Ruben Fossion

Heart Rate and Systolic Blood Pressure Variability in the Time Domain in Patients with Recent and Long-Standing Diabetes Mellitus

Diabetes Mellitus (DM) affects the cardiovascular response of patients. To study this effect, interbeat intervals (IBI) and beat-to-beat systolic blood pressure (SBP) variability of patients during supine, standing and controlled breathing tests were analyzed in the time domain. Simultaneous noninvasive measurements of IBI and SBP for 30 recently diagnosed and 15 long-standing DM patients were compared with the results for 30 rigorously screened healthy subjects (control). A statistically significant distinction between control and diabetic subjects was provided by the standard deviation and the higher moments of the distributions (skewness, and kurtosis) with respect to the median. To compare IBI and SBP for different populations, we define a parameter, α, that combines the variability of the heart rate and the blood pressure, as the ratio of the radius of the moments for IBI and the same radius for SBP. As diabetes evolves, α decreases, standard deviation of the IBI detrended signal diminishes (heart rate signal becomes more “rigid”), skewness with respect to the median approaches zero (signal fluctuations gain symmetry), and kurtosis increases (fluctuations concentrate around the median). Diabetes produces not only a rigid heart rate, but also increases symmetry and has leptokurtic distributions. SBP time series exhibit the most variable behavior for recently diagnosed DM with platykurtic distributions. Under controlled breathing, SBP has symmetric distributions for DM patients, while control subjects have non-zero skewness. This may be due to a progressive decrease of parasympathetic and sympathetic activity to the heart and blood vessels as diabetes evolves.

Time-Series Analysis of Embodied Interaction: Movement Variability and Complexity Matching As Dyadic Properties.

There is a growing consensus that a fuller understanding of social cognition depends on more systematic studies of real-time social interaction. Such studies require methods that can deal with the complex dynamics taking place at multiple interdependent temporal and spatial scales, spanning sub-personal, personal, and dyadic levels of analysis. We demonstrate the value of adopting an extended multi-scale approach by re-analyzing movement time-series generated in a study of embodied dyadic interaction in a minimal virtual reality environment (a perceptual crossing experiment). Reduced movement variability revealed an interdependence between social awareness and social coordination that cannot be accounted for by either subjective or objective factors alone: it picks out interactions in which subjective and objective conditions are convergent (i.e., elevated coordination is perceived as clearly social, and impaired coordination is perceived as socially ambiguous). This finding is consistent with the claim that interpersonal interaction can be partially constitutive of direct social perception. Clustering statistics (Allan Factor) of salient events revealed fractal scaling. Complexity matching defined as the similarity between these scaling laws was significantly more pronounced in pairs of participants as compared to surrogate dyads. This further highlights the multi-scale and distributed character of social interaction and extends previous complexity matching results from dyadic conversation to non-verbal social interaction dynamics. Trials with successful joint interaction were also associated with an increase in local coordination. Consequently, a local coordination pattern emerges on the background of complex dyadic interactions in the PCE task and makes joint successful performance possible.

Scale invariance as a symmetry in physical and biological systems: listening to photons, bubbles and heartbeats

Many dynamical systems from different areas of knowledge can be studied within the theoretical framework of time series, where the system can be considered as a black box, that only needs to be “listened” to. In this framework, non‐correlated series (white noise) and strongly correlated series (brownian noise or periodic series) constitute two extremes. Certain dynamical systems auto‐organize in a critical state that is characterized by 1/f or flicker noise. The family of fβ noises (β≤0) is fractal because fragments of the series are statistically identical to the original time series. 1/f noise (β = −1) is critical because it maximizes important complexity‐related quantities as memory, information content, efficiency and fractality. 1/f noise has been observed in classical systems, but also in quantum systems, and could possibly offer a unifying bridge of understanding between the macroscopic and the quantum world. In the present article, we will discuss some examples from both worlds.

Loss of Breathing Modulation of Heart Rate Variability in Patients with Recent and Long Standing Diabetes Mellitus Type II

Healthy subjects under rhythmic breathing have heart interbeat intervals with a respiratory band in the frequency domain that can be an index of vagal activity. Diabetes Mellitus Type II (DM) affects the autonomic nervous system of patients, thus it can be expected changes on the vagal activity. Here, the influence of DM on the breathing modulation of the heart rate is evaluated by analyzing in the frequency domain heart interbeat interval (IBI) records obtained from 30 recently diagnosed, 15 long standing DM patients, and 30 control subjects during standardized clinical tests of controlled breathing at 0.1 Hz, supine rest and standing upright. Fourier spectral analysis of IBI records quantifies heart rate variability in different regions: low-frequencies (LF, 0.04–0.15 Hz), high-frequencies (HF, 0.15–0.4 Hz), and a controlled breathing peak (RP, centered around 0.1 Hz). Two new parameters are introduced: the frequency radius rf (square root of the sum of LF and HF squared) and β (power of RP divided by the sum of LF and HF). As diabetes evolves, the controlled breathing peak loses power and shifts to smaller frequencies, indicating that heart rate modulation is slower in diabetic patients than in controls. In contrast to the traditional parameters LF, HF and LF/HF, which do not show significant differences between the three populations in neither of the clinical tests, the new parameters rf and β, distinguish between control and diabetic subjects in the case of controlled breathing. Sympathetic activity that is driven by the baroreceptor reflex associated with the 0.1 Hz breathing modulations is affected in DM patients. Diabetes produces not only a rigid heartbeat with less autonomic induced variability (rf diminishes), but also alters the coupling between breathing and heart rate (reduced β), due to a progressive decline of vagal and sympathetic activity.

Multiscale adaptive analysis of circadian rhythms and intradaily variability: Application to actigraphy time series in acute insomnia subjects

Circadian rhythms become less dominant and less regular with chronic-degenerative disease, such that to accurately assess these pathological conditions it is important to quantify not only periodic characteristics but also more irregular aspects of the corresponding time series. Novel data-adaptive techniques, such as singular spectrum analysis (SSA), allow for the decomposition of experimental time series, in a model-free way, into a trend, quasiperiodic components and noise fluctuations. We compared SSA with the traditional techniques of cosinor analysis and intradaily variability using 1-week continuous actigraphy data in young adults with acute insomnia and healthy age-matched controls. The findings suggest a small but significant delay in circadian components in the subjects with acute insomnia, i.e. a larger acrophase, and alterations in the day-to-day variability of acrophase and amplitude. The power of the ultradian components follows a fractal 1/f power law for controls, whereas for those with acute insomnia this power law breaks down because of an increased variability at the 90min time scale, reminiscent of Kleitman’s basic rest-activity (BRAC) cycles. This suggests that for healthy sleepers attention and activity can be sustained at whatever time scale required by circumstances, whereas for those with acute insomnia this capacity may be impaired and these individuals need to rest or switch activities in order to stay focused. Traditional methods of circadian rhythm analysis are unable to detect the more subtle effects of day-to-day variability and ultradian rhythm fragmentation at the specific 90min time scale.

Chaotic dynamics in collective models of nuclei

We present results of an extensive analysis of classical and quantum signatures of chaos in the geometric collective model (GCM) and the interacting boson model (IBM) of nuclei. Apart from comparing the regular fraction of the classical phase space and the Brody parameter for the nearest neighbor spacing distribution in the quantum case, we also adopt (i) the Peres lattices allowing one to distinguish ordered and disordered parts of spectra and to reveal main ordering principles of quantum states, (ii) the geometrical method to determine the position where the transition from order to chaos occurs, and (iii) we look for the 1/fα power law in the power spectrum of energy level fluctuations. The Peres method demonstrates the adiabatic separation of collective rotations in the IBM.

A physicist’s view of homeostasis: how time series of continuous monitoring reflect the function of physiological variables in regulatory mechanisms

OBJECTIVE Homeostasis is one of the key concepts of physiology and the basis to understand chronic-degenerative disease and human ageing, but is difficult to quantify in clinical practice. The variability of time series resulting from continuous and non-invasive physiological monitoring is conjectured to reflect the underlying homeostatic regulatory processes, but it is not clear why the variability of some variables such as heart rate gives a favourable health prognosis whereas the variability of other variables such as blood pressure implies an increased risk factor. The purpose of the present contribution is to quantify homeostasis using time-series analysis and to offer an explanation for the phenomenology of physiological time series. APPROACH Within the context of network physiology, which focusses on the interactions between various variables at multiple scales of time and space, it may be understood that different physiological variables may play distinct roles in their respective regulatory mechanisms. In the present contribution, we distinguish between regulated variables, such as blood pressure or core temperature, and physiological responses, such as heart rate and skin temperature. MAIN RESULTS We give evidence that in optimal conditions of youth and health the former are characterized by Gaussian statistics, low variability and represent the stability of the internal environment, whereas the latter are characterized by non-Gaussian distributions, large variability and reflect the adaptive capacity of the human body; in the adverse conditions of ageing and/or disease, adaptive capacity is lost and the variability of physiological responses is diminished, and as a consequence the stability of the internal environment is compromised and its variability increases. SIGNIFICANCE Time-series analysis allows one to quantify homeostasis in the optimal conditions of youth and health and the degradation of homeostasis or homeostenosis in the adverse conditions of ageing and/or disease, and may offer an alternative approach to diagnosis in clinical practice.

Homeostasis from a Time-Series Perspective: An Intuitive Interpretation of the Variability of Physiological Variables

Homeostasis implies the approximate constancy of specific regulated variables, where the independence of the internal from the external environment is ensured by adaptive physiological responses carried out by other so-called effector variables. The loss of homeostasis is the basis to understand chronic-degenerative disease and age-associated frailty. Technological advances presently allow to monitor a large variety of physiological variables in a non-invasive and continuous way and the statistics of the resulting physiological time series is thought to reflect the dynamics of the underlying control mechanisms. Recent years have seen an increased interest in the variability and/or complexity analysis of physiological time series with possible applications in pathophysiology. However, a general understanding is lacking for which variables variability is an indicator of good health (e.g., heart rate variability) and when on the contrary variability implies a risk factor (e.g., blood pressure variability). In the present contribution, we argue that in optimal conditions of youth and health regulated variables and effector variables necessarily exhibit very different statistics, with small and large variances, respectively, and that under adverse circumstances such as ageing and/or chronic-degenerative disease these statistics degenerate in opposite directions, i.e. towards an increased variability in the case of regulated variables and towards a decreased variability for effector variables. We demonstrate this hypothesis for a simple mathematical model of a thermostat, and for blood pressure and body temperature homeostasis for healthy controls and patients with metabolic disease, and suggest that this scheme may explain the general phenomenology of physiological variables of homeostatic regulatory mechanisms.

Looking for Biomarkers in Physiological Time Series

From the point of view of Complexity Sciences, health can be considered as the state of dynamical balance between robustness and adaptability to the changes in the environment. We consider that any human disease can be found in physiological time series by deviations from this point that reflects the loss of this balance. Thus, it is possible to find biomarkers based on non-invasive physiological parameters that characterize the critical healthy state, and could help as early warnings auxiliary for clinical diagnoses of different diseases. In this work, we present a time-domain analysis using the distribution moments, autocorrelation function, Poincare diagrams, and the spectral analysis of interbeat intervals and blood pressure time series for control subjects of different age and gender, and diabetic patients. As a preliminary result, a statistical significant difference was found between health and disease in the statistical moments of blood pressure and heart rate variability that can be proposed as biomarkers.

Data Mining and Time-Series Analysis as Two Complementary Approaches to Study Body Temperature in Obesity

Obesity is becoming a pandemic worldwide but the mechanisms that cause obesity are not well understood. One possibility are metabolic differences between lean and obese people, for which body temperature may offer a proxy which is relatively easy to measure. In the present contribution, we present results from two complementary methodological approaches to measure skin temperature as a function of body weight: in the first study temperature at the axilla and anthropometric measures were collected at a single time point in 1,073 male and female employees of all ages of the Universidad Nacional Autónoma de México (UNAM), whereas in the second study a 1-week continuous monitoring was realized of the skin temperature of the non-dominant wrist of 22 male young adults. In spite of the methodological differences, both studies indicate a higher mean temperature of the obese with respect to the lean subjects, possibly reflecting how obese people offset excess calorie intake by a higher heat transfer to the environment. On the other hand, with respect to the variance of the temperature over groups of underweight, normal weight, overweight and obese subjects, the first study that was realized in controlled circumstances did not detect any differences between groups, whereas the differences that were detected in the second study probably indicate behavioural differences between groups such as the level of physical activity.