Can HRV Predict Prolonged Hospitalization and Favorable or Unfavorable Short-Term Outcome in Patients with Acute Ischemic Stroke?

The aim of this study was to assess whether the heart rate variability (HRV) could predict a favorable or unfavorable stroke outcome. The endpoint was based on the National Institutes of Health Stroke Scale (NIHSS). The patient's health condition was assessed upon discharge from the hospital. An unfavorable stroke outcome was defined as death or NIHSS ≥ 9, while NIHSS < 9 meant a favorable stroke outcome. The studied group consisted of 59 patients with acute ischemic stroke AIS (mean age of 65.6 ± 13.2; 58% were females). An original and innovative non-linear measure was used to analyze HRV. It was based on symbolic dynamics consisting of comparing the "length of the longest words" in the night recording of HRV. "The length of the longest word" meant the longest sequence of identical adjacent symbols possible for a patient. An unfavorable stroke outcome occurred in 22 patients, whereas the majority of patients (37) had a favorable stroke outcome. The average hospitalization time of patients with clinical progression was 29 ± 14 days, and with favorable outcomes was 10 ± 3 days. Patients with long words (more than 150 adjacent RR intervals having the same symbol) were hospitalized no longer than 14 days and they had no clinical progression. The patients with a favorable stroke outcome were characterized by longer words. Our pilot study may be the beginning of work on the development of a non-linear, symbolic method as a predictor of prolonged hospitalization and increased risk of clinical progression in patients with AIS.

Heart rate variability as a predictor of stroke course, functional outcome, and medical complications: A systematic review.

Background: Heart rate variability (HRV) is a non-invasive marker of autonomic nervous system function that is based on the analysis of length differences between subsequent RR intervals of the electrocardiogram. The aim of this systematic review was to assess the current knowledge gap in the utility of HRV parameters and their value as predictors of the acute stroke course. Methods: A systematic review was performed in accordance with the PRISMA guidelines. Relevant articles published between 1 January 2016 and 1 November 2022 available in the PubMed, Web of Science, Scopus, and Cochrane Library databases were obtained using a systematic search strategy. The following keywords were used to screen the publications: "heart rate variability" AND/OR "HRV" AND "stroke." The eligibility criteria that clearly identified and described outcomes and outlined restrictions on HRV measurement were pre-established by the authors. Articles assessing the relationship between HRV measured in the acute phase of stroke and at least one stroke outcome were considered. The observation period did not exceed 12 months. Studies that included patients with medical conditions influencing HRV with no established stroke etiology and non-human subjects were excluded from the analysis. To minimize the risk of bias, disagreements throughout the search and analysis were resolved by two independent supervisors. Results: Of the 1,305 records obtained from the systematic search based on keywords, 36 were included in the final review. These publications provided insight into the usability of linear and non-linear HRV analysis in predicting the course, complications, and mortality of stroke. Furthermore, some modern techniques, such as HRV biofeedback, for the improvement of cognition performance after a stroke are discussed. Discussion: The present study showed that HRV could be considered a promising biomarker of a stroke outcome and its complications. However, further research is needed to establish a methodology for appropriate quantification and interpretation of HRV-derived parameters.

Simultaneous Entrainment Response Assessment at Multiple Sites.

BACKGROUND: The entrainment response, defined as the difference between the postpacing interval and the tachycardia cycle length (TCL) recorded from a mapping catheter, allows to track down the components of the tachycardia loop. OBJECTIVES: The aim of this study was to evaluate if the postpacing interval measured simultaneously from multiple sites that are remote from the pacing site (PPIR) could be clinically useful in mapping re-entrant circuits. METHODS: Ninety-two episodes of entrainment response in 29 patients with different macro-re-entrant tachycardias were evaluated using a standardized entrainment protocol. The spatial distribution of different values of PPIR-TCL in a simulation and a computational model of an entrained re-entrant tachycardia was also analyzed. RESULTS: The PPIR exceeded TCL by more than 20 milliseconds only if both pacing and recording sites were outside the tachycardia circuit. The PPIR-TCL at in-circuit sites was always ≤20 milliseconds. Sites with negative PPIR-TCL values were found either outside or inside the tachycardia circuit. CONCLUSIONS: Assessment of entrainment response from catheters remote from the pacing site may enhance spatial mapping of the tachycardia circuit. The PPIR-TCL above 20 milliseconds has an excellent positive predictive value in identifying sites outside the tachycardia circuit.

The Hemisphere of the Brain in Which a Stroke Has Occurred Visible in the Heart Rate Variability.

The aim of this study was to assess whether heart rate variability (HRV) could predict which hemisphere of the brain was affected during an acute ischemic stroke (AIS). To achieve this goal, we compared HRV between patients with a right (RH) and left hemispheric (LH) stroke. The studied group consisted of 64 patients with AIS (25 with RH and 39 with LH stroke, with a mean age of 64 ± 12 and 66 ± 13, p = 0.3, respectively) using 24 h Holter ECG records at NN intervals performed at a mean of 4.3 ± 2 days following their AIS. Standard linear methods were used to analyze HRV in the time and frequency domains, as well as nonlinear methods, including sample entropy, detrended fluctuation analysis, and asymmetry measures. Patients with an LH stroke had significantly greater values for sample entropy compared to subjects with an RH stroke (1.31 ± 0.53 vs. 0.92 ± 0.46, p = 0.003, Bonferroni-corrected p = 0.033, effect size = 0.8). The LH stroke group also had higher RMSSD (113 ± 81 vs. 76 ± 61, p = 0.06), pNN50 (33.35 ± 28.54 vs. 18.52 ± 23.75, p = 0.02), and HFnu (48.42 ± 16.41 vs. 42.66 ± 17.88, p = 0.11) values, when compared to the RH group, which was possibly related to higher activity in the parasympathetic system in the LH group. Conversely, subjects with RH stroke had higher LFnu (57.34 ± 17.88 vs. 51.58 ± 16.41, p-value = 0.11) and LF/HF ratios (2.24 ± 2.87 vs. 1.68 ± 2.50, p-value = 0.11), which were likely related to higher activity in the sympathetic nervous system, when compared to the LH stroke group. Our pilot study demonstrated that patients with RH stroke had lower HRV complexity than those with LH stroke, indicating that HRV could be useful in the discrimination of hemispheric involvement in AIS.

Heart rate variability comparison between young males after 4-6 weeks from the end of SARS-CoV-2 infection and controls.

Due to the prolonged inflammatory process induced by infection of the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), indices of autonomic nervous system dysfunction may persist long after viral shedding. Previous studies showed significant changes in HRV parameters in severe (including fatal) infection of SARS-CoV-2. However, few studies have comprehensively examined HRV in individuals who previously presented as asymptomatic or mildly symptomatic cases of COVID-19. In this study, we examined HRV in asymptomatic or mildly symptomatic individuals 5-7 weeks following positive confirmation of SARS-CoV-2 infection. Sixty-five ECG Holter recordings from young (mean age 22.6 ± 3.4 years), physically fit male subjects 4-6 weeks after the second negative test (considered to be the start of recovery) and twenty-six control male subjects (mean age 23.2 ± 2.9 years) were considered in the study. Night-time RR time series were extracted from ECG signals. Selected linear as well as nonlinear HRV parameters were calculated. We found significant differences in Porta's symbolic analysis parameters V0 and V2 (p < 0.001), α2 (p < 0.001), very low-frequency component (VLF; p = 0.022) and respiratory peak (from the PRSA method; p = 0.012). These differences may be caused by the changes of activity of the parasympathetic autonomic nervous system as well as by the coupling of respiratory rhythm with heart rate due to an increase in pulmonary arterial vascular resistance. The results suggest that the differences with the control group in the HRV parameters, that reflect the functional state of the autonomic nervous system, are measurable after a few weeks from the beginning of the recovery even in the post-COVID group-a young and physically active population. We indicate HRV sensitive markers which may be used in long-term monitoring of patients after recovery.

Assessment of time irreversibility in a time series using visibility graphs.

In this paper, we studied the time-domain irreversibility of time series, which is a fundamental property of systems in a nonequilibrium state. We analyzed a subgroup of the databases provided by University of Rochester, namely from the THEW Project. Our data consists of LQTS (Long QT Syndrome) patients and healthy persons. LQTS may be associated with an increased risk of sudden cardiac death (SCD), which is still a big clinical problem. ECG-based artificial intelligence methods can identify sudden cardiac death with a high accuracy. It follows that heart rate variability contains information about the possibility of SCD, which may be extracted, provided that appropriate methods are developed for this purpose. Our aim was to assess the complexity of both groups using visibility graph (VG) methods. Multivariate analysis of connection patterns of graphs built from time series was performed using multiplex visibility graph methods. For univariate time series, time irreversibility of the ECG interval QT of patients with LQTS was lower than for the healthy. However, we did not observe statistically significant difference in the comparison of RR intervals time series of the two groups studied. The connection patterns retrieved from multiplex VGs have more similarity with each other in the case of LQTS patients. This observation may be used to develop better methods for SCD risk stratification.

Asymmetric multiscale multifractal analysis (AMMA) of heart rate variability.

Objective.The physiological activity of the heart is controlled and modulated mostly by the parasympathetic and sympathetic nervous systems. Heart rate variability (HRV) analysis is therefore used to observe fluctuations that reflect changes in the activity in these two branches. Knowing that acceleration and deceleration patterns in heart rate fluctuations are asymmetrically distributed, the ability to analyze HRV asymmetry was introduced into MMA.Approach. The new method is called asymmetric multiscale multifractal analysis (AMMA) and the analysis involved six groups: 36 healthy persons, 103 cases with aortic valve stenosis, 36 with hypertrophic cardiomyopathy, 32 with atrial fibrillation, 59 patients with coronary artery disease (CAD) and 13 with congestive heart failure.Main results. Analyzing the results obtained for the 6 groups of patients based on the AMMA method, i.e. comparing the Hurst surfaces for heart rate decelerations and accelerations, it was noticed that these surfaces differ significantly. And the differences occur in most groups for large fluctuations (multifractal parameterq > 0). In addition, a similarity was found for all groups for the AMMA Hurst surface for decelerations to the MMA Hurst surface-heart rate decelerations (lengthening of the RR intervals) appears to be the main factor determining the shape of the complete Hurst surface and so the multifractal properties of HRV. The differences between the groups, especially for CAD, hypertrophic cardiomyopathy and aortic valve stenosis, are more visible if the Hurst surfaces are analyzed separately for accelerations and decelerations.Significance. The AMMA results presented here may provide additional input for HRV analysis and create a new paradigm for future medical screening. Note that the HRV analysis using MMA (without distinguishing accelerations from decelerations) gave satisfactory screening statistics in our previous studies.

Information Flow Between Heart Rhythm, Repolarization, and the Diastolic Interval Series for Healthy Individuals and LQTS1 Patients.

Using information theoretic measures, relations between heart rhythm, repolarization in the tissue of the heart, and the diastolic interval time series are analyzed. These processes are a fragment of the cardiovascular physiological network. A comparison is made between the results for 84 (42 women) healthy individuals and 65 (45 women) long QT syndrome type 1 (LQTS1) patients. Self-entropy, transfer entropy, and joint transfer entropy are calculated for the three time series and their combinations. The results for self-entropy indicate the well-known result that regularity of heart rhythm for healthy individuals is larger than that of QT interval series. The flow of information depends on the direction with the flow from the heart rhythm to QT dominating. In LQTS1 patients, however, our results indicate that information flow in the opposite direction may occur-a new result. The information flow from the heart rhythm to QT dominates, which verifies the asymmetry seen by Porta et al. in the variable tilt angle experiment. The amount of new information and self-entropy for LQTS1 patients is smaller than that for healthy individuals. However, information transfers from RR to QT and from DI to QT are larger in the case of LQTS1 patients.

The effect of persistent U-shaped patterns in RR night-time series on the heart rate variability complexity in healthy humans.

OBJECTIVE: U-shaped patterns, characteristic periods of time observed in tachograms, are a specific subgroup among very low frequency components characterized by relatively short periods of smooth accelerations followed by decelerations of heart rhythm. In this study, we characterize this phenomenon and its effect on heart rate variability (HRV) parameters. APPROACH: We calculated linear (the mean and standard deviation of RR intervals, RMSSD, pNN50 and the power of the frequency components) and nonlinear (V0, V1 and V2 Porta's symbolic analysis, Shannon and Sample entropy, Guzik's and Porta's asymmetry indexes, the exponents α1 and α2 of detrended fluctuation analysis and the Hurst surface h(q,s) of multiscale multifractal analysis (MMA)) HRV parameters for 65 RR interval night-time series (39 females, 37.5(11.3) years old and 26 males, 41.7(16.5) years old; all without organic heart diseases). All parameters were calculated for original data and for the three kinds of test data in which the following parts of the time series were replaced by 1/f noise: (A) the U-shape patterns annotated in a given data set, (B) randomly chosen windows of similar size as the U-shaped patterns, (C) acceleration-deceleration events shorter than U-shaped patterns. MAIN RESULTS: We found that the U-shaped patterns, as the most persistent structures in RR night-time intervals series, affect the long-range correlation properties (measured by α2). We also found that the U-shaped patterns importantly strictly affect the shape of h(q,s) surface at different scales s. Removing the U-shaped patterns results in the shape of the h(q,s) surface losing the properties characteristic for healthy heart rhythm. The largest quantitative effect of U-shaped patterns was obtained for the power of the VLF component. The mean percentage difference of the VLF component between the original data and the A to C type test data were 19.4%, -4.3% and 5.3%, respectively. SIGNIFICANCE: Although percentage contribution of U-shaped patterns is small compared to the whole night-time series (on the average 3.1%(1.7%) with a standard deviation of 1.7%), these patterns have a considerable impact on the HRV parameters describing the VLF, persistency, nonlinear correlations and multifractal properties.

Heart rate variability, multifractal multiscale patterns and their assessment criteria.

OBJECTIVE: Both the central nervous system and the autonomic nervous system are complex physiological networks which modulate the heart rate. They are spatially extended, have built-in delays and work on many time scales simultaneously-nonhomogeneous networks with multifractal dynamics. The object of our research was the analysis of human heart rate variability (HRV) using the nonlinear multiscale multifractal analysis (MMA) method for several cardiovascular diseases. The analysis of HRV (night-time recordings) involved six groups of patients: 61 healthy persons, 104 cases with aortic valve stenosis, 42 with hypertrophic cardiomyopathy, 36 with atrial fibrillation, 70 patients with coronary artery disease and 19 with congestive heart failure. 85% of patients formed a training data set (282 subjects) and 15% formed a test data set (50 subjects). APPROACH: Multiscale multifractal analysis allows one to analyze the complexity of HRV and find the scaling properties of its fluctuations. The main result of MMA is the Hurst surface, the shape of which changes depending on the medical case analyzed. We prepared six criteria to distinguish a multifractal pattern for healthy subjects. We also prepared additional criteria, enabling one to recognize atrial fibrillation. MAIN RESULTS: For the training data set, we obtained the following accuracy statistics in distinguishing the patients from the healthy: 68% for coronary artery disease, 67% for hypertrophic cardiomyopathy, 88% for atrial fibrillation, 74% for aortic valve stenosis and 83% for congestive heart failure. For the complete training data set we obtained an accuracy of 73%, and 80% for the test data set (mean for ten random selections of the test data set). SIGNIFICANCE: The results of MMA presented here provide an additional input into the diagnostic process and may help to create a paradigm for future studies on medical screening methods, especially in that MMA focuses on very low frequencies of HRV not easily accessible by standard medical techniques. Satisfactory statistics for screening using both MMA and the unfiltered version of LF/HF indicate that the nature of the complete network moderating heart rhythm needs to be studied and that sinus rhythm in clinical patients may not always be separated from arrhythmia when its incidence is large.

Monitoring fetal maturation-objectives, techniques and indices of autonomic function.

Monitoring the fetal behavior does not only have implications for acute care but also for identifying developmental disturbances that burden the entire later life. The concept, of 'fetal programming', also known as 'developmental origins of adult disease hypothesis', e.g. applies for cardiovascular, metabolic, hyperkinetic, cognitive disorders. Since the autonomic nervous system is involved in all of those systems, cardiac autonomic control may provide relevant functional diagnostic and prognostic information. The fetal heart rate patterns (HRP) are one of the few functional signals in the prenatal period that relate to autonomic control and, therefore, is predestinated for its evaluation. The development of sensitive markers of fetal maturation and its disturbances requires the consideration of physiological fundamentals, recording technology and HRP parameters of autonomic control. Based on the ESGCO2016 special session on monitoring the fetal maturation we herein report the most recent results on: (i) functional fetal autonomic brain age score (fABAS), Recurrence Quantitative Analysis and Binary Symbolic Dynamics of complex HRP resolve specific maturation periods, (ii) magnetocardiography (MCG) based fABAS was validated for cardiotocography (CTG), (iii) 30 min recordings are sufficient for obtaining episodes of high variability, important for intrauterine growth restriction (IUGR) detection in handheld Doppler, (iv) novel parameters from PRSA to identify Intra IUGR fetuses, (v) evaluation of fetal electrocardiographic (ECG) recordings, (vi) correlation between maternal and fetal HRV is disturbed in pre-eclampsia. The reported novel developments significantly extend the possibilities for the established CTG methodology. Novel HRP indices improve the accuracy of assessment due to their more appropriate consideration of complex autonomic processes across the recording technologies (CTG, handheld Doppler, MCG, ECG). The ultimate objective is their dissemination into routine practice and studies of fetal developmental disturbances with implications for programming of adult diseases.

Modeling heart rate variability including the effect of sleep stages.

We propose a model for heart rate variability (HRV) of a healthy individual during sleep with the assumption that the heart rate variability is predominantly a random process. Autonomic nervous system activity has different properties during different sleep stages, and this affects many physiological systems including the cardiovascular system. Different properties of HRV can be observed during each particular sleep stage. We believe that taking into account the sleep architecture is crucial for modeling the human nighttime HRV. The stochastic model of HRV introduced by Kantelhardt et al. was used as the initial starting point. We studied the statistical properties of sleep in healthy adults, analyzing 30 polysomnographic recordings, which provided realistic information about sleep architecture. Next, we generated synthetic hypnograms and included them in the modeling of nighttime RR interval series. The results of standard HRV linear analysis and of nonlinear analysis (Shannon entropy, Poincaré plots, and multiscale multifractal analysis) show that-in comparison with real data-the HRV signals obtained from our model have very similar properties, in particular including the multifractal characteristics at different time scales. The model described in this paper is discussed in the context of normal sleep. However, its construction is such that it should allow to model heart rate variability in sleep disorders. This possibility is briefly discussed.

Effect of the restitution properties of cardiac tissue on the repeatability of entrainment mapping response.

BACKGROUND: The difference between the postpacing interval (PPI) and the tachycardia cycle length (TCL; PPI-TCL) is a useful tool in mapping macro-reentrant tachycardias. However, entrainment pacing causes some perturbation of the conduction velocity within the tachycardia circuit, which may affect the repeatability and consequently the accuracy of the measurement of PPI-TCL. The aim of this study was to assess PPI-TCL repeatability both in vivo and in silico. METHODS AND RESULTS: In the experimental part, entrainment pacing was performed twice at each of the 124 tested sites for 30 patients undergoing radiofrequency ablation of atrial and ventricular re-entrant arrhythmias. A similar protocol was used in a simplified computer model of the cardiac tachycardia circuit in a 2-dimensional tissue strip using a Fenton-Karma model of cardiac tissue. In vivo, in the case of fast tachycardias (<350 ms), PPI-TCL variability observed was doubled compared with slow tachycardias (>350 ms; 95% Limits of Agreement ranged from -21.4 to 21.6 ms for TCL<350 ms and from -10.8 to 11.5 ms for TCL>350 ms). Simulations show that this increase of variability may be because of the oscillations of the conduction velocity inside the tachycardia circuits. The effect of the restitution properties of cardiac tissue on the outcome of entrainment pacing is discussed. CONCLUSIONS: PPI-TCL is characterized by a high repeatability with the differences between the results for individual stimulations of ≤20 ms. The variability of this parameter is significantly lower in the case of slow tachycardias.

Attraction and repulsion of spiral waves by inhomogeneity of conduction anisotropy--a model of spiral wave interaction with electrical remodeling of heart tissue.

Various forms of heart disease are associated with remodeling of the heart muscle, which results in a perturbation of cell-to-cell electrical coupling. These perturbations may alter the trajectory of spiral wave drift in the heart muscle. We investigate the effect of spatially extended inhomogeneity of transverse cell coupling on the spiral wave trajectory using a simple active media model. The spiral wave was either attracted or repelled from the center of inhomogeneity as a function of cell excitability and gradient of the cell coupling. High levels of excitability resulted in an attraction of the wave to the center of inhomogeneity, whereas low levels resulted in an escape and termination of the spiral wave. The spiral wave drift velocity was related to the gradient of the coupling and the initial position of the wave. In a diseased heart, a region of altered transverse coupling corresponds with local gap junction remodeling that may be responsible for stabilization-destabilization of spiral waves and hence reflect potentially important targets in the treatment of heart arrhythmias.

Development of multiscale complexity and multifractality of fetal heart rate variability.

During fetal development a complex system grows and coordination over multiple time scales is formed towards an integrated behavior of the organism. Since essential cardiovascular and associated coordination is mediated by the autonomic nervous system (ANS) and the ANS activity is reflected in recordable heart rate patterns, multiscale heart rate analysis is a tool predestined for the diagnosis of prenatal maturation. The analyses over multiple time scales requires sufficiently long data sets while the recordings of fetal heart rate as well as the behavioral states studied are themselves short. Care must be taken that the analysis methods used are appropriate for short data lengths. We investigated multiscale entropy and multifractal scaling exponents from 30 minute recordings of 27 normal fetuses, aged between 23 and 38 weeks of gestational age (WGA) during the quiet state. In multiscale entropy, we found complexity lower than that of non-correlated white noise over all 20 coarse graining time scales investigated. Significant maturation age related complexity increase was strongest expressed at scale 2, both using sample entropy and generalized mutual information as complexity estimates. Multiscale multifractal analysis (MMA) in which the Hurst surface h(q,s) is calculated, where q is the multifractal parameter and s is the scale, was applied to the fetal heart rate data. MMA is a method derived from detrended fluctuation analysis (DFA). We modified the base algorithm of MMA to be applicable for short time series analysis using overlapping data windows and a reduction of the scale range. We looked for such q and s for which the Hurst exponent h(q,s) is most correlated with gestational age. We used this value of the Hurst exponent to predict the gestational age based only on fetal heart rate variability properties. Comparison with the true age of the fetus gave satisfying results (error 2.17±3.29 weeks; p<0.001; R(2)=0.52). In addition, we found that the normally used DFA scale range is non-optimal for fetal age evaluation. We conclude that 30 min recordings are appropriate and sufficient for assessing fetal age by multiscale entropy and multiscale multifractal analysis. The predominant prognostic role of scale 2 heart beats for MSE and scale 39 heart beats (at q=-0.7) for MMA cannot be explored neither by single scale complexity measures nor by standard detrended fluctuation analysis.

A simple model of the right atrium of the human heart with the sinoatrial and atrioventricular nodes included.

Existing atrial models with detailed anatomical structure and multi-variable cardiac transmembrane current models are too complex to allow to combine an investigation of long time dycal properties of the heart rhythm with the ability to effectively simulate cardiac electrical activity during arrhythmia. Other ways of modeling need to be investigated. Moreover, many state-of-the-art models of the right atrium do not include an atrioventricular node (AVN) and only rarely--the sinoatrial node (SAN). A model of the heart tissue within the right atrium including the SAN and AVN nodes was developed. Looking for a minimal model, currently we are testing our approach on chosen well-known arrhythmias, which were until now obtained only using much more complicated models, or were only observed in a clinical setting. Ultimately, the goal is to obtain a model able to generate sequences of RR intervals specific for the arrhythmias involving the AV junction as well as for other phenomena occurring within the atrium. The model should be fast enough to allow the study of heart rate variability and arrhythmias at a time scale of thousands of heart beats in real-time. In the model of the right atrium proposed here, different kinds of cardiac tissues are described by sets of different equations, with most of them belonging to the class of Liénard nonlinear dynamical systems. We have developed a series of models of the right atrium with differing anatomical simplifications, in the form of a 2D mapping of the atrium or of an idealized cylindrical geometry, including only those anatomical details required to reproduce a given physiological phenomenon. The simulations allowed to reconstruct the phase relations between the sinus rhythm and the location and properties of a parasystolic source together with the effect of this source on the resultant heart rhythm. We model the action potential conduction time alternans through the atrioventricular AVN junction observed in cardiac tissue in electrophysiological studies during the ventricular-triggered atrial tachycardia. A simulation of the atrio-ventricular nodal reentry tachycardia was performed together with an entrainment procedure in which the arrhythmia circuit was located by measuring the post-pacing interval (PPI) at simulated mapping catheters. The generation and interpretation of RR times series is the ultimate goal of our research. However, to reach that goal we need first to (1) somehow verify the validity of the model of the atrium with the nodes included and (2) include in the model the effect of the sympathetic and vagal ANS. The current paper serves as a partial solution of the 1). In particular we show, that measuring the PPI-TCL entrainment response in proximal (possibly-the slow-conducting pathway), the distal and at a mid-distance from CS could help in rapid distinction of AVNRT from other atrial tachycardias. Our simulations support the hypothesis that the alternans of the conduction time between the atria and the ventricles in the AV orthodromic reciprocating tachycardia can occur within a single pathway. In the atrial parasystole simulation, we found a mathematical condition which allows for a rough estimation of the location of the parasystolic source within the atrium, both for simplified (planar) and the cylindrical geometry of the atrium. The planar and the cylindrical geometry yielded practically the same results of simulations.

Role of spiral wave pinning in inhomogeneous active media in the termination of atrial fibrillation by electrical cardioversion.

Atrial fibrillation is the most common type of arrhythmia to affect humans. One of the treatment modalities for atrial fibrillation is an electrical cardioversion. Electrical cardioversion can result in one of three outcomes: an immediate termination of arrhythmic activity, a delayed termination or unsuccessful termination. The mechanism of delayed termination is unknown. Here we present a model of an atrial fibrillation as a coexistence of several spiral waves pinned to the inhomogeneities in active media. We show that in inhomogeneous system delayed termination can be explained as the unpinning of a spiral wave from inhomogeneities and its termination after collision with the edge of the system.

Effect of parametric dichotomous Markov noise on the properties of crises in dynamical systems.

Properties of dynamical systems with dichotomous Markov noise which exhibit crises are investigated. We find numerically the dependence of the mean residence time on the precrisis attractor on the transition rate (or transition probability in the discrete-time case) of dichotomous Markov noise. To explain this dependence, we construct a simple Markov chain model, which allows us to find the mean residence time for the given transition rate with a good accuracy. Next, we find the distribution of residence times for a system driven by dichotomous Markov noise and also build a simple model to explain its properties.