Complex patterns of abnormal heartbeats

Characterization of autonomic states by complex sympathetic and parasympathetic dynamics

Assessment of heartbeat dynamics provides a promising framework for non-invasive monitoring of cardiovascular and autonomic states. Nevertheless, the non-specificity of such measurements among clinical populations and healthy conditions associated with different autonomic states severely limits their applicability and exploitation in naturalistic conditions. This limitation arises especially when pathological or postural change-related sympathetic hyperactivity is compared to autonomic changes across age and experimental conditions. In this frame, we investigate the intrinsic irregularity and complexity of cardiac sympathetic and vagal activity series in different populations, which are associated with different cardiac autonomic dynamics. Sample entropy, fuzzy entropy, and distribution entropy are calculated on the recently proposed sympathetic and parasympathetic activity indices (SAI and PAI) series, which are derived from publicly available heartbeat series of congestive heart failure patients, elderly and young subjects watching a movie in the supine position, and healthy subjects undergoing slow postural changes. Results show statistically significant differences between pathological/old subjects and young subjects in the resting state and during slow tilt, with interesting trends in SAI- and PAI-related entropy values. Moreover, while CHF patients and healthy subjects in upright position show the higher cardiac sympathetic activity, elderly and young subjects in resting state showed higher vagal activity. We conclude that quantification of intrinsic cardiac complexity from sympathetic and vagal dynamics may provide new physiology insights and improve on the non-specificity of heartbeat-derived biomarkers.

Cardio-Hypothalamic-Pituitary Coupling during Rest and in Response to Exercise

The objective of this study was to examine cardio hypothalamic-pituitary coupling and to better understand how the temporal relations between these systems are altered during rest and exercise conditions. An intensive within subjects study design was used. Seven adult males completed two visits, each consisting of either a 24 h period of complete rest or a 24 h period containing a high-intensity exercise bout. An intravenous catheter was used to collect serum samples every 10 min throughout the 24 h period (i.e., 145 samples/person/condition) to assess growth hormone (GH) dynamics throughout the 24 h period. Cardiac dynamics were also collected throughout the 24 h period and epoched into 3 min windows every 10 min, providing serial short-time measurements of heart rate variability (HRV) concurrent to the GH sampling. The standard deviation of the normal RR interval (SDNN), the root mean square of successive differences (rMSSD), and sample entropy (SampEn) was calculated for each epoch and used to create new profiles. The dynamics of these profiles were individually quantified using SampEn and recurrence quantification analysis (RQA). To address our central question, the coupling between these profiles with GH was assessed using cross-SampEn and cross-RQA (cRQA). A comparison between the epoched HRV profiles indicated a main effect between profiles for sample entropy (p < 0.001) and several measures from RQA. An interaction between profile and condition was observed for cross-SampEn (p = 0.04) and several measures from cRQA. These findings highlight the potential application of epoched HRV to assess changes in cardiac dynamics, with specific applications to assessing cardio hypothalamic-pituitary coupling.

Cardiac Autonomic Dysfunction and Incidence of de novo Atrial Fibrillation: Heart Rate Variability vs. Heart Rate Complexity

BACKGROUND

The REACT DX registry evaluates standard therapies to episodes of long-lasting atrial tachyarrhythmias and assesses the quality of sensing and stability of the lead and the implantable cardioverter-defibrillator (ICD) (BIOTRONIK Lumax VR-T DX and successors) over at least a 1-year follow-up period.

OBJECTIVE

To study the association between the risk of de novo device-detected atrial fibrillation (AF), the autonomic perturbations before the onset of paroxysmal AF and a 7-days heart rate variability (7dHRV) 1 month after ICD implantation.

METHODS

The registry consists of 234 patients implanted with an ICD, including 10 with de novo long-lasting atrial tachyarrhythmias with no prior history of AF. The patients were matched via the propensity-score methodology as well as for properties directly influencing the ECGs recorded using GE CardioMem CM 3000. Heart rate variability (HRV) analysis was performed using standard parameters from time- and frequency-domains, and from non-linear dynamics.

RESULTS

No linear HRV was associated with an increased risk of AF (p = n.s.). The only significant approach was derived from symbolic dynamics with the parameter "forbidden words" which distinguished both groups on all 7 days of measurements (p < 0.05), thereby quantifying the heart rate complexity (HRC) as drastically lower in the de novo AF group.

CONCLUSION

Cardiac autonomic dysfunction denoted by low HRC may be associated with higher AF incidence. For patients with mild to moderate heart failure, standard HRV parameters are not appropriate to quantify cardiac autonomic perturbations before the onset of AF. Further studies are needed to determine the individual risk for AF that would enable interventions to restore autonomic balance in the general population.

Using mathematics to diagnose, cure, and predict cardiac arrhythmia

Mathematics can be used to analyze and model cardiac arrhythmia. I discuss three different problems. (1) Diagnosis of atrial fibrillation based on the time intervals between subsequent beats. The probability density histograms of the differences of the intervals between consecutive beats have characteristic shapes for atrial fibrillation. (2) Curing atrial fibrillation by ablation of the core of rotors. Recent clinical studies have proposed that ablating the core of rotors in atrial tissue can cure atrial fibrillation. However, the claims are controversial. One problem that arises relates to difficulties associated with developing algorithms to identify the core of rotors. In model tissue culture systems, heterogeneity in the structure makes it difficult to unambiguously locate the core of rotors. (3) Risk stratification for sudden cardiac death (SCD). Despite numerous clinical studies, there is still a need for improved criteria to assess the risk of SCD. I discuss the possibility of using the dynamics of premature ventricular complexes to help make predictions. The development of wearable devices to record and analyze cardiac rhythms offers new prospects for the diagnosis and treatment of cardiac arrhythmia.

Long ECGs reveal rich and robust dynamical regimes in patients with frequent ectopy

We have analyzed the electrocardiographic data collected during continuous 7-day ambulatory recordings in patients with frequent premature ventricular complexes (PVCs). We analyze the dependence of the frequency and patterns of PVCs on the heart rate and the time of the day. Patients display rhythms of a complex yet consistent structure. In a given patient, the pattern remains robust over different days and particular repetitive patterns appear at specific heart rates, suggesting the appearance of bifurcations in the dynamics. Over the course of 24 h, we find that in some patients, patterns appear to depend only on the heart rate, whereas in others, both the time of the day and the heart rate play a role in controlling the dynamics. Identifying parameter values at which bifurcations occur facilitates the development of dynamical models for arrhythmia. The use of powerful recording and analysis techniques will enable improved analysis of data and better understanding of mechanisms of arrhythmia in individual patients.

Intrinsic Complexity of Sympathetic and Parasympathetic Dynamics from HRV series: a Preliminary Study on Postural Changes

The analysis of complex heartbeat dynamics has been widely used to characterize heartbeat autonomic control in healthy and pathological conditions. However, underlying physiological correlates of complexity measurements from heart rate variability (HRV) series have not been identified yet. To this extent, we investigated intrinsic irregularity and complexity of cardiac sympathetic and vagal activity time series during postural changes. We exploited our recently proposed HRV-based, time-varying Sympathetic and Parasympathetic Activity Indices (SAI and PAI) and performed Sample Entropy, Fuzzy Entropy, and Distribution Entropy calculations on publicly-available heartbeat series gathered from 10 healthy subjects undergoing resting state and passive slow tilt sessions. Results show significantly higher entropy values during the upright position than resting state in both SAI and PAI series. We conclude that an increase in HRV complexity resulting from postural changes may derive from sympathetic and vagal activities with higher complex dynamics.

Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices

Most methods for sudden cardiac death (SCD) prediction require long-term (24 h) electrocardiogram recordings to measure heart rate variability (HRV) indices or premature ventricular complex indices (with the heartprint method). This work aimed to identify the best combinations of HRV and heartprint indices for predicting SCD based on short-term recordings (1000 heartbeats) through a support vector machine (SVM). Eleven HRV indices and five heartprint indices were measured in 135 pairs of recordings (one before an SCD episode and another without SCD as control). SVMs (defined with a radial basis function kernel with hyperparameter optimization) were trained with this dataset to identify the 13 best combinations of indices systematically. Through 10-fold cross-validation, the best area under the curve (AUC) value as a function of γ (gamma) and cost was identified. The predictive value of the identified combinations had AUCs between 0.80 and 0.86 and accuracies between 80 and 86%. Further SVM performance tests on a different dataset of 68 recordings (33 before SCD and 35 as control) showed AUC = 0.68 and accuracy = 67% for the best combination. The developed SVM may be useful for preventing imminent SCD through early warning based on electrocardiogram (ECG) or heart rate monitoring.

Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO

Classifying arrhythmias can be a tough task for a human being and automating this task is highly desirable. Nevertheless fully automatic arrhythmia classification through Electrocardiogram (ECG) signals is a challenging task when the inter-patient paradigm is considered. For the inter-patient paradigm, classifiers are evaluated on signals of unknown subjects, resembling the real world scenario. In this work, we explore a novel ECG representation based on vectorcardiogram (VCG), called temporal vectorcardiogram (TVCG), along with a complex network for feature extraction. We also fine-tune the SVM classifier and perform feature selection with a particle swarm optimization (PSO) algorithm. Results for the inter-patient paradigm show that the proposed method achieves the results comparable to state-of-the-art in MIT-BIH database (53% of Positive predictive (+P) for the Supraventricular ectopic beat (S) class and 87.3% of Sensitivity (Se) for the Ventricular ectopic beat (V) class) that TVCG is a richer representation of the heartbeat and that it could be useful for problems involving the cardiac signal and pattern recognition.

Sudden death risk stratification in non-ischemic dilated cardiomyopathy using old and new tools: a clinical challenge

INTRODUCTION

Risk stratification for sudden cardiac death in non-ischemic dilated cardiomyopathy (NIDCM) remains a clinical challenge. Areas covered: Currently, left ventricular ejection fraction (LVEF), severity of heart failure symptoms according to NYHA classification, and morphology and duration of the QRS complex guide device management in these patients with implantation of a cardioverter defibrillator (ICD) and/or cardiac resynchronization therapy (CRT) devices. Recently, the results of a randomized trial stirred some controversy regarding the utility of ICD in NIDCM patients, however, a subsequent meta-analysis confirmed prior findings of the survival-prolonging benefit of device therapy. Newer risk markers, like late gadolinium enhancement in cardiac magnetic resonance imaging (CMR) detecting myocardial fibrosis, are encouraging in improving risk stratification in these patients. Furthermore, resurgence of an old tool, the electrophysiology study (EPS), and technical advances in genetics in identifying high-risk familial NIDCM, appear promising in this direction. Expert commentary: Based on old and new tools, a more individualized approach may be applied in NIDCM patients, whereby CMR, EPS and genetics may provide further guidance, particularly in patients with LVEF>35%. These issues are herein reviewed and a practical algorithm is proposed for risk stratification and device implantation in NIDCM patients with LVEF below and above 35%.

Predicting the risk of sudden cardiac death

Sudden cardiac death (SCD) is the result of a change of cardiac activity from normal (typically sinus) rhythm to a rhythm that does not pump adequate blood to the brain. The most common rhythms leading to SCD are ventricular tachycardia (VT) or ventricular fibrillation (VF). These result from an accelerated ventricular pacemaker or ventricular reentrant waves. Despite significant efforts to develop accurate predictors for the risk of SCD, current methods for risk stratification still need to be improved. In this article we briefly review current approaches to risk stratification. Then we discuss the mathematical basis for dynamical transitions (called bifurcations) that may lead to VT and VF. One mechanism for transition to VT or VF involves a perturbation by a premature ventricular complex (PVC) during sinus rhythm. We describe the main mechanisms of PVCs (reentry, independent pacemakers and abnormal depolarizations). An emerging approach to risk stratification for SCD involves the development of individualized dynamical models of a patient based on measured anatomy and physiology. Careful analysis and modelling of dynamics of ventricular arrhythmia on an individual basis will be essential in order to improve risk stratification for SCD and to lay a foundation for personalized (precision) medicine in cardiology.

Outlier-resilient complexity analysis of heartbeat dynamics

Complexity in physiological outputs is believed to be a hallmark of healthy physiological control. How to accurately quantify the degree of complexity in physiological signals with outliers remains a major barrier for translating this novel concept of nonlinear dynamic theory to clinical practice. Here we propose a new approach to estimate the complexity in a signal by analyzing the irregularity of the sign time series of its coarse-grained time series at different time scales. Using surrogate data, we show that the method can reliably assess the complexity in noisy data while being highly resilient to outliers. We further apply this method to the analysis of human heartbeat recordings. Without removing any outliers due to ectopic beats, the method is able to detect a degradation of cardiac control in patients with congestive heart failure and a more degradation in critically ill patients whose life continuation relies on extracorporeal membrane oxygenator (ECMO). Moreover, the derived complexity measures can predict the mortality of ECMO patients. These results indicate that the proposed method may serve as a promising tool for monitoring cardiac function of patients in clinical settings.

Local dynamics of heart rate: detection and prognostic implications

The original observation that reduced heart rate variability (HRV) confers poor prognosis after myocardial infarction has been followed by many studies of heart rate dynamics. We tested the hypothesis that an entropy-based local dynamics measure gave prognostic information in ambulatory patients undergoing 24-h electrocardiography. In this context, entropy is the probability that short templates will find matches in the time series. We studied RR interval time series from 24-h Holter monitors of 1564 consecutive patients over age 39. We generated histograms of the count of templates as a function of the number of templates matches in short RR interval time series, and found characteristic appearance of histograms for atrial fibrillation, sinus rhythm with normal HRV, and sinus rhythm with reduced HRV and premature ventricular contractions (PVCs). We developed statistical models to detect the abnormal dynamic phenotype of reduced HRV with PVCs and fashioned a local dynamics score (LDs) that, after controlling for age, added more prognostic information than other standard risk factors and common HRV metrics, including, to our surprise, the PVC count and the HRV of normal-to-normal intervals. Addition of the LDs to a predictive model using standard risk factors significantly increased the ROC area and the net reclassification improvement was 27%. We conclude that abnormal local dynamics of heart rate confer adverse prognosis in patients undergoing 24-h ambulatory electrocardiography.

Evaluating predictions of critical oxygen desaturation events

This paper presents a new approach for evaluating predictions of oxygen saturation levels in blood ( SpO2). A performance metric based on a threshold is proposed to evaluate  SpO2 predictions based on whether or not they are able to capture critical desaturations in the  SpO2 time series of patients. We use linear auto-regressive models built using historical  SpO2 data to predict critical desaturation events with the proposed metric. In 20 s prediction intervals, 88%-94% of the critical events were captured with positive predictive values (PPVs) between 90% and 99%. Increasing the prediction horizon to 60 s, 46%-71% of the critical events were detected with PPVs between 81% and 97%. In both prediction horizons, more than 97% of the non-critical events were correctly classified. The overall classification capabilities for the developed predictive models were also investigated. The area under ROC curves for 60 s predictions from the developed models are between 0.86 and 0.98. Furthermore, we investigate the effect of including pulse rate (PR) dynamics in the models and predictions. We show no improvement in the percentage of the predicted critical desaturations if PR dynamics are incorporated into the  SpO2 predictive models (p-value = 0.814). We also show that including the PR dynamics does not improve the earliest time at which critical  SpO2 levels are predicted (p-value = 0.986). Our results indicate oxygen in blood is an effective input to the PR rather than vice versa. We demonstrate that the combination of predictive models with frequent pulse oximetry measurements can be used as a warning of critical oxygen desaturations that may have adverse effects on the health of patients.

New methods for the analysis of heartbeat behavior in risk stratification

Developing better methods for risk stratification for tachyarrhythmic sudden cardiac remains a major challenge for physicians and scientists. Since the transition from sinus rhythm to ventricular tachycardia/fibrillation happens by different mechanisms in different people, it is unrealistic to think that a single measure will be adequate to provide a good index for risk stratification. We analyze the dynamical properties of ventricular premature complexes over 24 h in an effort to understand the underlying mechanisms of ventricular arrhythmias and to better understand the arrhythmias that occur in individual patients. Two dimensional density plots, called heartprints, correlate characteristic features of the dynamics of premature ventricular complexes and the sinus rate. Heartprints show distinctive characteristics in individual patients. Based on a better understanding of the natures of transitions from sinus rhythm to sudden cardiac and the mechanisms of arrhythmia prior to cardiac arrest, it should be possible to develop better methods for risk stratification.

Integration of early physiological responses predicts later illness severity in preterm infants

Physiological data are routinely recorded in intensive care, but their use for rapid assessment of illness severity or long-term morbidity prediction has been limited. We developed a physiological assessment score for preterm newborns, akin to an electronic Apgar score, based on standard signals recorded noninvasively on admission to a neonatal intensive care unit. We were able to accurately and reliably estimate the probability of an individual preterm infant's risk of severe morbidity on the basis of noninvasive measurements. This prediction algorithm was developed with electronically captured physiological time series data from the first 3 hours of life in preterm infants (< or =34 weeks gestation, birth weight < or =2000 g). Extraction and integration of the data with state-of-the-art machine learning methods produced a probability score for illness severity, the PhysiScore. PhysiScore was validated on 138 infants with the leave-one-out method to prospectively identify infants at risk of short- and long-term morbidity. PhysiScore provided higher accuracy prediction of overall morbidity (86% sensitive at 96% specificity) than other neonatal scoring systems, including the standard Apgar score. PhysiScore was particularly accurate at identifying infants with high morbidity related to specific complications (infection: 90% at 100%; cardiopulmonary: 96% at 100%). Physiological parameters, particularly short-term variability in respiratory and heart rates, contributed more to morbidity prediction than invasive laboratory studies. Our flexible methodology of individual risk prediction based on automated, rapid, noninvasive measurements can be easily applied to a range of prediction tasks to improve patient care and resource allocation.

Traumatic brain injury and aging: is a combination of progesterone and vitamin D hormone a simple solution to a complex problem?

Although progress is being made in the development of new clinical treatments for traumatic brain injury (TBI), little is known about whether such treatments are effective in older patients, in whom frailty, prior medical conditions, altered metabolism, and changing sensitivity to medications all can affect outcomes following a brain injury. In this review we consider TBI to be a complex, highly variable, and systemic disorder that may require a new pharmacotherapeutic approach, one using combinations or cocktails of drugs to treat the many components of the injury cascade. We review some recent research on the role of vitamin D hormone and vitamin D deficiency in older subjects, and on the interactions of these factors with progesterone, the only treatment for TBI that has shown clinical effectiveness. Progesterone is now in phase III multicenter trial testing in the United States. We also discuss some of the potential mechanisms and pathways through which the combination of hormones may work, singly and in synergy, to enhance survival and recovery after TBI.

Rapidly detecting disorder in rhythmic biological signals: a spectral entropy measure to identify cardiac arrhythmias

We consider the use of a running measure of power spectrum disorder to distinguish between the normal sinus rhythm of the heart and two forms of cardiac arrhythmia: atrial fibrillation and atrial flutter. This spectral entropy measure is motivated by characteristic differences in the power spectra of beat timings during the three rhythms. We plot patient data derived from ten-beat windows on a "disorder map" and identify rhythm-defining ranges in the level and variance of spectral entropy values. Employing the spectral entropy within an automatic arrhythmia detection algorithm enables the classification of periods of atrial fibrillation from the time series of patients' beats. When the algorithm is set to identify abnormal rhythms within 6 s, it agrees with 85.7% of the annotations of professional rhythm assessors; for a response time of 30 s, this becomes 89.5%, and with 60 s, it is 90.3%. The algorithm provides a rapid way to detect atrial fibrillation, demonstrating usable response times as low as 6s. Measures of disorder in the frequency domain have practical significance in a range of biological signals: the techniques described in this paper have potential application for the rapid identification of disorder in other rhythmic signals.

Using the memories of multiscale machines to characterize complex systems

A scheme is presented to extract detailed dynamical signatures from successive measurements of complex systems. Relative entropy based time series tools are used to quantify the gain in predictive power of increasing past knowledge. By lossy compression, data is represented by increasingly coarsened symbolic strings. Each compression resolution is modeled by a machine: a finite memory transition matrix. Applying the relative entropy tools to each machine's memory exposes correlations within many time scales. Examples are given for cardiac arrhythmias and different heart conditions are distinguished.

The rule of bigeminy revisited: analysis in sudden cardiac death syndrome

BACKGROUND

The rule of bigeminy is commonly explained by a reentrant mechanism. We hypothesize that in patients with prolonged ventricular repolarization, the rule of bigeminy may be caused by premature ventricular complexes (PVCs) due to early afterdepolarizations. We evaluated these ventricular arrhythmias over extended periods in patients with sudden cardiac death syndrome.

METHODS

The electrocardiographic (ECG) characteristics of 15 recordings from the PhysioNet Sudden Cardiac Death Holter Database were analyzed for the persistence of bigeminy, interaction between the underlying cardiac rhythm and the coupling interval, and influence of a prolonged initiating RR cycle on the self-perpetuation of the arrhythmias.

RESULTS

Eight (53%) patients had classic torsade de pointes (TdP), 5 (33%) had other polymorphic ventricular tachycardia (VT), and 2 (13%) had monomorphic VT. Group A, which comprised 6 of the patients with TdP, had the following ECG tetrad: (1) frequent ventricular bigeminy (>5% of total ventricular arrhythmias), (2) long corrected QT interval (>0.5 second), (3) relatively fixed coupling interval, and (4) onset of bigeminy (n = 4) and TdP (n = 6) after a short-long RR sequence. Patients in group A had slower heart rates (mean RR = 1.12 +/- 0.26 vs 0.77 +/- 0.13 seconds, P < .01), longer QT intervals (corrected QT = 0.57 +/- 0.06 vs 0.45 +/- 0.06 second; P < .01) and more cases with prominent U waves (83% vs 33%, P < .05) than patients in group B (n = 9), composed of patients who had other types of VT, or TdP without frequent bigeminy.

CONCLUSIONS

We identified a set of ECG characteristics that supports the notion that premature ventricular complexes during self-perpetuating ventricular bigeminy ("rule of bigeminy") in long QT syndromes may be due to early afterdepolarizations.

Exported oscillator competition: a concept to analyze complex rhythms

We study the interaction between two independent nonlinear oscillators competing through a neutral excitable element. The first oscillator, completely deterministic, acts as a normal pacemaker sending pulses to the neutral element which fires when it is excited by these pulses. The second oscillator, endowed with some randomness, though unable to make the excitable element to beat, leads to the occasional suppression of its firing. The missing beats or errors are registered and their statistics analyzed in terms of the noise intensity and the periods of both oscillators. This study is inspired in some complex rhythms such as a particular class of heart arrhythmia.

PhysioNet: an NIH research resource for complex signals

The Research Resource for Complex Physiologic Signals, supported by the National Institutes of Health (NIH), is intended to promote and facilitate investigations in the study of cardiovascular and other complex biomedical signals. The resource website (www.physionet.org) has 3 interdependent components: 1) PhysioBank is an archive of well-characterized digital recordings of physiologic signals and related data, including databases of electrocardiogram and heart rate time series from patients with heart failure, coronary disease, sleep apnea syndromes, and cardiac arrhythmias; 2) PhysioToolkit is a library of open-source software for physiologic signal processing and analysis; and 3) PhysioNet, for which the resource is named, is an on-line forum for dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. PhysioNet, in cooperation with the annual Computers in Cardiology conference, hosts a series of challenges inviting participants to tackle clinically interesting problems that are either unsolved or not well solved. PhysioNet invites contributions of databases and software from the biomedical community.