Complexity of heartbeat interval series in young healthy trained and untrained men

Abstract

The origin of heart rate variability (HRV) is largely in parasympathetic activity. The direct influence of sympathetic activity and other control mechanisms, especially at an increased HR, is not well understood. The objectives of the study were to investigate the influence of increasing HR on the properties of heartbeat interval (RR) series in young healthy subjects. ECG was recorded in 9 trained and 11 untrained young men during supine rest, standing, incremental running exercise and relaxation. During exercise, a breath-to-breath gas exchange was monitored. The RR time series analysis included the spectral analysis, detrended fluctuations analysis method and sample entropy (SampEn) calculation. During exercise, spectral powers were reduced dramatically in both groups. The dependence of short-term scaling exponent (alpha(1)) on the RR included a characteristic maximum, while SampEn for the same value of the RR had a minimum. The value of HR corresponding to the maximum of alpha(1) and minimum of SampEn (IHR) corresponded to the intrinsic HR obtained by an autonomic blockade. In trained subjects, the curves alpha(1) versus RR and SampEn versus RR were moved toward larger RR, compared with control. For HR values higher than IHR, alpha(1) decreased and SampEn increased. These results reveal that the complexity of the heart rhythm above intrinsic HR decreases with an increase in HR. We suggest that at the highest HR intrinsic heart control is reflected in the heart rhythm. We point out the possibility of developing a new non-invasive method for the determination of intrinsic HR from the curve alpha(1) versus RR.

Correlations in magnitude series to assess nonlinearities: Application to multifractal models and heartbeat fluctuations

“…5.a), it has also been found that exercise modifies the distribution of the power spectrum by reducing the low frequency components [16,34,35] and introducing very high frequencies related to the respiration rate [36], decreases the sample entropy [37]. Also, the linear correlations measured by the short scale DFA exponent (α 1 ) are not only reduced with exercise [37,38] but also can be correlated with the intensity of the exercise [39]. Nevertheless it is fair to say that the opposite result can also be also found in the literature [40].…”

Applying fractal analysis to short sets of heart rate variability data

“…This was motivated by the fact that in some practical circumstances short-term recordings of HRV data are more likely to be obtained [3,24,33,36]. Also important to consider is the analysis of any short-term database already collected that could be of interest to characterise using a fractal framework, and the assessment of nonsteady-state conditions such as dynamical exercise [20,39]. To our knowledge the statistical possibility of obtaining reliable estimations of a 1 has not been explicitly addressed or mentioned in studies using short-term recordings of HRV data [3,16,20,36,37,43,45,48].…”

Non-invasive electrocardiographic assessments of cardiac autonomic modulation in individuals with spinal cord injury

“…For example, measures of HR complexity while under vagal blockade have been shown to be unaffected, 31,32 reduced 20 or almost completely abolished. [33][34][35] Further, HR complexity is reduced by standing 36 and increased during low-intensity dynamic exercise 25 but unaltered during sympathetic blockade, 35 passive head-up tilt 25 or autonomic stress. 35 Differences in circulating catecholamine concentrations evoked by each stimulus have been proposed to explain divergent complexity responses, 25 but this concept requires further investigation.…”

“…Examination of the short-term fractal scaling exponent (α 1 ) determined by detrended fluctuation analysis has also produced conflicting results. This measure, which quantifies the fractal-like correlation properties of the HR signal, has been shown to be reduced by norepinephrine infusion, 37 cold face immersion 38 or progressive dynamic exercise 36 and increased during standing, 36 low-intensity dynamic exercise, 25 passive head-up tilt 25 or cold hand immersion, 38 and unaffected 35 or increased 25,32 by atropine administration. Nonetheless, similar to linear HRV, the majority of non-linear measures of HR appear to be impacted more by cardiac vagal than sympathetic modulation.…”