Recent Advances in the Noninvasive Study of Atrial Conduction Defects Preceding Atrial Fibrillation

The P-wave represents the electrical activity in the electrocardiogram (ECG) associated with the heart\u27s atrial contraction. This wave has merited significant research efforts in recent years with the aim to characterize atrial depolarization from the ECG. Indeed, the alterations of the P-wave main time, frequency, and wavelet features have been widely studied to predict the onset of atrial fibrillation (AF), both spontaneously and after a specific treatment, such as pharmacological or electrical cardioversion, catheter ablation, as well as cardiac surgery. To this respect, the P-wave prolongation is today a clinically accepted marker of high risk of suffering AF. However, given the relatively low P-wave amplitude in the ECG, its analysis has been most widely carried out from signal-averaged ECG signals. Unfortunately, these kind of recordings are uncommon in routine clinical practice and, moreover, they obstruct the possibility of studying the information carried by each single P-wave as well as its variability over time. These limitations have motivated the recent development of the beat-to-beat P-wave analysis, which has proven to be very useful in revealing interesting information about the altered atrial conduction preceding the onset of AF. Within this context, the main goal of this chapter is to review the most recent advances reached by this kind of analysis in the noninvasive assessment of atrial conduction alterations. Thus, the chapter will introduce and discuss the existing methods of the beat-to-beat P-wave analysis and their application to predict the onset of AF as well as its advantages and disadvantages compared with the signal-averaged P-wave analysis

Exponential distribution of long heart beat intervals during atrial fibrillation and their relevance for white noise behaviour in power spectrum

The statistical properties of heart beat intervals of 130 long-term surface electrocardiogram recordings during atrial fibrillation (AF) are investigated. We find that the distribution of interbeat intervals exhibits a characteristic exponential tail, which is absent during sinus rhythm, as tested in a corresponding control study with 72 healthy persons. The rate of the exponential decay lies in the range 3-12 Hz and shows diurnal variations. It equals, up to statistical uncertainties, the level of the previously uncovered white noise part in the power spectrum, which is also characteristic for AF. The overall statistical features can be described by decomposing the intervals into two statistically independent times, where the first one is associated with a correlated process with 1/f noise characteristics, while the second one belongs to an uncorrelated process and is responsible for the exponential tail. It is suggested to use the rate of the exponential decay as a further parameter for a better classification of AF and for the medical diagnosis. The relevance of the findings with respect to a general understanding of AF is pointed out

Prediction of postoperative atrial fibrillation using the electrocardiogram: A proof of concept

Hospital patients recovering from major cardiac surgery are at high risk of postoperative atrial fibrillation (POAF), an arrhythmia which can be life-threatening. With the development of a tool to predict POAF early enough, the development of the arrhythmia could be potentially prevented using prophylactic treatments, thus reducing risks and hospital costs. To date, no reliable method suitable for autonomous clinical integration has been proposed yet. This thesis presents a study on the prediction of POAF using the electrocardiogram. A novel P-wave quality assessment tool to automatically identify high-quality P-waves was designed, and its clinical utility was assessed. Prediction of paroxysmal atrial fibrillation (AF) was performed by implementing and improving a selection of previously proposed methods. This allowed to perform a systematic comparison of those methods, and to test if their combination improved prediction of AF. Finally, prediction of POAF was tested in a clinically relevant scenario. This included studying the 48 hours preceding POAF, and automatically excluding noise-corrupted P-waves using the quality assessment tool. The P-wave quality assessment tool identified high-quality P-waves with high sensitivity (0.93) and good specificity (0.84). In addition, this tool improved the ability to predict AF, since it improved the precision of P-wave measurements. The best predictors of AF and POAF were measurements of the variability in P-wave time- and morphological features. Paroxysmal AF could be predicted with high specificity (0.93) and good sensitivity (0.82) when several predictors were combined. Furthermore, POAF could be predicted 48 hours before its onset with good sensitivity (0.74) and specificity (0.70). This leaves time for prophylactic treatments to be administered and possibly prevent POAF. Despite being promising, further work is required for these techniques to be useful in the clinical setting

The Role of Electrocardiographic Markers in the Prevention of Atrial and Ventricular Arrhythmias

In our chapter, we overview the main clinical conditions that increase arrhythmogenicity, and we present the surface electrocardiogram (ECG) markers that could be suitable for the prediction of atrial and ventricular arrhythmias. We highlight the clinical value of the prolongation of the P-wave duration and P dispersion (Pd) in the prediction of atrial fibrillation, and we also expound the utility of QT interval, T-wave peak-to-end interval (Tpe), and Tpe/QT ratio (known as arrhythmogenic index (AIX)) in the prediction of ventricular arrhythmias. Furthermore, we present the results of our clinical investigations with regard to surface ECG markers among patients with increased arrhythmia vulnerability. Moreover, we mention other, novel, effectively used ECG markers

Methodology for detection of paroxysmal atrial fibrillation based on P-Wave, HRV and QR electrical alternans features

The detection of Paroxysmal Atrial Fibrillation (PAF) is a fairly complex process performed manually by cardiologists or electrophysiologists by reading an electrocardiogram (ECG). Currently, computational techniques for automatic detection based on fast Fourier transform (FFT), Bayes optimal classifier (BOC), k-nearest neighbors (K-NNs), and artificial neural network (ANN) have been proposed. In this study, six features were obtained based on the morphology of the P-Wave, the QRS complex and the heart rate variability (HRV) of the ECG. The performance of this methodology was validated using clinical ECG signals from the Physionet arrhythmia database MIT-BIH. A feedforward neural network was used to detect the presence of PAF reaching a general accuracy of 97.4%. The results obtained show that the inclusion of the information of the P-Wave, HRV and QR Electrical alternans increases the accuracy to identify the PAF event compared to other works that use the information of only one or at most two of them

Atrial conduction and atrial fibrillation: What can we learn from surface ECG?

Despite the advancements in pharmacological and non-pharmacological management of atrial fibrillation (AF) observed during last decades, available treatment modalities and predictors of their success are still far from optimal. Understanding of pathophysiological mechanisms underlying AF and assessment of atrial electrophysiological properties using easily available non-invasive diagnostic tools such as surface ECG are essential for further improvement of patient-tailored treatment strategies. P-wave duration is generally accepted as the most reliable non-invasive marker of atrial conduction and its prolongation has been associated with history of AF. However, patients with paroxysmal AF without structural heart disease may not have any impressive P-wave prolongation thus suggesting that the global conduction slowing is not an obligatory requirement for development of AF. In these settings, the morphology of P-wave becomes an important source of information concerning propagation of atrial activation. One of the most common morphologies, i.e. biphasic configuration of P-waves in right precordial leads has been considered a marker of left atrial enlargement but, seen in patients with structurally normal hearts, appears to be linked to an interatrial conduction defect. Recent advances in endocardial mapping technologies have linked certain P-wave morphologies with interatrial conduction patterns that may have clinical implications for invasive treatment of AF patients. The value of P-wave morphology extends beyond cardiac arrhythmias associated with atrial conduction delay and can be used for prediction of clinical outcome of wide range of cardiovascular disorders such as survival after myocardial infarction or the risk of stroke

Effect of high-pass filtering on ECG signal on the analysis of patients prone to atrial fibrillation

The aim of this study was to assess the effect of filtering techniques on the time-domain analysis of the ECG. Multi-lead ECG recordings obtained from chronic atrial fibrillation (AF) patients after successful external cardioversion have been acquired. Several high-pass filtering techniques and three cut-off frequency values were used: Bessel and Butterworth four-pole and two-pole bidirectional and unidirectional filters, at 0.01, 0.05 and 0.5 Hz low cut-off frequency. As a reference, a beat-by-beat linear piecewise interpolation was used to remove baseline wander, on each P-wave. Results show that ECG filtering affects the estimation of P-wave duration in a manner that depends upon the type of filter used: particularly, the bidirectional filters caused negligible variation of P-wave duration, while unidirectional ones provoked an increase higher than 8%

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

Quantification of not-dipolar components of atrial depolarization by principal component analysis of the P-wave

BACKGROUND: Principal component analysis (PCA) of the T-wave has been demonstrated to quantify the dipolar and not-dipolar components of the ventricular activation, the latter reflecting repolarization heterogeneity. Accordingly, the PCA of the P-wave could help in analyzing the heterogeneous propagation of sinus impulses in the atria, which seems to predispose to fibrillation. AIM: The aim of this study is to perform the PCA of the P-wave in patients prone to atrial fibrillation (AF). METHODS: PCA is performed on P-waves extracted by averaging technique from ECG recordings acquired using a 32-lead mapping system (2048 Hz, 24 bit, 0-400 Hz bandwidth). We extracted PCA parameters related to the dipolar and not dipolar components of the P-wave using the first 3 eigenvalues and the cumulative percent of variance explained by the first 3 PCs (explained variance EV). RESULTS AND CONCLUSIONS: We found that the EV associated to the low risk patients is higher than that associated to the high risk patients, and that, correspondingly, the first eigenvalue is significantly lower while the second one is significantly higher in the high risk patients respect to the low risk group. Factor loadings showed that on average all leads contribute to the first principal component