A study on stability analysis of atrial repolarization variability using ARX model in sinus rhythm and atrial tachycardia ECGs

© 2016 Elsevier Ireland Ltd Background The interaction between the PTa and PP interval dynamics from the surface ECG is seldom explained. Mathematical modeling of these intervals is of interest in finding the relationship between the heart rate and repolarization variability. Objective The goal of this paper is to assess the bounded input bounded output (BIBO) stability in PTa interval (PTaI) dynamics using autoregressive exogenous (ARX) model and to investigate the reason for causing instability in the atrial repolarization process. Methods Twenty-five male subjects in normal sinus rhythm (NSR) and ten male subjects experiencing atrial tachycardia (AT) were included in this study. Five minute long, modified limb lead (MLL) ECGs were recorded with an EDAN SE-1010 PC ECG system. The number of minute ECGs with unstable segments (N us ) and the frequency of premature activation (PA) (i.e. atrial activation) were counted for each ECG recording and compared between AT and NSR subjects. Results The instability in PTaI dynamics was quantified by measuring the numbers of unstable segments in ECG data for each subject. The unstable segments in the PTaI dynamics were associated with the frequency of PA. The presence of PA is not the only factor causing the instability in PTaI dynamics in NSR subjects, and it is found that the cause of instability is mainly due to the heart rate variability (HRV). C onclusion The ARX model showed better prediction of PTa interval dynamics in both groups. The frequency of PA is significantly higher in AT patients than NSR subjects. A more complex model is needed to better identify and characterize healthy heart dynamics

Incidence and predictors of premature ventricular complexes following catheter ablation for atrial fibrillation

BACKGROUND: Atrial fibrillation (AF) is the most common cardiac arrhythmia, and previous studies have focused on the epidemiology, mechanisms and risk factors for this global disease (Ryder and Benjamin 1999). Various studies have examined the mechanism, epidemiology, and risk factors for AF. One of the most common triggers for AF is believed to be premature atrial contractions (PACs) usually arising from the pulmonary veins of the left atrium, but the relationship between AF and premature ventricular complexes (PVCs) is not well understood. Studies investigating the triggers of premature beats in both the atria and ventricles are similar, so it is possible that treatment for one arrhythmia may affect the incidence of another. It is hypothesized that due to commonly shared mechanisms of triggered activity or automaticity between PACs and PVCs, and shared risk factors, that patients with AF undergoing treatment with catheter ablation may be prone to develop PVCs. OBJECTIVE: To investigate the incidence of clinically detected PVCs among patients undergoing catheter ablation for AF, and clinical predictors of PVC development in this cohort of patients. We also aim to evaluate if incident PVC detection is associated with recurrent AF following AF ablation in a cohort of 317 patients receiving treatment at a single academic medical center. METHODS: A total of 375 patients undergoing AF ablation from 2009-2012 were reviewed, and patients that underwent repeat ablations were excluded, yielding 317 patients for analysis. T-tests and Chi-squared analyses were used in univariate analyses to test for significance between characteristics of AF patients who did and did not develop PVCs. Kaplan-Meier analyses and Cox proportional hazards models were used for univariate and multivariate survival analyses, respectively, to assess the risks of incident PVC development. RESULTS: Of 317 patients with AF undergoing pulmonary vein isolation (PVI) ablation, 36.3% developed clinically detectable PVCs following ablation. A history of clinically evident PVC prior to catheter ablation for AF was associated with an 80% increase in risk of incident PVC development (HR=1.83, 95% CI 1.02-3.26, p=0.041). Additionally, a history of prior angioplasty, stent, or percutaneous coronary intervention (PCI) was associated with a 73% decreased risk of incident PVCs (HR=0.27, 95% CI 0.08-0.88, p=0.03). In patients with a history of PVC prior to ablation, or who developed PVCs after ablation, there was no significant difference in the risk of AF recurrence (HR=1.01, 95% CI 0.70-1.46, p=0.96; and HR=1.09, 95% CI 0.78-1.53, p=0.60, respectively). CONCLUSIONS: Over 1 in 3 patients develop clinically detected PVCs following catheter ablation. Predictors of incident PVC development include a history of PVC, whereas a history of angioplasty, stent, or PCI was associated with less incident PVC development. Furthermore, there was no significant association between both a history of PVC or incident PVC and risk of recurrent AF following ablation

Frequency Analysis of Atrial Fibrillation From the Surface Electrocardiogram

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy are sufficiently predictable by clinical and echocardiographic parameters. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface electrocardiogram (ECG) using digital signal processing (filtering, subtraction of averaged QRST complexes, and power spectral analysis) and shows large inter-individual variability. This measurement correlates well with intraatrial cycle length, a parameter which appears to have primary importance in AF domestication and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously, and responds better to antiarrhythmic drugs or cardioversion while high rate AF is more often persistent and refractory to therapy. In conclusion, frequency analysis of AF seems to be useful for non-invasive assessment of electrical remodeling in AF and may subsequently be helpful for guiding AF therapy

Electrocardiography in horses, part 2: how to read the equine ECG

The equine practitioner is faced with a wide variety of dysrhythmias, of which some are physiological. The recording of an exercise electrocardiogram (ECG) can help distinguish between physiological and pathological dysrhythmias, underlining the importance of exercise recordings. The evaluation of an ECG recording should be performed in a highly methodical manner in order to avoid errors. Each P wave should be followed by a QRS complex, and each QRS complex should be preceded by a P wave. The classification of dysrhythmias according to their origin helps to understand the associated changes on the ECG. In this respect, sinoatrial nodal (SA nodal), atrial myocardial, atrioventricular nodal (AV nodal) and ventricular myocardial dysrhythmias can be distinguished. Artefacts on the ECG can lead to misinterpretations. Recording an ECG of good quality is a prerequisite to prevent misinterpretations, but artefacts are almost impossible to avoid when recording during exercise. Changes in P or T waves during exercise also often lead to misinterpretations, however they have no clinical significance

How random is your heart beat?

We measure the content of random uncorrelated noise in heart rate variability using a general method of noise level estimation using a coarse grained entropy. We show that usually - except for atrial fibrillation - the level of such noise is within 5 - 15% of the variance of the data and that the variability due to the linearly correlated processes is dominant in all cases analysed but atrial fibrillation. The nonlinear deterministic content of heart rate variability remains significant and may not be ignored.Comment: see http://urbanowicz.org.p

Recurring patterns of atrial fibrillation in surface ECG predict restoration of sinus rhythm by catheter ablation

Background Non-invasive tools to help identify patients likely to benefit from catheter ablation (CA) of atrial fibrillation (AF) would facilitate personalised treatment planning. Aim To investigate atrial waveform organisation through recurrence plot indices (RPI) and their ability to predict CA outcome. Methods One minute 12-lead ECG was recorded before CA from 62 patients with AF (32 paroxysmal AF; 45 men; age 57±10 years). Organisation of atrial waveforms from i) TQ intervals in V1 and ii) QRST suppressed continuous AF waveforms (CAFW), were quantified using RPI: percentage recurrence (PR), percentage determinism (PD), entropy of recurrence (ER). Ability to predict acute (terminating vs. non-terminating AF), 3-month and 6-month postoperative outcome (AF vs. AF free) were assessed. Results RPI either by TQ or CAFW analysis did not change significantly with acute outcome. Patients arrhythmia-free at 6-month follow-up had higher organisation in TQ intervals by PD (

Quality Control in ECG-based Atrial Fibrillation Screening

This thesis comprises an introductory chapter and four papers related to quality control in ECG-based atrial fibrillation (AF) screening. Atrial fibrillation is a cardiac arrhythmia characterized by an irregular rhythm and constitutes a major risk factor for stroke. Anticoagulation therapy significantly reduces this risk, and therefore, AF screening is motivated. Atrial fibrillation screening is often done using ECGs recorded outside the clinical environment. However, the higher susceptibility of such ECGs to noise and artifacts makes the identification of patients with AF challenging. The present thesis addresses these challenges at different levels in the data analysis chain. Paper I presents a convolutional neural network (CNN)-based approach to identify transient noise and artifacts in the detected beat sequence before AF detection. The results show that by inserting a CNN, prior to the AF detector, the number of false AF detections is reduced by 22.5% without any loss in the sensitivity, suggesting that the number of recordings requiring expert review can be significantly reduced. Paper II investigates the signal quality of a novel wet electrode technology, and how the improved signal quality translates to improved beat detection and AF detection performance. The novel electrode technology is designed for reduction of motion artifacts typically present in Holter ECG recordings. The novel electrode technology shows a better signal quality and detection performance when compared to a commercially available counterpart, especially when the subject becomes more active. Thus, it has the potential to reduce the review burden and costs associated with ambulatory monitoring.Paper III introduces a detector for short-episode supraventricular tachycardia (sSVT) in AF screening recordings, which has been shown to be associated with an increased risk for future AF. Therefore, the identification of subjects with suchepisodes may increase the usefulness of AF screening. The proposed detector is based on the assumption that the beats in an sSVT episode display similar morphology, and that episodes including detections of deviating morphology should be excluded. The results show that the number of false sSVT detections can be significantly reduced (by a factor of 6) using the proposed detector.Paper IV introduces a novel ECG simulation tool, which is capable of producing ECGs with various arrhythmia patterns and with several different types of noise and artifacts. Specifically, the ECG simulator includes models to generate noise observed in ambulatory recordings, and when recording using handheld recording devices. The usefulness of the simulator is illustrated in terms of AF detection performance when the CNN training in Paper I is performed using simulated data. The results show a very similar performance when training with simulated data compared to when training with real data. Thus, the proposed simulator is a valuable tool in the development and training of automated ECG processing algorithms. Together, the four parts, in different ways, contribute to improved algorithmic efficiency in AF screening

Novel Approaches to ECG-Based Modeling and Characterization of Atrial Fibrillation

This thesis deals with signal processing algorithms for analysis of the electrocardiogram (ECG) during atrial fibrillation (AF). Such analysis can be used for diagnosing patients, and for monitoring and predicting their response to various treatment. The thesis comprises an introduction and five papers describing methods for ECG-based modeling and characterization of AF. Paper I--IV deal with methods for characterization of the atrial activity, whereas Paper V deals with modeling of the ventricular response, both problems with the assumption that AF is present. In Paper I, a number of measures characterizing the atrial activity in the ECG, obtained using time-frequency analysis as well as nonlinear methods, are evaluated for their ability to predict spontaneous termination of AF. The AF frequency, i.e, the repetition rate of the atrial fibrillatory waves of the ECG, proved to be a significant factor for discrimination between terminating and non-terminating AF. Noise is a common problem in ECG signals, particularly in long-term ambulatory recordings. Hence, robust algorithms for analysis and characterization are required. In Paper II, a robust method for tracking the AF frequency in noisy signals is presented. The method is based on a hidden Markov model (HMM), which takes the harmonic pattern of the atrial activity into account. Using the HMM-based method, the average RMS error of the frequency estimates at high noise levels was significantly lower compared to existing methods. In Paper III, the HMM-based method is employed for analysis of 24-h ambulatory ECG signals in order to explore circadian variation in AF frequency. Circadian variations reflect autonomic modulation; attenuation or absence of such variations may help to diagnose patients. Methods based on curve fitting, autocorrelation, and joint variation, respectively, are employed to quantify circadian variations, showing that it is present in most patients with long-standing persistent AF, although the short-term variation is considerable. In Paper IV, 24-h ambulatory ECG recordings with paroxysmal and persistent AF are analyzed using an entropy-based method for characterization of the atrial activity. Short segments are classified based on these measures, showing that it is feasible to distinguish between patient with paroxysmal and persistent AF from 10-s ECGs; the average classification rate was above 95%. The ventricular response during AF is mainly determined by the AV nodal blocking of atrial impulses. In Paper V, a new model-based approach for analysis of the ventricular response during AF is proposed. The model integrates physiological properties of the AV node and the atrial fibrillatory rate; the model parameters can be estimated from ECG signals. Results show that ventricular response is sufficiently represented by the estimated model in a majority of the recordings; in 85.7% of the analyzed 30-min segments the model fit was considered accurate, and that changes of AV nodal properties caused by autonomic modulation could be tracked through the estimated model parameters. In summary, the work within this thesis contributes with new methods for non-invasive analysis of AF, which can be used to tailor and evaluate different strategies for AF treatment