A Quantitative Model for the Ventricular Response During Atrial Fibrillation

We review the principal statistical properties of the RR interval sequence during atrial fibrillation. A simple quantitative electrophysiologic model is presented which successfully accounts for these statistical features. In this model the atrioventricular junction (AVJ) is treated as a single cell equivalent characterized by a refractory period and spontaneous rate of phase 4 depolarization. The atria are presumed to bombard the AVJ with impulses that arrive randomly in time; each impulse induces a partial depolarization of the AVJ equivalent cell. We show that other models for the ventricular response during atrial fibrillation (e. g., concealed conduction models) do not adequately account for the salient statistical features of the RR interval sequence. The present model may be utilized to characterize a sequence of RR intervals recorded from a given individual in terms of the numerical magnitudes of the model's four parameters: the mean rate at which atrial impulses bombard the AVJ, the relative amplitude of the impulses, the relative rate of spontaneous phase 4 depolarization of the AVJ equivalent cell, and the refractory period of the AVJ equivalent cell. Such a characterization may be useful in studying mechanisms of drug action and interaction, as well as potentially offering a quantitative means for optimizing pharmacologic manangement of chronic atrial fibrillation.     

An atrioventricular node model for analysis of the ventricular response during atrial fibrillation

This paper introduces a model of the atrioventricular node function during atrial fibrillation (AF), and describes the related ECG-based estimation method. The proposed model is defined by parameters that characterize the arrival rate of atrial impulses, the probability of an impulse choosing either one of the two atrioventricular nodal pathways, the refractory periods of these pathways, and the prolongation of the refractory periods. These parameters are estimated from the RR intervals using maximum likelihood estimation, except for the shorter refractory period which is estimated from the RR interval Poincaré plot, and the mean arrival rate of atrial impulses by the AF frequency. Simulations indicated that 200-300 RR intervals are generally needed for the estimates to be accurate. The model was evaluated on 30-min ECG segments from 36 AF patients. The results showed that 88% of the segments can be accurately modeled when the estimated probability density function (PDF) and an empirical PDF were at least 80% in agreement. The model parameters were estimated during head-up tilt test to assess differences caused by sympathetic stimulation. Both refractory periods decreased as a result of stimulation, and the likelihood of an impulse choosing the pathway with the shorter refractory period increased.     

A technique for measurement of the extent of spatial organizationof atrial activation during atrial fibrillation in the intact humanheart

Atrial fibrillation (AF) is a common clinical problem, associated with considerable morbidity and motility, for which effective management strategies have yet to be devised. The absence of objective measures to guide selection of antiarrhythmic drug therapy for maintenance of sinus rhythm leaves only clinical endpoints (either beneficial or detrimental) for assessment of drug action, with occasional catastrophic consequences. As part of an attempt to provide an objective framework for the assessment of antiarrhythmic drug action on the electrophysiologic determinants of atrial fibrillation, the authors have developed a measure of the spatial organization of atrial activation processes during atrial fibrillation. By recording activation sequences at multiple equally spaced locations on the endocardial surface of the atrial during atrial fibrillation in humans and determining the degree of correlation between these activation sequences as a function of distance, the authors have been able to construct spatial correlation functions for atrial activation. They have found that atrial activation remains well-correlated, independent of distance during normal sinus rhythm and atrial flutter. During atrial fibrillation, correlation decays monotonically with distance and the space-constant for this decay may be used to describe the relative spatial organization of atrial fibrillation. The authors provide examples of the impact of antiarrhythmic agents on the space-constant and suggest that assessment of the relative spatial organization of atrial activation using this methodology may potentially provide an objective framework to guide therapy in patients with AF     

Cellular automaton model of ventricular fibrillation

A theoretical analysis of ventricular fibrillation and the requirements for fibrillation are performed using a discrete element neighborhood (cellular automaton) model of ventricular conduction. The model is configured as a 2500 element rectangular grid on the surface of a cylinder. It is shown that vulnerability to fibrillation is strongly influenced by excited state duration which primarily determines the nature of the underlying reentry activity. As excited state duration is increased fibrillation changes from "coarse" macroreentrant activity to the more chaotic "fine" fibrillation sustained by multiple wavelets of microreentry. In general, defibrillation is achieved by a stimulus strong enough to depolarize the majority of relative refractory elements. The threshold for defibrillation is increased for the more irregular microreentrant fibrillation.     

Evaluation of techniques for recognition of ventricular arrhythmiasby implanted devices

Implantable devices that provide antitachycardia and defibrillation capability currently have limited ability to distinguish among different cardiac rhythms. We have investigated three methods of electrogram analysis: rate, irregularity, and amplitude distribution. In 35 episodes in 19 patients, we applied these three algorithms to 15 s recorded passages of ventricular electrograms during supraventricular tachycardia (N = 11), ventricular tachycardia (N = 11), and ventricular fibrillation (N = 13). Each was individually paired with a recording of sinus rhythm from the same patient. All recordings were obtained during standard electrophysiologic testing. Each algorithm was successful at distinguishing the tachyarrhythmias from sinus rhythm at one or more levels of algorithm parameterization. Rate alone discriminated supraventricular tachycardia from ventricular fibrillation but did not distinguish between supraventricular and ventricular tachycardia. Rate combined with irregularity distinguished between ventricular tachycardia and ventricular fibrillation, but did not discriminate between ventricular and supraventricular tachycardia. Although the amplitude distribution algorithm was unable to separate perfectly any of the three tachyarrhythmias, it provided the best performance in separating supraventricular and ventricular tachycardia (82 percent sensitivity and specificity). We conclude that algorithms based on rate, irregularity, and amplitude distribution analysis of ventricular electrograms may distinguish sinus rhythm from tachyarrhythmias, but may not distinguish among tachyarrhythmias.     

Atrial activity extraction for atrial fibrillation analysis using blind source separation

This contribution addresses the extraction of atrial activity (AA) from real electrocardiogram (ECG) recordings of atrial fibrillation (AF). We show the appropriateness of independent component analysis (ICA) to tackle this biomedical challenge when regarded as a blind source separation (BSS) problem. ICA is a statistical tool able to reconstruct the unobservable independent sources of bioelectric activity which generate, through instantaneous linear mixing, a measurable set of signals. The three key hypothesis that make ICA applicable in the present scenario are discussed and validated: 1) AA and ventricular activity (VA) are generated by sources of independent bioelectric activity; 2) AA and VA present non-Gaussian distributions; and 3) the generation of the surface ECG potentials from the cardioelectric sources can be regarded as a narrow-band linear propagation process. To empirically endorse these claims, an ICA algorithm is applied to recordings from seven patients with persistent AF. We demonstrate that the AA source can be identified using a kurtosis-based reordering of the separated signals followed by spectral analysis of the sub-Gaussian sources. In contrast to traditional methods, the proposed BSS-based approach is able to obtain a unified AA signal by exploiting the atrial information present in every ECG lead, which results in an increased robustness with respect to electrode selection and placement.     

Detection of atrial activity from high-voltage leads of implantableventricular defibrillators using a cancellation technique

The inability to detect atrial activity limits implantable ventricular cardioverter defibrillators (ICD) in discriminating tachycardias and can result in inappropriate therapy. This study attempted to detect atrial activity on the wide-spaced bipole signals formed by the high-voltage (HV) leads of the ICD during device implantation and to develop an algorithm for the detection of atrial fibrillation (AFib) from these signals. The authors used a method that cancelled ventricular and correlated atrial activity from the HV lead signals and measured frequency and amplitude distribution information to discriminate sinus rhythm (SR) and AFib segments. The authors analyzed 186 data segments from 21 patients (six AFib, 14 SR, one AFib and SR). For individual segments in this data set, the sensitivity of the algorithm was 78%, specificity 92.65%, positive and negative predictive values 79.59 and 91.97%, respectively. These results demonstrate that atrial activity is present in the HV lead signals, and AFib detection can be achieved in many, but not all cases, using information currently available to ICDs. Prior work from surface electrocardiograms suggests that this algorithm can function during ventricular tachycardias. However, specificity of the algorithm is not high enough for clinical use     

Convolutive blind source separation algorithms applied to the electrocardiogram of atrial fibrillation: study of performance

The analysis of the surface electrocardiogram (ECG) is the most extended noninvasive technique in medical diagnosis of atrial fibrillation (AF). In order to use the ECG as a tool for the analysis of AF, we need to separate the atrial activity (AA) from other cardioelectric signals. In this matter, statistical signal processing techniques, like blind source separation (BSS), are able to perform a multilead statistical analysis with the aim to obtain the AA. Linear BSS techniques can be divided in two groups depending on the mixing model: algorithms where instantaneous mixing of sources is assumed, and convolutive BSS (CBSS) algorithms. In this work, a comparison of performance between one relevant CBSS algorithm, namely Infomax, and one of the most effective independent component analysis (ICA) algorithms, namely FastICA, is developed. To carry out the study, pseudoreal AF ECGs have been synthesized by adding fibrillation activity to normal sinus rhythm. The algorithm performances are expressed by two indexes: the signal to interference ratio (SIRAA) and the cross-correlation (RAA) between the original and the estimated AA. Results empirically prove that the instantaneous mixing model is the one that obtains the best results in the AA extraction, given that the mean SIRAA obtained by the FastICA algorithm (37.6 +/- 17.0 dB) is higher than the main SIRAA obtained by Infomax (28.5 +/- 14.2 dB). Also the RAA obtained by FastICA (0.92 +/- 0.13) is higher than the one obtained by Infomax (0.78 +/- 0.16).     

A detector for a chronic implantable atrial tachyarrhythmia monitor

Continuous long-term monitoring of atrial fibrillation (AF) and tachycardia (AT) is an unmet clinical need, which could be met with a chronically-implanted monitor. Improved therapeutic decisions based on accurate monitoring of parameters, such as daily AF/AT burden (hours/ day) may lead to improvements in clinical outcomes such as reduction in hospitalizations, symptoms, and strokes. This paper describes an AF/AT detector that detects AF as well as AT with an irregular ventricular response, and a supplementary AT detector for AT with more regular ventricular response. Seven databases with significant durations of AF, AT, and sinus rhythm were used to evaluate the performance of the detectors. All patient records with AF (N = 124) were detected by the AF/AT detector to have AF/AT burden with a mean, median, and 75 percentile of absolute error in burden detection of 8.8, 0, and 4 min, respectively. In patients having AF burden (= or > 10 min), the AF/AT detector was found to have burden accuracy within 20% of true burden in 96% of patients. The specificity was 94%, defined as follows: in patient records without AF/AT (N = 174), the percentage with AF/AT burden = or < 10 min in the 24-h recordings. The AF/AT detector underestimatesAT burden, thus degrading performance, in patients with significant amounts of AT with more regular ventricular response. The supplementary AT detector reduces the underestimation of AT while overestimating burden in patients without a significant amount of AT. The detectors described here could be implemented in an implantable monitor for accurate long-term AF/AT monitoring.     

Phase-rectified signal averaging used to estimate the dominant frequencies in ECG signals during atrial fibrillation

Phase-rectified signal averaging (PRSA) is a technique recently introduced to enhance quasi-periodic signal components. An important parameter that can be extracted from surface ECG is the dominant frequency (DF) of atrial fibrillation (AF). AF signal components are always highly contaminated by the ventricular complexes, and the cancellation of these components is never perfect. The remaining artifacts tend to induce erroneous DF estimates. In this paper, we report on the use of PRSA in the context of noninvasive AF classification procedures for improving DF estimation. The potential of PRSA is demonstrated by experiments both on synthetic and clinical ECG signals.     

Analysis of a novel expanded tip wire (ETW) antenna for microwave ablation of cardiac arrhythmias

A novel expanded tip wire (ETW) catheter antenna is proposed for microwave ablation for the treatment of atrial fibrillation (AF). The antenna is designed as an integral part of coaxial cable so that it can be inserted via a 6F catheter. A numerical model based on the rotationally symmetric finite-difference time-domain technique incorporating the generalized perfectly matched layer as the absorbing boundary condition has been utilized to accurately model the interaction between the antenna and the myocardium. Numerical and in-vitro experimental results are presented for specific absorption rate, return loss and heating pattern produced by the antenna. Both numerical modeling and in-vitro experimentation show that the proposed ETW antenna produces a well-defined electric field distribution that provides continuous long and linear lesions for the treatment of AF.     

A logical state model of circus movement atrial flutter. Role ofanatomic obstacles, anisotropic conduction and slow conduction zones oninduction, sustenance, and overdrive paced modulation of reentrantcircuits

Because the relative roles of anatomical obstacles, in combination with functional barriers, anisotropic conduction, and slow conduction can not be readily assessed with current electrophysiological techniques, an atrial activation model was developed to study the mechanisms of circus movement atrial flutter. A discrete state model consisting of 4,096 logically connected cardiac elements was used to simulate atrial activation; an inexcitable region simulating the inferior vena cava (IVC) was also incorporated in the model. Atrial flutter was induced by programmed premature stimulation, Anisotropic conduction velocity properties, regional variations in slow conduction, regional refractory gradients and stimulation parameters were specified for each simulation. The reentrant circuit generally consisted of a single reentrant impulse which circulated around a continuous line of functional bidirectional conduction block joined to the IVC. Rapid pacing, 5-30 ms shorter than the spontaneous reentrant cycle length, was applied to entrain and/or terminate the rhythm. The results of this study demonstrate that patterns of initiation, entrainment, termination and reinitiation of circus movement atrial flutter mimic results from in vivo activation mapping studies. The authors find that sustained circus movement atrial flutter circuits depend on: 1) natural anatomical obstacles to stabilize reentrant circuits, and 2) anisotropic conduction properties to reduce the degree of functional conduction block needed to maintain circus movement. Rapid pacing of simulated circus movement atrial flutter demonstrated that the entrainment criteria can be satisfied in a two-dimensional syncytium     

Temporal and spatial phase analyses of the electrocardiogram stratify intra-atrial and intra-ventricular organization

We hypothesized that electrocardiogram (ECG) spatial phase analysis would define a spectrum of intracardiac organization from atrial fibrillation (AF), nonisthmus-dependent and isthmus-dependent atrial flutter (AFL) to supraventricular tachycardias (SVT), and similarly for ventricular arrhythmias. We analyzed arrhythmia ECGs of 33 patients with isthmus (n = 9) and nonisthmus (n = 5) dependent AFL and SVT: atrial (n = 3), atrioventricular nodal (n = 3), and orthodromic reciprocating (n = 3) tachycardias, as well as AF (n = 5), ventricular tachycardia (monomorphic, VT-MM; n = 7), and fibrillation (VF; n = 3). ECG spatial phase was considered coherent when the correlation coefficient of an atrial (or ventricular) template to its ECG over time maintained a constant relationship in XY, XZ, and YZ planes. Regularity was quantified spectrally from ECG and correlation series. Spatial coherence occurred in 9/9 cases of isthmus--but only 1/5 of cases of nonisthmus-dependent AFL (p < 0.01; chi2). All showed one dominant spectral peak (temporal coherence). In AF, spatial phase was inconsistent in all planes and spectra were broad band. Temporal and spatial coherence occurred in other SVT. VT-MM maintained spatial phase and a single spectral peak, while VF displayed neither. Our conclusions are that temporal and spatial phase analysis from the ECG stratifies intra-atrial and intra-ventricular organization and reveals subtle variability lost on visual inspection.     

Noninvasive ECG as a tool for predicting termination of paroxysmal atrial fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia and entails an increased risk of thromboembolic events. Prediction of the termination of an AF episode, based on noninvasive techniques, can benefit patients, doctors and health systems. The method described in this paper is based on two-lead surface electrocardiograms (ECGs): 1-min ECG recordings of AF episodes including N-type (not terminating within an hour after the end of the record), S-type (terminating 1 min after the end of the record) and T-type (terminating immediately after the end of the record). These records are organised into three learning sets (N, S and T) and two test sets (A and B). Starting from these ECGs, the atrial and ventricular activities were separated using beat classification and class averaged beat subtraction, followed by the evaluation of seven parameters representing atrial or ventricular activity. Stepwise discriminant analysis selected the set including dominant atrial frequency (DAF, index of atrial activity) and average HR (HRmean, index of ventricular activity) as optimal for discrimination between N/T-type episodes. The linear classifier, estimated on the 20 cases of the N and T learning sets, provided a performance of 90% on the 30 cases of a test set for the N/T-type discrimination. The same classifier led to correct classification in 89% of the 46 cases for N/S-type discrimination. The method has shown good results and seems to be suitable for clinical application, although a larger dataset would be very useful for improvement and validation of the algorithms and the development of an earlier predictor of paroxysmal AF spontaneous termination time.     

Preventive ablation strategies in a biophysical model of atrial fibrillation based on realistic anatomical data.

Ablation strategies to prevent episodes of paroxysmal atrial fibrillation (AF) have been subject to many clinical studies. The issues mainly concern pattern and transmurality of the lesions. This paper investigates ten different ablation strategies on a multilayered 3-D anatomical model of the atria with respect to 23 different setups of AF initiation in a biophysical computer model. There were 495 simulations carried out showing that circumferential lesions around the pulmonary veins (PVs) yield the highest success rate if at least two additional linear lesions are carried out. The findings compare with clinical studies as well as with other computer simulations. The anatomy and the setup of ectopic beats play an important role in the initiation and maintenance of AF as well as the resulting therapy. The computer model presented in this paper is a suitable tool to investigate different ablation strategies. By including individual patient anatomy and electrophysiological measurement, the model could be parameterized to yield an effective tool for future investigation of tailored ablation strategies and their effects on atrial fibrillation.     

Comparison of atrial signal extraction algorithms in 12-lead ECGs with atrial fibrillation

Analysis of atrial rhythm is important in the treatment and management of patients with atrial fibrillation. Several algorithms exist for extracting the atrial signal from the electrocardiogram (ECG) in atrial fibrillation, but there are few reports on how well these techniques are able to recover the atrial signal. We assessed and compared three algorithms for extracting the atrial signal from the 12-lead ECG. The 12-lead ECGs of 30 patients in atrial fibrillation were analyzed. Atrial activity was extracted by three algorithms, Spatiotemporal QRST cancellation (STC), principal component analysis (PCA), and independent component analysis (ICA). The amplitude and frequency characteristics of the extracted atrial signals were compared between algorithms and against reference data. Mean (standard deviation) amplitude of QRST segments of V1 was 0.99 (0.54) mV, compared to 0.18 (0.11) mV (STC), 0.19 (0.13) mV (PCA), and 0.29 (0.22) mV (ICA). Hence, for all algorithms there were significant reductions in the amplitude of the ventricular activity compared with that in V1. Reference atrial signal amplitude in V1 was 0.18 (0.11) mV, compared to 0.17 (0.10) mV (STC), 0.12 (0.09) mV (PCA), and 0.18 (0.13) mV (ICA) in the extracted atrial signals. PCA tended to attenuate the atrial signal in these segments. There were no significant differences for any of the algorithms when comparing the amplitude of the reference atrial signal with that of the extracted atrial signals in segments in which ventricular activity had been removed. There were no significant differences between algorithms in the frequency characteristics of the extracted atrial signals. There were discrepancies in amplitude and frequency characteristics of the atrial signal in only a few cases resulting from notable residual ventricular activity for PCA and ICA algorithms. In conclusion, the extracted atrial signals from these algorithms exhibit very similar amplitude and frequency characteristics. Users of these algorithms should be observant of residual ventricular activities which can affect the analysis of the fibrillatory waveform in clinical practice.     

A simple electrical-mechanical model of the heart applied to thestudy of electrical-mechanical alternans

Recent evidence has shown that a subtle alternation in the surface ECG (electrical alternans) may be correlated with the susceptibility to ventricular fibrillation. This paper presents evidence that a mechanical alternation in the heart beat (mechanical alternans) generally accompanies electrical alternans. A simple finite-element computer model, which emulates both the electrical and mechanical activity of the heart, is presented. A pilot animal study is also reported. The computer model and the animal study both found that 1) there exists a regime of combined electrical-mechanical alternans during the transition from a normal rhythm towards a fibrillatory rhythm, 2) the detected degree of alternation is correlated with the relative instability of the rhythm, and 3) the electrical and mechanical alternans may result from a dispersion in local electrical properties leading to a spatial-temporal alternation in the electrical conduction process.     

Waveform Characterization of Atrial Fibrillation Using Phase Information

A novel method for characterization of f-wave morphology in atrial fibrillation (AF) is presented. The method decomposes atrial activity into fundamental and harmonic components, dividing each component into short blocks for which the amplitudes, frequencies, and phases are estimated. The phase delays between the fundamental and each of the harmonics, here referred to as harmonic phase relationships, are used as features of f-wave morphology. The estimated waves are clustered into typical morphologic patterns. The performance of the method is illustrated by simulated signals, ECG signals recorded from 36 patients with organized AF, and an ECG signal recorded during drug loading with flecainide. The results show that the method can distinguish a wide variety of f-wave morphologies, and that typical morphologies can be established for further analysis of AF.      

A novel method for detection of the transition between atrial fibrillation and sinus rhythm

Automatic detection of atrial fibrillation (AF) for AF diagnosis, especially for AF monitoring, is necessarily desirable for clinical therapy. In this study, we proposed a novel method for detection of the transition between AF and sinus rhythm based on RR intervals. First, we obtained the delta RR interval distribution difference curve from the density histogram of delta RR intervals, and then detected its peaks, which represented the AF events. Once an AF event was detected, four successive steps were used to classify its type, and thus, determine the boundary of AF: 1) histogram analysis; 2) standard deviation analysis; 3) numbering aberrant rhythms recognition; and 4) Kolmogorov-Smirnov (K-S) test. A dataset of 24-h Holter ECG recordings (n = 433) and two MIT-BIH databases (MIT-BIH AF database and MIT-BIH normal sinus rhythm (NSR) database) were used for development and evaluation. Using the receiver operating characteristic curves for determining the threshold of the K-S test, we have achieved the highest performance of sensitivity and specificity (SP) (96.1% and 98.1%, respectively) for the MIT-BIH AF database, compared with other previously published algorithms. The SP was 97.9% for the MIT-BIH NSR database.     

A high-temporal resolution algorithm for quantifying organization during atrial fibrillation

Atrial fibrillation (AF) has been described as a "random" or "chaotic" rhythm. Evidence suggests that AF may have transient episodes of temporal and spatial organization. We introduce a new algorithm that quantifies AF organization by the mean-squared error (MSE) in the linear prediction between two cardiac electrograms. This algorithm calculates organization at a finer temporal resolution. (approximately 300 ms) than previously published algorithms. Using canine atrial epicardial mapping data, we verified that the MSE algorithm showed nonfibrillatory rhythms to be significantly more organized than fibrillatory rhythms (p < .00001). Further, we compared the sensitivity of MSE to that of two previously published algorithms by analyzing AF with simulated noise and AF manipulated with vagal stimulation or by adenosine administration to alter the character of the AF. MSE performed favorably in the presence of noise. While all three algorithms distinguished between low and high vagal AF, MSE was the most sensitive in its discrimination. Only MSE could distinguish baseline AF from AF with adenosine. We conclude that our algorithm can distinguish different levels of organization during AF with a greater temporal resolution and sensitivity than previously described algorithms. This algorithm could lead to new ways of analyzing and understanding AF as well as improved techniques in AF therapy.