexity index, NCI) and regularity (information storage, IS), and a measure quantifying deviations from linear correlations in a time series (Gaussian linear contrast, GLC), are applied to short HRV recordings obtained in young (Y) and old (O) healthy subjects and in myocardial infarction (MI) patients monitored in the resting supine position and in the upright position reached through head-up tilt. The method of surrogate data is employed to detect the presence and quantify the contribution of nonlinear dynamics to HRV. We find that the three me...

Multimedia Tools and Applications, 2020

In this paper, based upon the appearance of patterns derived from a time series, we have investigated the suitability of multiscale entropy (MSE) technique for complexity quantification of cardiac rhythms in chronic pathological conditions. MSE analysis was developed to quantify the complexity of a wide variety of biomedical signals. Here, sample entropy (SampEn) technique was evaluated across multiple spatio-temporal scales. In SampEn, to find the appearance of repetitive patterns in multi-dimensional phase space, the threshold value 's' is prefixed as 0.2. However, the cardiac rhythms of some pathologies are characterized with considerable erratic beat-to-beat fluctuations, and hence, in accordance with that, the patterns concealed in the pathologic cardiac rhythms spread across a wider region of multidimensional phase space. But, fixed threshold value 's' assigns a fewer similarity pattern inside a circle of fixed dimensions, and hence, the higher entropy rate is associated with the chronic pathologic cardiac rhythms when compared to healthy cardiac rhythms. This flaw of SampEn is present in MSE, which leads to the wrong estimation of complexity associated with a time series. The outcome of this issue is clearly visible at low time scales, where periodto-period fluctuations in chronic pathologic cardiac rhythms and in randomized time series are significantly increased. In this present study, MSE analysis was performed over synthetic simulated database comprising of (white noise) WN and (power noise) PN signals. Further, MSE analysis was performed on the RR-interval series collected from (normal sinus rhythm) NSR group, and patients affected by (Atrial Fibrillation) AF. A fixed number of data samples 'M' of 10,000 were considered for each type of time series. Here, it is being observed that at some time scales, MSE assigns higher entropy to the WN and AF group, rather than PN and NSR group respectively, which is a wrong estimation of complexity. However, both the groups are discriminated efficiently by this algorithm. Further, it is concluded that MSE measure both the entropy and short term variations associated with a time series, but unable to investigate the real complexity (meaning full structural organization) present in a signal.

Philosophical Transactions of The Royal Society A: Mathematical, Physical and Engineering Sciences, 2009

Methods from nonlinear dynamics (NLD) have shown new insights into heart rate (HR) variability changes under various physiological and pathological conditions, providing additional prognostic information and complementing traditional time-and frequencydomain analyses. In this review, some of the most prominent indices of nonlinear and fractal dynamics are summarized and their algorithmic implementations and applications in clinical trials are discussed. Several of those indices have been proven to be of diagnostic relevance or have contributed to risk stratification. In particular, techniques based on mono-and multifractal analyses and symbolic dynamics have been successfully applied to clinical studies. Further advances in HR variability analysis are expected through multidimensional and multivariate assessments. Today, the question is no longer about whether or not methods from NLD should be applied; however, it is relevant to ask which of the methods should be selected and under which basic and standardized conditions should they be applied.

Geriatric Care

In recent years, many research groups are trying to quantify the physiological signals of an individual, proposing new models to assess the complex dynamics of biological control systems. Indeed, life coincides with the good handling of the structures in the organism and of physiological control mechanisms, while disease and death coincide with the loss of structure and of coordinated functions. The homeodynamic systems which normally govern health are the same that cause pathological events when activated inadequately, or rather, when the balance between order and chaos of the elementary physiological processes is no longer effectively controlled in relation to any type of stress, both external and internal to the body. In a complex system, loss or alteration of communication between physiological signals means pathology. In this paper a signal analysis method based on Entropy (E), Lyapunov exponent (1), Median Absolute Deviation (MAD), Multiscale Entropy (MSE), is proposed to esti...

Clinical Autonomic Research, 2012

PloS one, 2018

Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior. The single scale based traditional entropy measures yielded contradictory results about the dynamics of real world time series data of healthy and pathological subjects. Recently the multiscale entropy (MSE) algorithm was introduced for precise description of the complexity of biological signals, which was used in numerous fields since its inception. The original MSE quantified the complexity of coarse-grained time series using sample entropy. The original MSE may be unreliable for short signals bec...