Quantifying Effects of Pharmacological Blockers of Cardiac Autonomous Control Using Variability Parameters

Objective: The aim of this study was to identify the most sensitive heart rate and blood pressure variability (HRV and BPV) parameters from a given set of well-known methods for the quantification of cardiovascular autonomic function after several autonomic blockades.

Methods: Cardiovascular sympathetic and parasympathetic functions were studied in freely moving rats following peripheral muscarinic (methylatropine), β1-adrenergic (metoprolol), muscarinic + β1-adrenergic, α1-adrenergic (prazosin), and ganglionic (hexamethonium) blockades. Time domain, frequency domain and symbolic dynamics measures for each of HRV and BPV were classified through paired Wilcoxon test for all autonomic drugs separately. In order to select those variables that have a high relevance to, and stable influence on our target measurements (HRV, BPV) we used Fisher's Method to combine the p-value of multiple tests.

Results: This analysis led to the following best set of cardiovascular variability parameters: The mean normal beat-to-beat-interval/value (HRV/BPV: meanNN), the coefficient of variation (cvNN = standard deviation over meanNN) and the root mean square differences of successive (RMSSD) of the time domain analysis. In frequency domain analysis the very-low-frequency (VLF) component was selected. From symbolic dynamics Shannon entropy of the word distribution (FWSHANNON) as well as POLVAR3, the non-linear parameter to detect intermittently decreased variability, showed the best ability to discriminate between the different autonomic blockades.

Conclusion: Throughout a complex comparative analysis of HRV and BPV measures altered by a set of autonomic drugs, we identified the most sensitive set of informative cardiovascular variability indexes able to pick up the modifications imposed by the autonomic challenges. These indexes may help to increase our understanding of cardiovascular sympathetic and parasympathetic functions in translational studies of experimental diseases.

Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography

The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However, their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG, and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability (HRV) analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave).

Alpha-wave frequency characteristics in health and insomnia during sleep

Appearances of alpha waves in the sleep electrencephalogram indicate physiological, brief states of awakening that lie in between wakefulness and sleep. These microstates may also cause the loss in sleep quality experienced by individuals suffering from insomnia. To distinguish such pathological awakenings from physiological ones, differences in alpha-wave characteristics between transient awakening and wakefulness observed before the onset of sleep were studied. In polysomnographic datasets of sleep-healthy participants (n = 18) and patients with insomnia (n = 10), alpha waves were extracted from the relaxed, wake state before sleep onset, wake after sleep-onset periods and arousals of sleep. In these, alpha frequency and variability were determined as the median and standard deviation of inverse peak-to-peak intervals. Before sleep onset, patients with insomnia showed a decreased alpha variability compared with healthy participants (P < 0.05). After sleep onset, both groups showed patterns of decreased alpha frequency that was lower for wake after sleep-onset periods of shorter duration. For patients with insomnia, alpha variability increased for short wake after sleep-onset periods. Major differences between the two groups were encountered during arousal. In particular, the alpha frequency in patients with insomnia rebounded to wake levels, while the frequency in healthy participants remained at the reduced level of short wake after sleep-onset periods. Reductions in alpha frequency during wake after sleep-onset periods may be related to the microstate between sleep and wakefulness that was described for such brief awakenings. Reduced alpha variability before sleep may indicate a dysfunction of the alpha generation mechanism in insomnia. Alpha characteristics may also prove valuable in the study of other sleep and attention disorders.

Insomnia Characterization: From Hypnogram to Graph Spectral Theory

OBJECTIVE

To quantify and differentiate control and insomnia sleep onset patterns through biomedical signal processing of overnight polysomnograms.

METHODS

The approach consisted of three tandem modules: 1) biosignal processing module, which used state-space time-varying autoregressive moving average (TVARMA) processes with recursive particle filter, 2) hypnogram generation module that implemented a fuzzy inference system (FIS), and 3) insomnia characterization module that discriminated between control and subjects with insomnia using a logistic regression model trained with a set of similarity measures ( d1, d2 , d3, d4). The study employed sleep onset periods from 16 control and 16 subjects with insomnia.

RESULTS

State-spaced TVARMA processes with recursive particle filtering provided resilience to nonlinear, nonstationary, and non-Gaussian conditions of biosignals. FIS managed automated sleep scoring robust to intersubjects' and interraters' variability. The similarity distances quantified in a scalar measure the transitions amongst sleep onset stages, computed from expert and automated hypnograms. A statistical set of unpaired two-tailed t -tests suggested that distances d1 , d2, and d3 had larger statistical significance ( ) to characterize sleeping patterns. The logistic regression model classified control and subjects with insomnia with sensitivity 87 % , specificity 75 %, and accuracy 81 %.

CONCLUSION

Our approach can perform a supportive role in either biosignal processing, sleep staging, insomnia characterization, or all the previous, coping with time-consuming procedures and massive data volumes of standard protocols.

SIGNIFICANCE

The introduction of graph spectral theory and logistic regression for the diagnosis of insomnia represents a novel concept, attempting to describe complex sleep dynamics throughout transitions networks and scalar measures.

Synchronisation and coupling analysis: applied cardiovascular physics in sleep medicine

Sleep is a physiological process with an internal program of a number of well defined sleep stages and intermediate wakefulness periods. The sleep stages modulate the autonomous nervous system and thereby the sleep stages are accompanied by different regulation regimes for the cardiovascular and respiratory system. The differences in regulation can be distinguished by new techniques of cardiovascular physics. The number of patients suffering from sleep disorders increases unproportionally with the increase of the human population and aging, leading to very high expenses in the public health system. Therefore, the challenge of cardiovascular physics is to develop highly-sophisticated methods which are able to, on the one hand, supplement and replace expensive medical devices and, on the other hand, improve the medical diagnostics with decreasing the patient's risk. Methods of cardiovascular physics are used to analyze heart rate, blood pressure and respiration to detect changes of the autonomous nervous system in different diseases. Data driven modeling analysis, synchronization and coupling analysis and their applications to biosignals in healthy subjects and patients with different sleep disorders are presented. Newly derived methods of cardiovascular physics can help to find indicators for these health risks.

Different approaches of symbolic dynamics to quantify heart rate complexity

The analysis of symbolic dynamics applied to physiological time series is able to retrieve information about dynamical properties of the underlying system that cannot be gained with standard methods like e.g. spectral analysis. Different approaches for the transformation of the original time series to the symbolic time series have been proposed. Yet the differences between the approaches are unknown. In this study three different transformation methods are investigated: (1) symbolization according to the deviation from the average time series, (2) symbolization according to several equidistant levels between the minimum and maximum of the time series, (3) binary symbolization of the first derivative of the time series. Each method was applied to the cardiac interbeat interval series RR(i) and its difference ΔRR(I) of 17 healthy subjects obtained during head-up tilt testing. The symbolic dynamics of each method is analyzed by means of the occurrence of short sequences ('words') of length 3. The occurrence of words is grouped according to words without variations of the symbols (0V%), words with one variation (1V%), two like variations (2LV%) and two unlike variations (2UV%). Linear regression analysis showed that for method 1 0V%, 1V%, 2LV% and 2UV% changed with increasing tilt angle. For method 2 0V%, 2LV% and 2UV% changed with increasing tilt angle and method 3 showed changes for 0V% and 1V%. In conclusion, all methods are capable of reflecting changes of the cardiac autonomic nervous system during head-up tilt. All methods show that even the analysis of very short symbolic sequences is capable of tracking changes of the cardiac autonomic regulation during head-up tilt testing.

Quantifying the causal strength of multivariate cardiovascular couplings with momentary information transfer

This article studies a recently introduced information-theoretic approach to detect and quantify the causal couplings in a complex cardiovascular system. In the first step a causal algorithm detects the coupling delays and in the second step the causal strength of each coupling mechanism is quantified using the recently introduced momentary information transfer. As an example, the method is applied to time series of respiration, systolic and diastolic blood pressure, and heart rate of pregnant healthy women and women suffering from pre-eclampsia. A possible explanation for the influence of heart rate on systolic blood pressure is found and some differences between healthy women and patients are discussed.

Changes of sleep-stage transitions due to ageing and sleep disorder

Transition patterns between different sleep stages are analysed in terms of probability distributions of symbolic sequences for young and old subjects with and without sleep disorder. Changes of these patterns due to ageing are compared with variations of transition probabilities due to sleep disorder.

Heart rate detrended fluctuation indexes as estimate of obstructive sleep apnea severity

In the present study, we aimed at investigating a heart rate variability (HRV) biomarker that could be associated with the severity of the apnea-hypopnea index (AHI), which could be used for an early diagnosis of obstructive sleep apnea (OSA). This was a cross-sectional observational study on 47 patients (age 36 ± 9.2 standard deviation) diagnosed with mild (23.4%), moderate (34%), or severe (42.6%) OSA. HRV was studied by linear measures of fast Fourier transform, nonlinear Poincaré analysis, and detrended fluctuation analysis (DFA)—DFA α1 characterizes short-term fluctuations, DFA α2 characterizes long-term fluctuations. Associations between polysomnography indexes (AHI, arousal index [AI], and oxygen desaturation index [ODI]) and HRV indexes were studied. Patients with different grades of AHI had similar sympathovagal balance levels as indicated by the frequency-domain and Poincaré HRV indexes. The DFA α2 index was significantly positive correlated with AHI, AI, and ODI (Pearson r: 0.55, 0.59, and 0.59, respectively, with P < 0.0001). The ROC analysis revealed that DFA α2 index predicted moderate and severe OSA with a sensitivity/specificity/area under the curve of 0.86/0.64/0.8 (P = 0.005) and 0.6/0.89/0.76 (P = 0.003), respectively. Our data indicate that the DFA α2 index may be used as a reliable index for the detection of OSA severity.

Symbolic dynamics marker of heart rate variability combined with clinical variables enhance obstructive sleep apnea screening

Many sleep centres try to perform a reduced portable test in order to decrease the number of overnight polysomnographies that are expensive, time-consuming, and disturbing. With some limitations, heart rate variability (HRV) has been useful in this task. The aim of this investigation was to evaluate if inclusion of symbolic dynamics variables to a logistic regression model integrating clinical and physical variables, can improve the detection of subjects for further polysomnographies. To our knowledge, this is the first contribution that innovates in that strategy. A group of 133 patients has been referred to the sleep center for suspected sleep apnea. Clinical assessment of the patients consisted of a sleep related questionnaire and a physical examination. The clinical variables related to apnea and selected in the statistical model were age (p < 10(-3)), neck circumference (p < 10(-3)), score on a questionnaire scale intended to quantify daytime sleepiness (p < 10(-3)), and intensity of snoring (p < 10(-3)). The validation of this model demonstrated an increase in classification performance when a variable based on non-linear dynamics of HRV (p < 0.01) was used additionally to the other variables. For diagnostic rule based only on clinical and physical variables, the corresponding area under the receiver operating characteristic (ROC) curve was 0.907 (95% confidence interval (CI) = 0.848, 0.967), (sensitivity 87.10% and specificity 80%). For the model including the average of a symbolic dynamic variable, the area under the ROC curve was increased to 0.941 (95% = 0.897, 0.985), (sensitivity 88.71% and specificity 82.86%). In conclusion, symbolic dynamics, coupled with significant clinical and physical variables can help to prioritize polysomnographies in patients with a high probability of apnea. In addition, the processing of the HRV is a well established low cost and robust technique.

Dietary omega-3 fatty acids modulate the eicosanoid profile in man primarily via the CYP-epoxygenase pathway

Cytochrome P450 (CYP)-dependent metabolites of arachidonic acid (AA) contribute to the regulation of cardiovascular function. CYP enzymes also accept EPA and DHA to yield more potent vasodilatory and potentially anti-arrhythmic metabolites, suggesting that the endogenous CYP-eicosanoid profile can be favorably shifted by dietary omega-3 fatty acids. To test this hypothesis, 20 healthy volunteers were treated with an EPA/DHA supplement and analyzed for concomitant changes in the circulatory and urinary levels of AA-, EPA-, and DHA-derived metabolites produced by the cyclooxygenase-, lipoxygenase (LOX)-, and CYP-dependent pathways. Raising the Omega-3 Index from about four to eight primarily resulted in a large increase of EPA-derived CYP-dependent epoxy-metabolites followed by increases of EPA- and DHA-derived LOX-dependent monohydroxy-metabolites including the precursors of the resolvin E and D families; resolvins themselves were not detected. The metabolite/precursor fatty acid ratios indicated that CYP epoxygenases metabolized EPA with an 8.6-fold higher efficiency and DHA with a 2.2-fold higher efficiency than AA. Effects on leukotriene, prostaglandin E, prostacyclin, and thromboxane formation remained rather weak. We propose that CYP-dependent epoxy-metabolites of EPA and DHA may function as mediators of the vasodilatory and cardioprotective effects of omega-3 fatty acids and could serve as biomarkers in clinical studies investigating the cardiovascular effects of EPA/DHA supplementation.

Cardiovascular variability before and after delivery: recovery from arterial stiffness in women with preeclampsia 4 days post partum

OBJECTIVE

We studied the short-term response of autonomic control to delivery in normal pregnancies and pregnancies with preeclampsia (PE).

METHODS

Fourteen healthy pregnant women and 13 women with PE were monitored within four days before and four days after delivery and compared to values of 14 non-pregnant women as controls using high-resolution electrocardiogram and noninvasive continuous blood pressure monitoring.

RESULTS

In PE, blood pressure remained elevated four days postpartum, but markers for arterial stiffness normalized. In contrast, none of heart rate variability and baroreflex sensitivity parameters, altered due to either pregnancy or disease, were normalized 96 h after delivery.

CONCLUSION

Four days after delivery, the maternal cardiovascular system is still strongly affected by pregnancy independent of the health status.

Coupling analysis of transient cardiovascular dynamics

The analysis of effects from coupling in and between systems is important in data-driven investigations as practiced in many scientific fields. It allows deeper insights into the mechanisms of interaction emerging among individual smaller systems when forming complex systems as in the human circulatory system. For systems featuring various regimes, usually only the epochs before and after a transition between different regimes are analyzed, although relevant information might be hidden within these transitions. Transient behavior of cardiovascular variables may emerge, on the one hand, from the recovery of the system after a severe disturbance or, on the other hand, from adaptive behavior throughout changes of states. It contains important information about the processes involved and the relations between state variables such as heart rate, blood pressure, and respiration. Therefore, we apply an ensemble approach to extend the method of symbolic coupling traces to time-variant coupling analysis. These new ensemble symbolic coupling traces are capable of determining coupling direction, strength, and time offset τ from transient dynamics in multivariate cardiovascular data. We use this method to analyze data recorded during an orthostatic test to reveal a transient structure that cannot be detected by classic methods.

Estimating coupling directions in the cardiorespiratory system using recurrence properties

The asymmetry of coupling between complex systems can be studied by conditional probabilities of recurrence, which can be estimated by joint recurrence plots. This approach is applied for the first time on experimental data: time series of the human cardiorespiratory system in order to investigate the couplings between heart rate, mean arterial blood pressure and respiration. We find that the respiratory system couples towards the heart rate, and the heart rate towards the mean arterial blood pressure. However, our analysis could not detect a clear coupling direction between the mean arterial blood pressure and respiration.

Classification of cardiovascular time series based on different coupling structures using recurrence networks analysis

We analyse cardiovascular time series with the aim of performing early prediction of preeclampsia (PE), a pregnancy-specific disorder causing maternal and foetal morbidity and mortality. The analysis is made using a novel approach, namely the ε-recurrence networks applied to a phase space constructed by means of the time series of the variabilities of the heart rate and the blood pressure (systolic and diastolic). All the possible coupling structures among these variables are considered for the analysis. Network measures such as average path length, mean coreness, global clustering coefficient and scale-local transitivity dimension are computed and constitute the parameters for the subsequent quadratic discriminant analysis. This allows us to predict PE with a sensitivity of 91.7 per cent and a specificity of 68.1 per cent, thus validating the use of this method for classifying healthy and preeclamptic patients.

Assessment of feature selection and classification approaches to enhance information from overnight oximetry in the context of apnea diagnosis

This study is aimed at assessing the usefulness of different feature selection and classification methodologies in the context of sleep apnea hypopnea syndrome (SAHS) detection. Feature extraction, selection and classification stages were applied to analyze blood oxygen saturation (SaO2) recordings in order to simplify polysomnography (PSG), the gold standard diagnostic methodology for SAHS. Statistical, spectral and nonlinear measures were computed to compose the initial feature set. Principal component analysis (PCA), forward stepwise feature selection (FSFS) and genetic algorithms (GAs) were applied to select feature subsets. Fisher's linear discriminant (FLD), logistic regression (LR) and support vector machines (SVMs) were applied in the classification stage. Optimum classification algorithms from each combination of these feature selection and classification approaches were prospectively validated on datasets from two independent sleep units. FSFS + LR achieved the highest diagnostic performance using a small feature subset (4 features), reaching 83.2% accuracy in the validation set and 88.7% accuracy in the test set. Similarly, GAs + SVM also achieved high generalization capability using a small number of input features (7 features), with 84.2% accuracy on the validation set and 84.5% accuracy in the test set. Our results suggest that reduced subsets of complementary features (25% to 50% of total features) and classifiers with high generalization ability could provide high-performance screening tools in the context of SAHS.

Diminished heart beat non-stationarities in congestive heart failure

Studies on heart rate variability (HRV) have become popular and the possibility of diagnosis based on non-invasive techniques compels us to overcome the difficulties originated on the environmental changes that can affect the signal. We perform a non-parametric segmentation which consists of locating the points where the signal can be split into stationary segments. By finding stationary segments we are able to analyze the size of these segments and evaluate how the signal changes from one segment to another, looking at the statistical moments given in each patch, for example, mean and variance. We analyze HRV data for 15 patients with congestive heart failure (CHF; 11 males, 4 females, age 56±11 years), 18 elderly healthy subjects (EH; 11 males, 7 females, age 50±7 years), and 15 young healthy subjects (YH; 11 females, 4 males, age 31±6 years). Our results confirm higher variance for YH, and EH, while CHF displays diminished variance with p-values <0.01, when compared to the healthy groups, presenting higher HRV in healthy subjects. Moreover, it is possible to distinguish between YH and EH with p < 0.05 through the segmentation outcomes. We found high correlations between the results of segmentation and standard measures of HRV analysis and a connection to results of detrended fluctuation analysis (DFA). The segmentation applied to HRV studies detects aging and pathological conditions effects on the non-stationary behavior of the analyzed groups, promising to contribute in complexity analysis and providing risk stratification measures.