Heart Rate Variability and Non-Linear Dynamics in Risk Stratification

Abstract

The time-domain measures and power–spectral analysis of heart rate variability (HRV) are classic conventional methods to assess the complex regulatory system between autonomic nervous system and heart rate and are most widely used. There are abundant scientific data about the prognostic significance of the conventional measurements of HRV in patients with various conditions, particularly with myocardial infarction. Some studies have suggested that some newer measures describing non-linear dynamics of heart rate, such as fractal measures, may reveal prognostic information beyond that obtained by the conventional measures of HRV. An ideal risk indicator could specifically predict sudden arrhythmic death as the implantable cardioverter-defibrillator (ICD) therapy can prevent such events. There are numerically more sudden deaths among post-infarction patients with better preserved left ventricular function than in those with severe left ventricular dysfunction. Recent data support the concept that HRV measurements, when analyzed several weeks after acute myocardial infarction, predict life-threatening ventricular tachyarrhythmias in patients with moderately depressed left ventricular function. However, well-designed prospective randomized studies are needed to evaluate whether the ICD therapy based on the assessment of HRV alone or with other risk indicators improves the patients’ prognosis. Several issues, such as the optimal target population, optimal timing of HRV measurements, optimal methods of HRV analysis, and optimal cutpoints for different HRV parameters, need clarification before the HRV analysis can be a widespread clinical tool in risk stratification.

Insights into the clinical and functional significance of cardiac autonomic dysfunction in Chagas disease

“…However, this was the sole index evaluated in this study and was derived from series of ECG R-R intervals employing the 24h Holter monitoring, which methodologically show some restrictions limiting the heart rate variability analysis and interpretation. To this regard, it should be reinforced that there are abundant data supporting the prognostic significance and the role as risk marker of some classical linear and newer non-linear measures of heart variability in different clinical conditions other than Chagas disease [48][49][50][51][52]69,71 . But, several factors influence the prognostic value of heart rate variability measurements when they are used in risk stratification, which among the more important are: a) the type and combination of indexes, b) the nature of the autonomic modulation that the indexes reflect, if overall or parasympathetic or sympathetic exclusive or balanced one, c) the circadian rhythm of heart rate variability, d) the epoch of evaluation during the development of the clinical, functional or pathological condition analysed, e) the severity of left ventricular dysfunction, f) the method employed to achieve, process and editing the ECG R-R intervals series, if derived from 5min short-term recording in controlled experimental setting, or long-term ambulatory 24h Holter recording under uncontrolled interfering factors and non-stationary data series, g) the nature of the selected adverse outcome 50,51,69,71 .…”

Correntropy-Based Pulse Rate Variability Analysis in Children with Sleep Disordered Breathing

“…Some studies have suggested that certain nonlinear measures of HRV are better predictors of future adverse events in various patient groups than the standard linear measures [13]. For example, Gutiérrez-Tobal et al proved the usefulness of the spectral entropy and multiscale entropy analyses of HRV to detect OSA [6], and Pan et al showed that multiscale entropy analysis may serve as a preliminary screening tool for assessing OSA severity prior to polysomnography [15].…”

“…The major advantage of CSD, is that it provides information on both the time structure and the statistical distribution of the data, which can be expected to facilitate the detection of heart rate nonlinearities that conventional techniques, based on second-order statistics, are otherwise unable to do. Nevertheless, the disadvantage of this analysis is that the physiological background of the nonlinear measures of HRV is much less well understood than that of the conventional measures [13].…”

“…HRV analysis is based on the inter-beat interval (RRI) variation calculated from Electrocardiography (ECG), whereas PRV analysis is based on the pulse-to-pulse interval (PPI) variation calculated from PPG [10]. Some studies have reported a chaotic heart beat behaviour [11,12] suggesting that measures describing nonlinear dynamics of heart rate, such as fractal measures, may reveal prognostic information beyond that obtained by conventional measures [6,10,13]. Several studies have proposed the use of entropy methods, i.e., sample entropy [14] and multiscale entropy [6,15], as measures of complexity to differentiate between the HRV pattern of normal and OSA subjects.…”

Heart Rate Variability, Ventricular Late Potentials and Heart Rate Turbulence as Indicators of Coronary Reperfusion in St Segment Elevation Myocardial Infarction

“…Од-нако в подавляющем большинстве работ как отече-ственных, так и зарубежных авторов показатели ВСР, ППЖ и ТСР анализировались ретроспективно по ре-зультатам суточного мониторирования электрокар-диограммы (ЭКГ), проведенного, преимущественно, в интервале от 2 сут до 2 нед от момента развития за-болевания, что ограничивает применение данных, полученных в ходе этих исследований [2][3][4][8][9][10][11][12].…”