CHAOS THEORY AND PARAMETERS OF NONLINEAR DYNAMICS IN THE ASSESSMENT OF CARDIAC DISEASES
Heart failure (HF) is one of major causes for morbidity and mortality in general population. An increased sympathetic activity is a potential trigger for aggreviation of HF, serious cardiac arrhythmias and premature death. Heart rate variability (HRV) is an accepted approach to a non-invasive evaluation of the autonomic balance and modulation of the cardiovascular system. Reduction in HRV is associated with an increased mortality in various cardiac diseases, including HF. Parameters of nonlinear dynamics and fractal analysis quantify a number of HRV features which are different from standard HRV analysis based on time and frequency domain.
The aim of the study is to evaluate the autonomic imbalance status of patients with heart failure and to correlate clinical parameters considered for risk stratification with heart rate variability (HRV) and nonlinear dynamics parameters, like left ventricular ejection fraction (LVEF %), vs. standard deviation of all NN intervals – SDNN (ms) - and detrended fluctuation analysis - DFAa1 (ms) –approximative entropy
– ApEn (ms) - respectively sample entropy – SamEn (ms) – in a study group (n: 81). Patients have been classified in two groups considering the LVEF (%), group A ( n: 54, LVEF > 45 %, men: 36, women: 17) and group B ( n: 27, LVEF < 45 %, men: 20, women: 7 ). The analysed ECG signal was obtained from 24 hours Holter monitoring, using the Labtech Ltd, Hungary, 24 hours Holter system. Additional software has been used to perform ECG signal analysis, Kubios HRV v.2.1, Department of Applied Physics University of Eastern Finland.
The approximate entropy (ApEn) analysis was applied to quantify the regularity and complexity of time series, as well as unpredictability of fluctuations in time series. Approximate entropy parameters correlates negatively with LF/HF ratio (r: -0.30) and with SDNN (r: -0.30).
Dynamic analysis based on chaos theory point out the multifractal time series in heart failure patients who loss normal fractal characteristics and regularity in HRV. During the last years, a number of nonlinear methods from Chaos Theory, fractal analysis of time series, and Information Theory, have been proposed in this setting. The Approximate Entropy (ApEn), and later the Sample Entropy (SampEn), are in the last group. Approximate entropy (ApEn) was applied to quantify the regularity and complexity of time series, as well as unpredictability of fluctuations in time series. SampEn statistic was used to assess the variability of the RR-interval signals from 24-hour Holter recordings. We analyzed the behavior of RR intervals dynamics and of the heart rate in relationship with age, gender and day/night rhythm in patients with heart failure.
This study suggests that nonlinear analysis technique may complement conventional heart rate variability analysis. The nonlinear dynamic analysis of the RR intervals could have a larger clinical perspective linked to the circadian rhythm and to the age – gender relationship. Also it is necessary to improve ventricular arrhythmia prophylaxis in patients with heart failure and preserved left ventricular left ejection fraction.