Heart rate variability and nonlinear dynamic analysis in patients with stress-induced cardiomyopathy

Abstract

Complexity-based analyses may quantify abnormalities in heart rate variability (HRV). The aim of this study was to investigate the clinical and prognostic significances of dynamic HRV changes in patients with stress-induced cardiomyopathy Takotsubo syndrome (TS) by means of linear and nonlinear analysis. Patients with TS were included in study after complete noninvasive and invasive cardiovascular diagnostic evaluation and compared to an age and gender matched control group of healthy subjects. Series of R-R interval and of ST-T interval values were obtained from 24-h ECG recordings after digital sampling. HRV analysis was performed by 'range rescaled analysis' to determine the Hurst exponent, by detrended fluctuation analysis to quantify fractal long-range correlation properties, and by approximate entropy to assess time-series predictability. Short- and long-term fractal-scaling exponents were significantly higher in patients with TS in acute phases, opposite to lower approximate entropy and Hurst exponent, but all variables normalized in a few weeks. Dynamic HRV analysis allows assessing changes in complexity features of HRV in TS patients during the acute stage, and to monitor recovery after treatment, thus complementing traditional ECG and clinically analysis.

Nonlinear Analysis of Heart Rate Variability: A Comprehensive Review

“…Twenty-five female patients (60.2 ± 8.3 years) with symptoms and signs of stress-induced cardiomyopathy, were identified and included in the study and the control group consisted of 50 randomly selected age-matched (mean age 63.1 ± 7.2 years) healthy women. The values found for alpha1 and alpha2 were 1.11 ± 0.05 and 1.18 ± 0.04 for the control group and 1.31 ± 0.06 and 1.25 ± 0.05 for the group with cardiomyopathy showing a fractal behavior in the controls and a trend to strongly correlated behavior in the study group [19] . What one finds is that regardless of the studied disease or age, and even sample size and acquisition methodology, values that deviate from the normal value of 1.0 (increasing or decreasing) are associated with higher morbid gravity or worse prognosis revealing loss of fractal behavior toward random or strongly correlated behavior.…”

“…These values were 0.98 ± 0.31 and 0.76 ± 0.43 for alfa1 with 1.01 ± 0.09 and 0.99 ± 0.18 for alpha2, respectively. Krstacic G et al (2012) tested the applicability of nonlinear methods for HRV performance evaluation in patients with stressinduced cardiomyopathy (Takotsubo Syndrome or "Broken Heart Syndrome") whose pathogenesis is possibly related to acute release of large amounts of stress hormones leading to effect known as 'catecholamine induced myocardial stunning'. Twenty-five female patients (60.2 ± 8.3 years) with symptoms and signs of stress-induced cardiomyopathy, were identified and included in the study and the control group consisted of 50 randomly selected age-matched (mean age 63.1 ± 7.2 years) healthy women.…”

“…H is directly related to fractal dimension, D, where 1 < D < 2, such that D = 2 -H. Alternatively the local Hurst Exponent is related to the exponent αof the DFA method by the relationship α= 1+ H [26] . Krstacic G et al [19] (2012) testing the applicability of nonlinear methods for HRV performance evaluation in patients with stressinduced cardiomyopathy (Takotsubo Syndrome or "Broken Heart Syndrome") evaluated twenty-five female patients (60.2 ± 8.3 years) with symptoms and signs of stress-induced cardiomyopathy and 50 randomly selected age-matched (mean age 63.1 ± 7.2 years), healthy women, employing a measure of the Hurst exponent. The value found for the control group was 0.84 ± 0.05 and 0.58 ± 0.09 for the group with cardiomyopathy (P < 0.001).…”

The Influence of Corticosteroids on Heart Rate Variablity in Acute Cervical Spinal Cord Injury

“…Analysis of HRV has been used to assess autonomic function. It is altered in many diseases [8][9][10][11] . Th e parameters of HRV have been studied in patients with SCI and calculated from 24-hour Holter electrocardiogram (ECG) 12,13 .…”

Heart Rate Analysis by Sparse Representation for Acute Pain Detection

“…A study by Sisto et al [24] reported a neonatal pain detector using an acoustic analysis algorithm of crying to provide an objective estimate of neonatal pain. The heart rate itself was further studied by applying mathematical tools such as the heart rate variability (HRV) and other spectral analysis methods (SAM) like the Fourier transform [3,4,16,20]. Extensive studies on adult and pediatric populations using SAM have not managed to produce a reliable, real-time pain detector [17,21,25].…”