Complexity Variability Assessment of Nonlinear Time-Varying Cardiovascular Control

The application of complex systems theory to physiology and medicine has provided meaningful information about the nonlinear aspects underlying the dynamics of a wide range of biological processes and their disease-related aberrations. However, no studies have investigated whether meaningful information can be extracted by quantifying second-order moments of time-varying cardiovascular complexity. To this extent, we introduce a novel mathematical framework termed complexity variability, in which the variance of instantaneous Lyapunov spectra estimated over time serves as a reference quantifier. We apply the proposed methodology to four exemplary studies involving disorders which stem from cardiology, neurology and psychiatry: Congestive Heart Failure (CHF), Major Depression Disorder (MDD), Parkinson?s Disease (PD), and Post-Traumatic Stress Disorder (PTSD) patients with insomnia under a yoga training regime. We show that complexity assessments derived from simple time-averaging are not able to discern pathology-related changes in autonomic control, and we demonstrate that between-group differences in measures of complexity variability are consistent across pathologies. Pathological states such as CHF, MDD, and PD are associated with an increased complexity variability when compared to healthy controls, whereas wellbeing derived from yoga in PTSD is associated with lower time-variance of complexity

Entropy Analysis of Univariate Biomedical Signals:Review and Comparison of Methods

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Heart rate differences between symptomatic and asymptomatic Brugada syndrome patients at night

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Efficient transfer entropy analysis of non-stationary neural time series

Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience. Estimating transfer entropy from two processes requires the observation of multiple realizations of these processes to estimate associated probability density functions. To obtain these observations, available estimators assume stationarity of processes to allow pooling of observations over time. This assumption however, is a major obstacle to the application of these estimators in neuroscience as observed processes are often non-stationary. As a solution, Gomez-Herrero and colleagues theoretically showed that the stationarity assumption may be avoided by estimating transfer entropy from an ensemble of realizations. Such an ensemble is often readily available in neuroscience experiments in the form of experimental trials. Thus, in this work we combine the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series. We present an efficient implementation of the approach that deals with the increased computational demand of the ensemble method's practical application. In particular, we use a massively parallel implementation for a graphics processing unit to handle the computationally most heavy aspects of the ensemble method. We test the performance and robustness of our implementation on data from simulated stochastic processes and demonstrate the method's applicability to magnetoencephalographic data. While we mainly evaluate the proposed method for neuroscientific data, we expect it to be applicable in a variety of fields that are concerned with the analysis of information transfer in complex biological, social, and artificial systems.Comment: 27 pages, 7 figures, submitted to PLOS ON

Feasibility of Ultra-Short-Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and during Stress via Time-Domain and Entropy-Based Measures

Heart Rate Variability (HRV) and Blood Pressure Variability (BPV) are widely employed tools for characterizing the complex behavior of cardiovascular dynamics. Usually, HRV and BPV analyses are carried out through short-term (ST) measurements, which exploit ~five-minute-long recordings. Recent research efforts are focused on reducing the time series length, assessing whether and to what extent Ultra-Short-Term (UST) analysis is capable of extracting information about cardiovascular variability from very short recordings. In this work, we compare ST and UST measures computed on electrocardiographic R-R intervals and systolic arterial pressure time series obtained at rest and during both postural and mental stress. Standard time–domain indices are computed, together with entropy-based measures able to assess the regularity and complexity of cardiovascular dynamics, on time series lasting down to 60 samples, employing either a faster linear parametric estimator or a more reliable but time-consuming model-free method based on nearest neighbor estimates. Our results are evidence that shorter time series down to 120 samples still exhibit an acceptable agreement with the ST reference and can also be exploited to discriminate between stress and rest. Moreover, despite neglecting nonlinearities inherent to short-term cardiovascular dynamics, the faster linear estimator is still capable of detecting differences among the conditions, thus resulting in its suitability to be implemented on wearable devices

The fluctuation behavior of heart and respiratory system signals as a quantitative tool for studying long-term environmental exposures and chronic diseases

Background: Several studies over the last decades have suggested that a wide range of disease states, as well as the aging process itself, are marked by progressive impairment of the involved physiological processes to adapt, resulting in a loss of complexity in the dynamics of physiological functions. Therefore, measuring complexity from physiological system signals holds enormous promise for providing a new understanding of the mechanisms underlying physiological systems and how they change with diseases and aging. Furthermore, since physiological systems are continuously exposed to environmental factors, measuring how physiological complexity changes during exposure to environmental elements might also provide new insights into their effects. Indeed, this approach may be able to unveil subtle but important changes in the regulatory mechanisms of physiological systems not detectable by traditional analysis methods. Objectives: The overall objective of this PhD thesis was to quantify the complexity of the dynamics of heart and respiratory system signals, in order to investigate how this complexity changes with long-term environmental exposures and chronic diseases, using data from large epidemiological and clinical studies, in order to control for most potential confounders of the fluctuation behavior of systems signals (e.g., demographic, environmental, clinical, and lifestyle factors). We specifically aimed (1) at assessing the influence, first, of long-term smoking cessation, and second, of long-term exposure to traffic-related particulate matter of less than 10 micrometers in diameter (TPM10), on the regulation of the autonomic cardiovascular system and heart rate dynamics in an aging general population, using data from the SAPALDIA cohort study; (2) to assess whether the subgrouping of patients with recurrent obstructive airway diseases, including mild-to-moderate asthma, severe asthma, and COPD, according to their pattern of lung function fluctuation, allows for the identification of phenotypes with specific treatable traits, using data from the BIOAIR study. Methods: In the SAPALDIA cohort, a population-based Swiss cohort, 1608 participants ≥ 50 years of age underwent ambulatory 24-hr electrocardiogram monitoring and reported on lifestyle and medical history. In each participant, heart rate variability and heart rate dynamics were characterized by means of various quantitative analyses of the inter-beat interval time series generated from 24-hour electrocardiogram recordings. Each parameter obtained was then used as the outcome variable in multivariable linear regression models in order to evaluate the association with (1) smoking status and time elapsed since smoking cessation; (2) long-term exposure to TPM10. The models were adjusted for known confounding factors. In the BIOAIR study, we conducted a time series clustering analysis based on the fluctuation of twice-daily FEV1 measurements recorded over a one year period in a mixed group of 134 adults with mild-to-moderate asthma, severe asthma, or COPD from the longitudinal Pan-European BIOAIR study. Results: In the SAPALDIA cohort, our findings indicate that smoking triggers adverse changes in the regulation of the cardiovascular system, even at low levels of exposure since current light smokers exhibited significant changes as compared to lifelong non-smokers. Moreover, there was evidence for a dose-response effect. Furthermore, full recovery was achieved in former smokers (i.e., normalization to the level of lifelong non-smokers). However, while light smokers fully recovered within the 15 first years of cessation, heavy former smokers might need up to 15-25 years to fully recover. Regarding long-term exposure to TPM10, we did not observe an overall association with heart rate variability/heart rate dynamics in the entire study population. However, significant changes in the heart rate dynamics were found in subjects without cardiovascular morbidity and significant changes, both in the heart rate dynamics and in the heart rate variability, were found in non-obese subjects without cardiovascular morbidity. Furthermore, subjects with homozygous GSTM1 gene deletion appeared to be more susceptible to the effects of TPM10. In the BIOAIR study, we identified five phenotypes, of those three distinct phenotypes of severe asthma, in which the progressive functional alteration of the lung corresponded to a gradually increasing clinical severity and translated into specific risks of exacerbation and treatment response features. Conclusions: This thesis hopes to demonstrate the importance of multidimensional approaches to gain understanding in the complex functioning of the human physiological system and of disease processes. Characterization of the complexity in the fluctuation behavior of system signals holds enormous promise for providing new understandings of the regulatory mechanisms of physiological systems and how they change with diseases. However, it is important to combine this kind of approach with classical epidemiological approaches in order to disentangle the various contributions of the intrinsic physiological dynamics, aging, diseases and comorbidities, lifestyle, and environment. In the SAPALDIA cohort study, we were able to disentangle the influence of specific environmental exposures, such as particulate matter air pollution and smoking exposure, on the heart rate variability and heart rate dynamics, and thus to unveil long-term alterations in former heavy smokers, as well as adverse effects of low level, but long-term, exposure to TPM10 in healthy subjects and in subjects with homozygous GSTM1 gene deletion. In the BIOAIR study, we provide evidence that airway dynamics contain substantial information, which enables the identification of clinically meaningful phenotypes, in which the functional alteration of the lung translates into specific treatable traits

Measuring the Rate of Information Exchange in Point-Process Data With Application to Cardiovascular Variability

The amount of information exchanged per unit of time between two dynamic processes is an important concept for the analysis of complex systems. Theoretical formulations and data-efficient estimators have been recently introduced for this quantity, known as the mutual information rate (MIR), allowing its continuous-time computation for event-based data sets measured as realizations of coupled point processes. This work presents the implementation of MIR for point process applications in Network Physiology and cardiovascular variability, which typically feature short and noisy experimental time series. We assess the bias of MIR estimated for uncoupled point processes in the frame of surrogate data, and we compensate it by introducing a corrected MIR (cMIR) measure designed to return zero values when the two processes do not exchange information. The method is first tested extensively in synthetic point processes including a physiologically-based model of the heartbeat dynamics and the blood pressure propagation times, where we show the ability of cMIR to compensate the negative bias of MIR and return statistically significant values even for weakly coupled processes. The method is then assessed in real point-process data measured from healthy subjects during different physiological conditions, showing that cMIR between heartbeat and pressure propagation times increases significantly during postural stress, though not during mental stress. These results document that cMIR reflects physiological mechanisms of cardiovascular variability related to the joint neural autonomic modulation of heart rate and arterial compliance

Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Coronary Artery Disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the Heart Rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD