Diurnal variations of the dominant cycle length of chronic atrial fibrillation

Heart rate characteristic based modelling of atrial fibrillatory rate using implanted cardiac monitor data

Objective. The objective of the present study is to investigate the feasibility of using heart rate characteristics to estimate atrial fibrillatory rate (AFR) in a cohort of atrial fibrillation (AF) patients continuously monitored with an implantable cardiac monitor. We will use a mixed model approach to investigate population effect and patient specific effects of heart rate characteristics on AFR, and will correct for the effect of previous ablations, episode duration, and onset date and time.Approach. The f-wave signals, from which AFR is estimated, were extracted using a QRST cancellation process of the AF episodes in a cohort of 99 patients (67% male; 57 ± 12 years) monitored for 9.2(0.2-24.3) months as median(min-max). The AFR from 2453 f-wave signals included in the analysis was estimated using a model-based approach. The association between AFR and heart rate characteristics, prior ablations, and episode-related features were modelled using fixed-effect and mixed-effect modelling approaches.Main results. The mixed-effect models had a better fit to the data than fixed-effect models showing h.c. of determination (R2 = 0.49 versusR2 = 0.04) when relating the variations of AFR to the heart rate features. However, when correcting for the other factors, the mixed-effect model showed the best fit (R2 = 0.04). AFR was found to be significantly affected by previous catheter ablations (p< 0.05), episode duration (p< 0.05), and irregularity of theRRinterval series (p< 0.05).Significance. Mixed-effect models are more suitable for AFR modelling. AFR was shown to be faster in episodes with longer duration, less organizedRRintervals and after several ablation procedures.

Respiratory Induced Modulation in f-Wave Characteristics During Atrial Fibrillation

The autonomic nervous system (ANS) is an important factor in cardiac arrhythmia, and information about ANS activity during atrial fibrillation (AF) may contribute to personalized treatment. In this study we aim to quantify respiratory modulation in the f-wave frequency trend from resting ECG. First, an f-wave signal is extracted from the ECG by QRST cancelation. Second, an f-wave model is fitted to the f-wave signal to obtain a high resolution f-wave frequency trend and an index for signal quality control ( S ). Third, respiratory modulation in the f-wave frequency trend is extracted by applying a narrow band-pass filter. The center frequency of the band-pass filter is determined by the respiration rate. Respiration rate is estimated from a surrogate respiration signal, obtained from the ECG using homomorphic filtering. Peak conditioned spectral averaging, where spectra of sufficient quality from different leads are averaged, is employed to obtain a robust estimate of the respiration rate. The envelope of the filtered f-wave frequency trend is used to quantify the magnitude of respiratory induced f-wave frequency modulation. The proposed methodology is evaluated using simulated f-wave signals obtained using a sinusoidal harmonic model. Results from simulated signals show that the magnitude of the respiratory modulation is accurately estimated, quantified by an error below 0.01 Hz, if the signal quality is sufficient ( S > 0 . 5 ). The proposed method was applied to analyze ECG data from eight pacemaker patients with permanent AF recorded at baseline, during controlled respiration, and during controlled respiration after injection of atropine, respectively. The magnitude of the respiratory induce f-wave frequency modulation was 0.15 ± 0.01, 0.18 ± 0.02, and 0.17 ± 0.03 Hz during baseline, controlled respiration, and post-atropine, respectively. Our results suggest that parasympathetic regulation affects the magnitude of respiratory induced f-wave frequency modulation.

Effects of refractory gradients and ablation on fibrillatory activity

BACKGROUND

The mechanisms involved in onset, maintenance, and termination of atrial fibrillation are not well understood. A biophysical model could be useful to determine how the events unfold.

METHOD

A two-dimensional cellular automaton consisting of 576 × 576 grid nodes was implemented to demonstrate the types of electrical activity that may occur in compromised atrial substrate. Electrical activation between nodes was made anisotropic (2:1), and the refractory period (RP) was adjusted from 74 to 192 ms in the spatial domain. Presence of collagen fibers were simulated as short lines of conduction block at many random grid sites, while ablation lesions were delineated as longer lines of block. An S1-S2 pulse from one grid corner was utilized to initiate simulated electrical activity. Simulations were done in which 1. no ablation lines, 2. random ablation lines, and 3. parallel ablation lines were added to the grid to determine how this affected the formation and annihilation of rotational activity after S1-S2 stimulation.

RESULTS

As the premature (S2) wavefront traversed the grid, rotational activity formed near boundaries where wavefronts propagated from shorter to longer refractory regions, causing unidirectional block, and were anchored by fiber clusters. Multiple wavelets appeared when wavefronts originating from different driving rotational features collided, and/or by their encounter with RP discontinuities. With the addition of randomly orientated simulated ablation lesions, followed by reinduction of fibrillatory activity, mean activation interval (AI) prolonged from a baseline level of 144.2 ms-160.3 ms (p < 0.001 in most comparisons). During fibrillatory activity, when parallel ablation lines were added to short RP regions, AI prolonged to 150.4 ms (p < 0.001), and when added to long RP regions, AI prolonged to 185.3 ms (p < 0.001). In all cases, AI prolongation after simulated ablation resulted from reduced number and/or from the isolation of local drivers, so that distant drivers in short RP regions activated long RP regions N:1, while distant drivers in long RP regions activated short RP regions at a relatively slow rate.

CONCLUSIONS

An automaton model was found useful to generate and test hypotheses concerning fibrillatory activity, which can then be validated in the clinical electrophysiology laboratory.

Association between atrial fibrillatory rate and heart rate variability in patients with atrial fibrillation and congestive heart failure

BACKGROUND

Even if atrial fibrillatory rate (AFR) has been related to clinical outcome in patients with atrial fibrillation (AF), its relation with ventricular response has not been deeply studied. The aim of this study was to investigate the relation between AFR and RR series variability in patients with AF.

METHODS

Twenty-minute electrocardiograms in orthogonal leads were processed to extract AFR, using spatiotemporal QRST cancellation and time frequency analysis, and RR series in 127 patients (age 69 ± 11 years) with congestive heart failure (NYHA II-III) enrolled in the MUSIC study (MUerte Subita en Insufficiencia Cardiaca). Heart rate variability and irregularity were assessed by time domain parameters and entropy-based indices, respectively and their correlation with AFR investigated.

RESULTS

Variability measures seem not to be related to AFR, while irregularity measures do. A significant correlation between AFR and variability parameters of heart rate variability during AF was found only in patients not treated with antiarrhythmics drugs (correlation = 0.56 P < 0.05 for pNN50), while this correlation was lost in patients taking rate- or rhythm-control drugs. A significant positive correlation between AFR and indices of RR irregularity was found, showing that a higher AFR is related to a less organized RR series (correlation = 0.33 P < 0.05 for regularity index for all patients, correlation increased in subgroups of patients treated with the same drug).

CONCLUSIONS

These results suggest that a higher AFR is associated with a higher degree of irregularity of ventricular response that is observed regardless of the use of rate-controlling drugs.

Quantification of anaesthetic effects on atrial fibrillation rate by partial least-squares

The mechanism underlying atrial fibrillation (AF) remains poorly understood. Multiple wandering propagation wavelets drifting through both atria under hierarchical models are not understood. Some pharmacological drugs, known as antiarrhythmics, modify the cardiac ionic currents supporting the fibrillation process within the atria and may modify the AF propagation dynamics terminating the fibrillation process. Other medications, theoretically non-antiarrhythmic, may slightly affect the fibrillation process in non-defined mechanisms. We evaluated whether the most commonly used anaesthetic agent, propofol, affects AF patterns. Partial least-squares (PLS) analysis was performed to reduce significant noise into the main latent variables to find the differences between groups. The final results showed an excellent discrimination between groups with slow atrial activity during the propofol infusion.

Atrial fibrillatory rate and irregularity of ventricular response as predictors of clinical outcome in patients with atrial fibrillation

Atrial fibrillation (AF) remains the most common arrhythmia encountered in clinical practice. Electrocardiogram (ECG)-based predictors of clinical outcome, however, have mostly been studied during sinus rhythm, whereas noninvasive risk stratification of patients with AF remains largely unexplored. Advances in ECG signal processing have led to the development of noninvasive methods of atrial fibrillatory rate (AFR) assessment using spatiotemporal QRST cancellation and time frequency analysis that demonstrated its predictive value for the outcome of pharmacologic and catheter-based interventions for AF. Recently, the prognostic value of AFR was evaluated in patients with congestive heart failure and indicated that reduced AFR may be an independent predictor of total and congestive heart failure-related mortality. A high degree of irregularity of the RR intervals during AF and its dependence on the modulation of the atrioventricular conduction rather than sinus node automaticity hampers the use of conventional heart rate variability approach in patients with AF. However, RR irregularity measures that can be applied to short-time ECG recordings appear to be promising predictors of clinical outcome.

Circadian variation of human ventricular fibrillation dominant frequency

AIM

Circadian variation in human ventricular fibrillation (VF) dominant frequency is unknown. If present this would provide evidence of physiological influence on VF. The objective was to quantify the circadian variation in human VF dominant frequency.

METHODS

Eight-lead Holter ECG recordings were obtained from a patient with severe myocarditis and chronic VF who was supported by a biventricular assist device. Recordings of up to 24h duration were obtained on 6 days with an average interval between recordings of 7 days. Dominant frequency and amplitude were obtained using spectral analysis and assessed for (i) circadian (ii) inter-recording and (iii) inter-lead differences.

RESULTS

There was a significant circadian variation in amplitude (night: 0.027+/-0.004mVHz vs day: 0.044+/-0.006mVHz, p<0.0001) but not dominant frequency (night: 7.85+/-0.62Hz vs day: 7.93+/-0.54Hz, p>0.05). There were significant differences between recordings in dominant frequency which ranged from 6.80+/-0.29Hz to 8.36+/-0.38Hz (p<0.0001) and dominant frequency spectral amplitude which ranged from 0.033+/-0.014mVHz to 0.043+/-0.017mVHz (p<0.0001). Histograms of dominant frequencies in leads exhibited strikingly different distributions, particularly in V2 that was characterised by a bimodal distribution, while the other leads were characterised by predominantly unimodal distributions.

CONCLUSION

VF dominant frequency spectral amplitude exhibited circadian variability. In a patient with severe myocarditis, supported with a biventricular assist device and in chronic VF, these results provide evidence for modulation of VF, probably induced by changes in posture and physical activity.

Electrophysiological characteristics associated with symptoms in pacemaker patients with paroxysmal atrial fibrillation

PURPOSE

The purpose of this study is to identify the electrophysiological factors affecting symptoms in paroxysmal atrial fibrillation (PAF) using patients with paroxysmal atrial fibrillation and pacemakers with advanced atrial fibrillation (AF) diagnostics.

METHODS

Seventy-nine patients (age 71.0 +/- 8.2, 54.4% male) with symptomatic PAF and AF burden of 1% to 50% with DDDRP pacemakers implanted were assessed for 6 months. Patients recorded symptom onset and duration and these were correlated with device-derived electrophysiological data.

RESULTS

Of 2,638 AF episodes, 333 were symptomatic and 2,305 asymptomatic, with 194 non-atrial tachyarrhythmia symptomatic episodes giving a sensitivity of 12.6% and a positive predictive value of 63.2% for specific AF symptoms. Symptomatic AF episodes were 3.8 times more common diurnally than nocturnally (p < 0.001). Diurnally, symptomatic AF was significantly associated with a shorter AF cycle length (CL; p = 0.04), faster ventricular rate (p = 0.004), shorter PR interval (p < 0.001), faster preceding heart rate (p = 0.001) and increased early recurrence of AF (p < 0.04). Nocturnally, a significantly longer AF CL (p = 0.04) and PR interval (p < 0.001) prior to AF onset predicted symptomatic AF.

CONCLUSIONS

Symptoms in PAF are predicted by changes in AF episode duration, ventricular rate during AF, preceding sinus heart rate, AV nodal conduction and AF cycle length but not ventricular irregularity. Excess diurnal sympathetic tone and excess nocturnal vagal tone predispose to symptomatic PAF. These findings may have relevance for therapies for symptom control of PAF.

Analysis of surface atrial signals: time series with missing data?

Uncovering of the atrial signal for patients undergoing episodes of atrial fibrillation is usually obtained from surface ECG by removing waves induced by ventricular activities. Once earned the atrial signal, the detection of the dominant fibrillation frequency is often the main (and only) goal. In this work we verified if subtraction of the ventricular activity might be avoided by performing spectral analysis on those ECG segments where ventricular activity is absent, (i.e. the T-Q intervals). While the approach might seem crude, in here the question was recast into a problem of missing data in a long time series and proper methods were applied: the Lomb periodogram and the iterative Singular Spectrum Analysis. The two methods were tested on both simulated signals and "realistic" atrial signals constructed using the ECG recordings provided by the 2004 Computers in Cardiology competition. The results obtained showed that both techniques were able to provide a reliable quantification of the dominant oscillation, with a slightly superior performance of the iterative Singular Spectrum Analysis. Absolute errors larger than 1.0 Hz were unlikely (p < 0.05) up to 130-140 bpm. Such level of agreement is consistent with similar comparative works where techniques for separating the atrial signal from ventricular waves were considered.

Exercise testing for non-invasive assessment of atrial electrophysiological properties in patients with persistent atrial fibrillation

AIMS

Experimental studies suggest that the autonomic nervous system modulates atrial refractoriness and conduction velocity in atrial fibrillation (AF). These modulatory effects are, however, difficult to assess in the clinical setting. This study sought to non-invasively characterize in patients with persistent AF, the influence of autonomic modulation induced by exercise on atrial fibrillatory rate as marker of atrial refractoriness and to identify clinical and electrocardiographic predictors of atrial rate response.

METHODS AND RESULTS

In 24 patients (16 males, mean age 60 +/- 13 years) with persistent AF (16 +/- 25 months), continuous ECGs were recorded during bicycle exercise testing. Fibrillatory rate (in fibrillations per minute, fpm) was assessed at baseline and immediately after termination of exercise with spatiotemporal QRST cancellation and time-frequency analysis. Ventricular response was characterized by time-domain HRV indices. Exercise had no influence on mean fibrillatory rate (409 +/- 42 vs. 414 +/- 43 fpm, P = NS). Seven patients responded to exercise with an increase in fibrillatory rate (26 +/- 10 fpm, P < 0.001 and three with a decrease (-21 +/- 8 fpm, P < 0.001), while the remaining 14 patients did not show a response. Responders' HRV indices changed in response to exercise similarly to that of non-responders. Their baseline fibrillatory rate was, however, lower than that of non-responders (387 +/- 18 vs. 425 +/- 48 fpm, P = 0.028). No other clinical or echocardiographic variable was associated with fibrillatory rate response. Twelve weeks after cardioverson, responders were more likely to remain in sinus rhythm than non-responders (88 vs. 46 %, P = 0.04).

CONCLUSIONS

Exercise-induced autonomic activation produces changes in atrial electrophysiological properties that can be detected by time-frequency analysis. Higher baseline fibrillatory rates are associated with an impaired atrial response to exercise that suggests advanced electrical remodelling and reduced sensitivity to autonomic stimuli.

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 gamma 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 of 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 gamma 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 discussed.

Analysis of surface electrocardiograms in atrial fibrillation: techniques, research, and clinical applications

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy is sufficiently predictable by clinical and echocardiographic parameters. The purpose of this article is to describe technical aspects of novel electrocardiogram (ECG) analysis techniques and to present research and clinical applications of these methods for characterization of both the fibrillatory process and the ventricular response during AF. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface ECG using digital signal processing (extraction of atrial signals and spectral analysis). This measurement shows large inter-individual variability and correlates well with intra-atrial cycle length, a parameter which appears to have primary importance in AF maintenance and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously and responds better to antiarrhythmic drugs or cardioversion, whereas high-rate AF is more often persistent and refractory to therapy. Ventricular responses during AF can be characterized by a variety of methods, which include analysis of heart rate variability, RR-interval histograms, Lorenz plots, and non-linear dynamics. These methods have all shown a certain degree of usefulness, either in scientific explorations of atrioventricular (AV) nodal function or in selected clinical questions such as predicting response to drugs, cardioversion, or AV nodal modification. The role of the autonomic nervous system for AF sustenance and termination, as well as for ventricular rate responses, can be explored by different ECG analysis methods. In conclusion, non-invasive characterization of atrial fibrillatory activity and ventricular response can be performed from the surface ECG in AF patients. Different signal processing techniques have been suggested for identification of underlying AF pathomechanisms and prediction of therapy efficacy.

Patients with persistent atrial fibrillation taking oral verapamil exhibit a lower atrial frequency on the ECG

BACKGROUND

While there is agreement that verapamil attenuates the AF- induced refractory period shortening when given before AF induction, controversy exists regarding its effects when given after the onset of persistent AF. This study aimed to compare atrial fibrillatory frequency obtained from the surface ECG in patients with persistent atrial fibrillation (AF) with oral verapamil treatment to those without this treatment.

METHODS AND RESULTS

Surface ECG recordings were performed in 57 patients (34 male, 23 female, mean age 59 +/- 11 years) with persistent AF (> 7 days). The frequency content of the fibrillatory baseline was quantified using digital signal processing (filtering, QRST complex averaging and subtraction, Fourier transformation). In 27 patients with verapamil treatment (120 or 240 mg/day for at least 7 days) mean fibrillatory frequency measured 6.4 +/- 0.2 Hz, compared to 7.0 +/- 0.4 Hz (P = 0.012) in 30 patients without verapamil. In a subset of 20 randomly selected patients (10 with, 10 without verapamil treatment) a 24-hour Holter ECG recording was performed and fibrillatory frequency determined at 4 PM, 10 PM, 4 AM, and 10 AM. While there was a significant frequency reduction in the verapamil treated patients at night (P = 0.011), it remained constant over time in the other patients.

CONCLUSION

In patients with persistent AF, fibrillatory frequency assessed by spectral analysis of the surface ECG is lower in patients taking verapamil.

Atrial fibrillation signal organization predicts sinus rhythm maintenance in patients undergoing cardioversion of atrial fibrillation

AIMS

Electrical remodelling is believed to influence the outcome following cardioversion of patients with persistent atrial fibrillation (AF). However, the results in clinical studies are conflicting. We assessed the hypothesis that non-invasively obtained atrial fibrillatory organization can be used as a predictor of sinus rhythm (SR) maintenance.

METHODS AND RESULTS

Fifty-four patients (37 men, age 67+/-11) with persistent AF (median duration 3 months, 1 day to 18 months), without anti-arrhythmic drug treatment, referred for cardioversion were studied. Assessment of the atrial harmonic decay was made by time-frequency analysis of the ECG. At 1-month follow-up, 30 patients had relapsed into AF. The mean harmonic decay at inclusion of those relapsing into AF was 1.5+/-0.3 compared with 1.1+/-0.3 among those maintaining SR (P=0.0004). Using a cut-off value of harmonic decay <or=1.5 to determine suitability for cardioversion would have resulted in a clinically useful discriminator (sensitivity=92%, specificity=47%, PPV=59%, and NPV=88%).

CONCLUSION

This study shows that patients relapsing rapidly to AF have a higher harmonic decay than those maintaining SR. The degree of AF signal organization (harmonic decay) was a superior discriminator to other patient parameters. Further studies are needed to confirm these results and to determine the electrophysiological correlate of harmonic decay.

Indices of Electrical and Contractile Remodeling During Atrial Fibrillation in Man

BACKGROUND

Atrial electrical and contractile remodeling have been demonstrated to coincide during atrial fibrillation (AF) in experimental studies. We explored whether electrical and contractile remodeling correlate in man and explored its clinical implications.

METHODS

Forty-nine patients with persistent AF were studied. Electrical remodeling was assessed noninvasively using spectral analysis to estimate the average fibrillatory rate (AFR). Atrial contractility was assessed by transesophageal echocardiography (TEE) measurement of left atrial appendage outflow velocity (LAAOV).

RESULTS

The AFR was 403+/-43 fibrillations per minute (fpm) and the LAAOV was 0.27+/-0.14 m/s. A significant correlation was found between AFR and LAAOV (r=-0.47, P=0.001). In patients with a LAAOV>or=0.25 m/s, the AFR was 387+/-48 fpm compared to 419+/-31 fpm among patients with LAAOV<0.25 m/s (P<0.01).

CONCLUSIONS

This study demonstrates that indices of electrical and contractile remodeling are strongly correlated in persistent AF in man. The interindividual overlap, however, is too large to allow predictions of LAAOV based on fibrillatory frequency alone.

Rapid fluctuations in atrial fibrillatory electrophysiology detected during controlled respiration

Heart rate during sinus rhythm is modulated through the autonomic nervous system, which generates short-term oscillations. The high-frequency components in these oscillations are associated with respiration, causing sinus arrhythmia, mediated by the parasympathetic nervous system. In this study, we evaluated whether slow, controlled respiration causes cyclic fluctuations in the frequency of the fibrillating atria. Eight patients (four women; median age 63 yr, range 53-68 yr) with chronic atrial fibrillation (AF) and third-degree atrioventricular block treated by permanent pacemaker were studied. ECG was recorded during baseline rest, during 0.125-Hz frequency controlled respiration, and finally during controlled respiration after full vagal blockade. We calculated fibrillatory frequency using frequency analysis of the fibrillatory ECG for overlapping 2.5-s segments; spectral analysis of the resulting frequency trend was performed to determine the spectrum of variations of fibrillatory frequency. Normalized spectral power at respiration frequency increased significantly during controlled respiration from 1.4 (0.76-2.0) (median and range) at baseline to 2.7 (1.2-5.8) (P = 0.01). After vagal blockade, the power at respiration frequency decreased to 1.2 (0.23-2.8) (P = 0.01). Controlled respiration causes cyclic fluctuations in the AF frequency in patients with long-duration AF. This phenomenon seems to be related to parasympathetic modulations of the AF refractory period.

Relationship between pattern of occurrence of atrial fibrillation and surface electrocardiographic fibrillatory wave characteristics

OBJECTIVES

The purpose of this study was to assess whether surface ECG fibrillatory (f)-wave characteristics reflect clinical variables, especially pattern of occurrence.

BACKGROUND

In clinically stable patients, f waves have fairly constant quantitative characteristics. Both electrophysiologic and structural remodeling might modify f waves.

METHODS

We analyzed f waves from 238 patients (120 men and 118 women; age range 30-97 years, mean 77 +/- 12) with atrial fibrillation identified by retrospective chart review as paroxysmal, persistent, or permanent fibrillation. Analysis was performed in the time and frequency domains on ECGs after QRS-T cancellation. Student's t-test and multivariate analysis were used for comparison.

RESULTS

The f waves of 12 patients taking rhythm control drugs had lower frequency ("slower" fibrillation) than the f waves of patients not taking such drugs (5.3 +/- 0.6 vs 6.0 +/- 0.7 Hz, P < .001). Of the 226 remaining patients, 59 were paroxysmal, 30 were persistent, and 72 were permanent; 65 had an unknown pattern. Paroxysmal and persistent patients were younger than permanent (74 +/- 12 and 72 +/- 15 vs 80 +/- 9 years, P < .002 for both). Paroxysmal, persistent, and permanent patients had different f-wave frequencies of 5.7 +/- 0.7, 6.1 +/- 0.8, and 6.2 +/- 0.6 Hz, respectively (P = .01 for paroxysmal vs persistent and P < .001 for paroxysmal vs permanent). Patients older than 77 years (mean age) had lower f wave frequency than those younger 77 years (6.0 +/- 0.7 vs 6.2 +/- 0.7 Hz, P = .01). Using multivariate analysis, the overall pattern-frequency relationship was significant (p = .014). There was a statistically significant inverse correlation between frequency and age (R = .27, slope = -0.017 Hz/year, P < .001).

CONCLUSIONS

ECG f-wave frequency reflects specific clinical variables, with higher frequency in permanent than paroxysmal fibrillation but lower frequency in older than younger patients. These findings are consistent with the idea that fibrillatory waves are modified by both electrophysiologic and structural remodeling.

Analysis of the surface electrocardiogram for monitoring and predicting antiarrhythmic drug effects in atrial fibrillation

Specific antiarrhythmic therapy with class I and III drugs for atrial fibrillation (AF) conversion and prevention of its recurrence is frequently utilized in clinical practice. Besides being only moderate effective, the utilization of antiarrhythmic drugs may be associated with serious side effects. In the clinical setting it is difficult to directly evaluate the effects of antiarrhythmic drugs on the individual patient's atrial electrophysiology, thereby predicting their efficacy in restoring and maintaining sinus rhythm. Analysis of the surface electrocardiogram in terms of P-wave signal averaged ECG during sinus rhythm and spectral characterization of fibrillatory waves during AF for evaluation of atrial antiarrhythmic drug effects is a new field of investigation. Both techniques provide reproducible parameters for characterizing atrial electrical abnormalities and seem to contain prognostic information regarding antiarrhythmic drug efficacy. Further research is needed which elucidates the most challenging clinical questions in AF management whom to place on antiarrhythmic drug treatment and what antiarrhythmic drug to prescribe. Analysis of the surface ECG might have the potential to answer these questions.

Surface ECG vector characteristics of organized and disorganized atrial activity during atrial fibrillation

BACKGROUND

The aim of this study was to examine atrial organization from vectorcardiograms (VCGs) derived from the surface ECG of atrial fibrillatory waves.

METHODS

We retrieved ECGs recorded during ventricular asystole from 22 patients with AF undergoing ablation of the AV junction. The synthesized VCG of each f-wave cycle of each ECG and its plane of best fit, described by azimuth and elevation angles relative to the frontal plane, were computed.

RESULTS

Fifteen of the 22 ECGs had at least 30% of the planes in a single 30-degree region of azimuth angles. Of these 15, 12 had the greatest percentage of planes with azimuth angles within 30 degrees of the sagittal plane; two were near the frontal plane; and one near the right anterior oblique plane.

CONCLUSIONS

Varying degrees of organization were observed from VCGs of fibrillatory waves with the more organized examples having planes predominately near the sagittal plane.

Atrial Fibrillatory Wave Characteristics on Surface Electrogram:

INTRODUCTION

Fibrillatory waves on the surface ECG have been scrutinized to allow inferences about underlying mechanisms and pathophysiology, based on the premise that fibrillatory waves do not vary "randomly" but provide a consistent reflection of the underlying state of the atria in an individual patient. This premise is untested.

METHODS AND RESULTS

Ten standard ECGs were recorded over a 24-hour period in each of 20 clinically stable inpatients with atrial fibrillation. After QRS-T cancellation, the remainder fibrillatory waves were analyzed. Interpatient versus intrapatient differences in fibrillatory wave characteristics were evaluated by analysis of variance (ANOVA). The fibrillatory wave peak-to-peak amplitude of all the patients ranged from 0.06 to 0.35 mV, whereas 1 SD of the amplitude for each patient ranged from 0.004 to 0.053 mV. Short-term peak frequencies of all the patients ranged from 4.6 to 8.0 Hz, whereas 1 SD for each patient varied from 0.2 to 0.5 Hz. For these and all other parameters tested, interpatient differences were significantly greater compared to intrapatient differences (P < 0.0001).

CONCLUSION

Fibrillatory wave characteristics are repeatable from ECG to ECG over 24 hours for clinically stable patients, whereas substantial differences are present between patients. Further study of the relationship of such characteristics to pathophysiology and management decisions is valid and warranted.

Circadian variation of arrhythmia onset patterns in patients with persistent atrial fibrillation

BACKGROUND

The circadian onset patterns and cycle lengths of atrial tachyarrhythmias (AT) were determined in a group of patients with persistent atrial fibrillation.

METHODS

Fifteen patients, mean age 63 +/- 14 years and 80% male, were implanted with the Jewel AF atrial defibrillator (Medtronic, Minneapolis, Minn) for persistent atrial fibrillation only. Onset times of AT and median onset atrial cycle lengths were determined from device memory.

RESULTS

Over a follow-up period of 23.3 +/- 7 months, 227 episodes of persistent AT were treated by patient-activated atrial defibrillation. The peak onset of persistent AT was nocturnal, with 74% of episodes initiating between 8 pm and 8 am. Eighty-seven percent of the patients experienced an additional 403 paroxysmal AT episodes. These episodes showed a "double-peaked" pattern with the least number of episodes occurring between midnight and 8 am. The mean onset atrial cycle length of persistent AT was significantly shorter than the paroxysmal AT episodes (200 +/- 37 ms vs 240 +/- 39 ms, P <.005). The atrial cycle lengths at arrhythmia onset of both paroxysmal and persistent AT episodes also demonstrated circadian variation.

CONCLUSION

There is a circadian distribution of onsets for persistent AT with predominance at night. Patients with persistent AF have >1 type of atrial arrhythmia with differences in the onset patterns and atrial cycle lengths, suggesting different triggers and onset mechanisms.