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.

This consensus guideline discusses the electrocardiographic phenomenon of beat-to-beat QT interval variability (QTV) on surface electrocardiograms. The text covers measurement principles, physiological basis, and clinical value of QTV. Technical considerations include QT interval measurement and the relation between QTV and heart rate variability. Research frontiers of QTV include understanding of QTV physiology, systematic evaluation of the link between QTV and direct measures of neural activity, modelling of the QTV dependence on the variability of other physiological variables, distinction between QTV and general T wave shape variability, and assessing of the QTV utility for guiding therapy. Increased QTV appears to be a risk marker of arrhythmic and cardiovascular death. It remains to be established whether it can guide therapy alone or in combination with other risk factors. QT interval variability has a possible role in non-invasive assessment of tonic sympathetic activity.

Background-The pivot is critical to rotors postulated to maintain atrial fibrillation (AF). We reasoned that wavefronts circling the pivot should broaden the amplitude distribution of bipolar electrograms because of directional information encoded in these signals. We aimed to determine whether Shannon entropy (ShEn), a measure of signal amplitude distribution, could differentiate the pivot from surrounding peripheral regions and thereby assist clinical rotor mapping. Methods and Results-Bipolar electrogram recordings were studied in 4 systems: (1) computer simulations of rotors in a 2-dimensional atrial sheet; (2) isolated rat atria recorded with a multi-electrode array (n=12); (3) epicardial plaque recordings of induced AF in hypertensive sheep (n=11); and (4) persistent AF patients (n=10). In the model systems, rotation episodes were identified, and ShEn calculated as an index of amplitude distribution. In humans, ShEn distribution was analyzed at AF termination sites and with respect to complex fractionated electrogram mean. We analyzed rotation episodes in simulations (4 cycles) and animals (rats: 14 rotors, duration 80±81 cycles; sheep: 13 rotors, 4.2±1.5 cycles).The maximum ShEn bipole was consistently colocated with the pivot zone. ShEn was negatively associated with distance from the pivot zone in simulated spiral waves, rats, and sheep. ShEn was modestly inversely associated with complex fractionated electrogram; however, there was no relationship at the sites of highest ShEn. Conclusions-ShEn is a mechanistically based tool that may assist AF rotor mapping. (Circ Arrhythm Electrophysiol.2013;6:48-57.)