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 frequency-domain 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.

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the neurophysiological processes underpinning the SMR often vary over time and across subjects. Inherent intra- and inter-subject variability causes covariate shift in data distributions that impede the transferability of model parameters amongst sessions/subjects. Transfer learning includes machine learning-based methods to compensate for inter-subject and inter-session (intra-subject) variability manifested in EEG-derived feature distributions as a covariate shift for BCI. Besides transfer learning approaches, recent studies have explored psychological and neurophysiological predictors as well as inter-subject associativity assessment, which may augment transfer learning in EEG-based BCI. Here, we highlight the importance of measuring inter-session/subject performance predictors for generalized BCI frameworks for both normal and motor-impaired people, reducing the necessity for tedious and annoying calibration sessions and BCI training.

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.

Objective: To assess the effects of abruptly intensified physical training on cardiovascular control. Design: Retrospective longitudinal study. Setting: Research laboratory. Participants: Ten healthy athletes (5 men and 5 women) from track and field as well as triathlon. Interventions: A 2-week training camp, including daily stepwise increasing cycling tests, running of 40 minutes, and additional cycling of 60 minutes. Main Outcome Measurements: Time and frequency domain parameters of resting heart rate and blood pressure variability (HRV and BPV) and baroreflex sensitivity (BRS), before, during, and after the training camp. Results: We found significantly reduced HRV during the training camp (mean beat-to-beat interval: 1042 [937 to 1194] ms vs. 933 [832 to 1103] ms vs. 1055 [947 to 1183] ms, P < 0.01; root-mean-square of beat-to-beat interval differences: 68 [52 to 95] ms vs. 52 [38 to 71] ms vs. 61 [48 to 78] ms, P < 0.05). Further, BRS was significantly reduced: 25.2 (20.4 to 40.4) ms/mmHg vs. 17.0 (12.9 to 25.7) ms/mmHg vs. 25.7 (18.8 to 29.1) ms/mmHg, P < 0.05. These effects disappeared at a large degree after 3 to 4 days of recovery. Conclusion: Abruptly intensified physical training results in an altered autonomic cardiovascular activity towards parasympathetic inhibition and sympathetic activation that can be monitored by means of HRV and BRS analyses and might provide useful markers to avoid the overtraining syndrome.