Investigations of cardiac rhythm fluctuation using the DFA method (original) (raw)
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Detrended fluctuation analysis of heart rate by means of symbolic series
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Detrended fluctuation analysis (DFA) has been shown to be a useful tool for diagnosis of patients with cardiac diseases. The scaling exponents obtained with DFA are an indicator of power-law correlations in signal fluctuation, independently of signal amplitude and external trends. In this work, an approach based on DFA was proposed for analyzing heart rate variability (HRV) by means of RR series. The proposal consisted on transforming consecutive RR increments to symbols, according to an adapted symbolic-quantization. Three scaling exponents were calculated, α HF , α LF and α VLF , which correspond to the well known VLF, LF and HF frequency bands in the power spectral of the HRV. This DFA approach better characterized high and low risk of cardiac mortality in ischemic cardiomyiopathy patients than DFA applied to RR time series or RR increment series.
IEEE, 2013
Variation in time between two successive heart beats occurring due to internal and external stimulation causes Heart Rate Variability (HRV). HRV is a tool for indirect investigation of both cardiac and autonomic system function in both healthy and diseased condition. It has been speculated that HRV analysis by nonlinear method might bring potentially useful prognosis information into light which will be helpful for assessment of cardiac condition. In this study, HRV from two types of data sets (normal sinus rhythm and sinus arrhythmia) are analyzed which are stored in MIT-BIH (Massachusetts Institute of Technology – Beth Israel hospital) database, an extended collection of recorded physiological signals. Then two nonlinear methods, approximate entropy (ApEn) and detrended fluctuation analysis (DFA), have been applied to analyze HRV of both Arrhythmia patients and people having normal sinus rhythm. It has been clearly shown that nonlinear parameters obtained from these two methods reflect the opposite heart condition of the two types of subjects under study, healthy and diseased, by HRV measures. Thus, value of the nonlinear parameters found in this work can be used as standard when treating suspected patients for diagnosis of Arrhythmia. Also, by measuring these nonlinear parameter values, heart condition can be understood.
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Recent developments in the theory of nonlinear dynamics have paved the way for analyzing signals generated from nonlinear biological systems. The main purpose of the present work is based on the analysis of the ECG signal, initially extracting the features of ECG, which are used for the detection and/or classification of ECGs. For this work, Correlation Dimension (D2), Largest Lyapunov Exponent (LLE), Ap-proximate Entropy (ApEn), Sample Entropy (SampEn) and Poincare plot methods were used from nonlinear time series analysis to characterize human ECG signals obtained from 24 hour-Holter recording. Four groups of ECG signals have been investigated. D2 and LLE are increasingly used to classify ECG signals. ECG time series were classified according to the results obtained from computation of above chaotic features. Our results, obtained from clinical data, improved the previous studies, which allow one to distinguish between healthy group and patients groups with more confidence than th...
Methods derived from nonlinear dynamics for analysing heart rate variability
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.
Improved analysis of heart rate variability by methods of nonlinear dynamics
Journal of Electrocardiology, 1995
The traditional analysis of heart rate variability (HRV) in the time and frequency domains seems to be an independent predictive marker for sudden cardiac death. Because the usual applied methods of HRV analysis describe only linear or strong periodic phenomena, the authors have developed new methods of HRV analysis based on nonlinear dynamics. In that way, parameters are extracted that quantify more complex processes and their complicated relationships. These methods are symbolic dynamics that describes the beat-to-beat dynamics and renormalized entropy that compares the complexity of power spectra on a normalized energy level. In an initial investigation, the HRV of 35 healthy subjects and 39 cardiac patients have been analyzed. Using discriminant functions, the authors found an optimal (100%) differentiation between the group of healthy subjects (even using only an age-matched subgroup of 12 subjects) and that of patients after myocardial infarction with a high electrical risk (Lown 4b). Applying this discriminant function to a group of patients with low electrical risk, four patients show the same behavior indicative of a high risk score, which might be a sign for a hidden high risk, two patients show healthy behavim, and the remaining patients show a separate pattern. The use of new methods of nonlinear dynamics in combination with parameters of the time and frequency domains in HRV offers possibilities for improved classification of HRV behavior. It is suggested that this could lead to a more detailed classification of individual high risk, Key words: heart rate variability, nonlinear dynamics, sudden cardiac death, symbolic dynamics, renormalized entropy. Ventricular arrhythmias, especially ventricular tachycardia and ventricular fibrillation, are in many cases the cause of sudden cardiac death in patients after myocardial infarction. The improved identification of patients
2007 Computers in Cardiology, 2007
, have been widely used for quantifying the Heart Rate Variability (HRV) for cardiac risk stratification purposes. However, the physiological meaning of these measurements is not clear. Given that existing lumped parameter models contain a detailed physiological description of several of the circulatory system regulation processes, we hypothesize that controlled changes in these processes will highlight the physiological basis in DFA indices. We used a detailed lumped parameter model of HRV, introduced earlier [6]. Ten signals were generated in different physiological conditions. DFA coefficients α 1 , α 2 , and the Hurst exponent, were calculated. A clear disruption point was always observed. Modifications in sympatho-vagal activity yielded significant changes in α 1 when compared to basal, but not in α 2 or Hurst exponent. Modifications in non-nervous system mediated changes yielded significant differences only for peripheral resistance and heart period, only in α 1 .I n conclusion, the analysis of the effect of changes in the regulatory system on the HRV chaotic/fractal indices can be analyzed using detailed lumped parameter models.