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Europace Advance Access originally published online on April 30, 2007
Europace 2007 9(8):638-642; doi:10.1093/europace/eum074
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© The European Society of Cardiology 2007. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

ATRIAL FIBRILLATION

Electrocardiographic characteristics of fibrillatory waves in new-onset atrial fibrillation

Daniela Husser1,2, David S. Cannom1, Anil K. Bhandari1, Martin Stridh3, Leif Sörnmo3, S. Bertil Olsson2 and Andreas Bollmann1,2,*

1 Department of Cardiology, Good Samaritan Hospital and Harbor-UCLA Medical Center Los Angeles, CA, USA; 2 Department of Cardiology, Lund University, Lund, Sweden; 3 Department of Electroscience, Lund University, Lund, Sweden

Manuscript submitted 25 January 2007. Accepted after revision 21 March 2007.

* Corresponding author: Department of Cardiology, Otto-von-Guericke-University Magdeburg, Leipziger Street 44, 39120 Magdeburg, Germany Tel: +49 391 67 13203; fax: +49 391 67 13202. E-mail adress: andreas.bollmann{at}medizin.uni-magdeburg.de


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Conclusions
 References
 
Aims In atrial fibrillation (AF), fibrillatory waves of surface electrocardiograms (ECG) vary among patients with respect to waveform and repetition rate. The purpose of this study was to (i) explore clinical determinants of new-onset AF and (ii) determine prognostic significance to predict initial treatment outcome of electrocardiographic fibrillatory wave characteristics in new-onset AF.

Methods and results Twenty-five patients (15 male, mean age 69 ± 16 years) with new-onset AF (median AF duration 8 days) were studied. Fibrillatory rate and exponential decay defined as decay of the curve that connects power maxima of dominant and harmonic frequency components were obtained by spatiotemporal QRST cancellation and time–frequency analysis of the index ECG (before treatment initiation). Baseline AF rate was 380 ± 50 fibrillations per minute (fpm) (range 222–494); patients’ age (ß = – 1.747, P = 0.003) and AF duration (ß = 0.726, P = 0.036) were independently related with fibrillatory rate. AF terminated within 24 h in seven patients, while it was persistent in the other 18 patients. Terminating AF had lower atrial rate (333 ± 66 vs. 398 ± 40 fpm, P = 0.005) and exponential decay (1.03 ± 0.36 vs. 1.40 ± 0.37, P = 0.041) than persisting AF. Multivariate analysis revealed fibrillatory rate to be the only independent predictor of AF termination or persistence (ß = 0.031, P = 0.031). Sensitivity and specificity for predicting AF termination were strongly related to fibrillatory rate (area under the curve = 0.817). Sensitivity and specificity were 89% and 71% for a fibrillatory rate of 355 fpm.

Conclusions Fibrillatory rates vary substantially among patients to new-onset AF and are related to patients' age and AF duration. Lower fibrillatory rates indicate higher chances of spontaneous AF termination within 24 h.

Key Words: Atrial fibrillation, ECG, Antiarrhythmic drugs


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Conclusions
 References
 
It is a common observation that in atrial fibrillation (AF), fibrillatory waves of surface electrocardiograms (ECG) vary among patients with respect to waveform and repetition rate. In order to characterize better the fibrillatory process from the surface ECG, time–frequency analysis has been developed and applied in different AF populations.1Go–4Go In particular, atrial rate as measure of atrial refractoriness5Go and a global measure of AF organization (exponential decay) based on the harmonic structure of the frequency power spectrum6Go can be extracted which has been done so far in patients with paroxysmal or persistent AF. Interestingly, a large inter-individual variability has been observed in both AF forms with higher rates and less AF organization being attributed to advanced atrial remodelling.3Go In accordance, higher fibrillatory rates were associated with AF persistence7Go,8Go or recurrence following cardioversion.9Go,10Go

Whether or not inter-individual differences in waveform parameters are already present in new-onset AF is unknown. Furthermore, clinical variables associated with and prognostic significance for predicting treatment outcome of electrocardiographic fibrillatory wave characteristics in new-onset AF have not been explored. Consequently, in this study, fibrillatory waves in new-onset AF were analysed by time–frequency analysis, and the possible relation between them and both clinical variables as well as outcome of initial treatment was investigated.


   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Conclusions
 References
 
Patients
Twenty-five patients with new-onset AF enrolled at our institution in the FRACTAL registry were studied. FRACTAL (The Fibrillation Registry Assessing Costs, Therapies, Adverse Events and Lifestyle) is a prospective, multicenter registry of patients with AF in the United States and Canada. Enrolment occurred within 3 months of the first electrocardiographically documented episode of AF for each patient. Initial treatment was left to the treating physician's discretion and consisted of either class I or class III anti-arrhythmic drugs or atrioventricular node blocking drugs.11Go

Electrocardiogram acquisition and analysis
Digitally stored 10-s, resting 12-lead ECGs (GE Muse CV, GE Healthcare, Milwaukee, WI, USA) were downloaded from the hospital ECG database for off-line signal processing. The surface ECG was processed using analysis techniques which have been described in detail before.1Go Briefly, after analog-to-digital conversion (500 Hz, 12-bit, 0.05–300 Hz), electrograms were stored on optical disc and transferred to a personal computer. After high-pass filtering to remove baseline wander, atrial fibrillatory activity was extracted using spatiotemporal QRST cancellation (Figure 1, top).12Go Since the dominant frequency component of interest is within the range of 4–9 Hz, the resulting fibrillatory baseline signal was downsampled to 50 Hz and subjected to spectral analysis.


Figure 1
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  		Figure 1 Time–frequency analysis of AF. Top: Resting ECG from a patient with AF, and the same interval after QRST cancellation (top panels, amplitude scale is magnified). Bottom: The fibrillatory signal is then subjected to time–frequency analysis resulting in a power frequency spectrum (left) and a frequency trend (right).

 
One frequency estimate of the atrial signal was obtained every second from overlapping 2.5-s windows by short-term Fourier transform (segment-wise fast Fourier transform). Thus, this instantanenous signal was represented in a spectral profile (Figure 1, bottom left), which was also used to quantify the general morphology of the signal (i.e. exponential decay, see below for definition) as well as for quality control excluding non-reliable signal intervals. Subsequently, the frequency of the atrial signal was trended as a function of time (Figure 1, bottom right). Frequencies were converted to fibrillatory rates with its unit fibrillations per minute (fpm) as advocated previously (rate = frequency x60).3Go Mean fibrillatory rate (in fpm) defined as the average of instantaneous fibrillatory rates over the 10-s ECG segment was determined. Exponential decay was defined as decay of the curve that connects power maxima of dominant and harmonic frequency components in the 4–25 Hz frequency band (Figure 1, bottom left). Higher harmonic frequency components are associated with a smaller exponential decay and indicate more organized rhythms,1Go,2Go which may be attributed to less refractoriness dispersion.4Go

Previously, ECG parameters obtained during AF have been shown to be highly reproducible.13Go

Statistical analysis
All continuous variables are presented as mean ±1 standard deviation. The possible relation between ECG parameters and clinical variables was assessed using correlation analysis for continuous variables (age, AF duration, left atrial diameter, left ventricular ejection fraction) and Student's t-test for unpaired data of categoric variables (gender, underlying heart disease). Clinical, echo-, and electrocardiographic variables were compared between patients in whom AF terminated spontaneously within 24 h after treatment initiation to whose in whom AF persisted. Multivariate analysis were applied to identify independent predictors of both baseline fibrillatory rate and initial treatment outcome. For both analysis, variables with a P-value <0.1 found in univariate analysis were included. A P-value <0.05 was considered statistically significant.


   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Conclusions
 References
 
Patient population and fibrillatory wave characteristics
There were 15 male and 10 female patients with a mean age of 69 ± 16 years (range 17–86). Median AF duration at the time of ECG acquisition was 8 days (range 0–78). Patient characteristics are summarized in Table 1.


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  		Table 1 Patient population

 
Baseline AF rate of the index ECG (before treatment initiation) was 380 ± 50 fpm (range 222–494) and exponential decay was 1.31 ± 0.39 (range 0.43–2.12). Both parameters were highly correlated (R = 0.722, P < 0.001). Ventricular rate measured 100 ± 24 bpm (range 65–158).

Fibrillatory rate correlated with age (R = – 0.580, P = 0.002), AF duration (R = 0.435, P = 0.03), and LVEF (R = – 0.563, P = 0.01), while exponential decay was not associated with any clinical or echocardiographic variable. Using multivariate analysis, age (ß = – 1.747, P = 0.003) and AF duration (ß = 0.726, P = 0.036) were independent predictors of fibrillatory rate (Figure 2).


Figure 2
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  		Figure 2 Relation between fibrillatory rate and patients' characteristics; such as age (top) and AF duration (bottom). Please note that AF duration is expressed on a logarithmic scale.

 
Atrial fibrillation termination and fibrillatory wave characteristics
Patients were treated with AV-nodal blocking (n = 7) or anti-arrhythmic drugs (n = 18: flecainide, n = 4; procainamide, n = 2; propafenone, n = 2; quinidine, n = 1; sotalol, n = 9). Among clinical variables, AF duration was shorter in patients treated for rate-control (5 ± 10 vs. 31 ± 28 days, P = 0.026) and their ventricular rate tended to be slightly higher (114 ± 27 vs. 95 ± 21 bpm, P = 0.087). Those patients had both a lower baseline fibrillatory rate (344 ± 81 vs. 394 ± 36 fpm, P = 0.041) and a lower exponential decay (0.99 ± 0.33 vs. 1.44 ± 0.35, P = 0.009) when compared with patients undergoing anti-arrhythmic drug initiation.

AF was terminated within 24 h after treatment initiation in seven patients, while it was persistent in the other 18 patients. Clinical, echo-, and electrocardiographic characteristics are compared in Table 1.

Multivariate analysis revealed fibrillatory rate to be the only independent predictor for AF termination (ß = 0.031, P = 0.031). No other clinical variable or treatment strategy was associated with the outcome of initial treatment. Sensitivity and specificity for predicting AF termination were strongly related to fibrillatory rate (area under the curve = 0.817). Sensitivity and specificity were 89% and 71% for a fibrillatory rate of 355 fpm (Figure 3).


Figure 3
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  		Figure 3 Relation between fibrillatory rate and outcome of initial treatment. Individual fibrillatory rates stratified by initial treatment (open circle, rate control drugs; closed circle, class I or III anti-arrhythmic drugs). Best predictive accuracy was obtained with a cut-off of 355 fpm (thick line), 100% sensitivity was observed with a cut-off of 330 fpm with a specificity of 57%, and 100% specificity was observed with a cut-off of 415 fpm with a sensitivity of 39% (dashed lines).

 

   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Conclusions
 References
 
Non-invasive methods for analysis of fibrillatory waves of the surface ECG have been advocated as becoming useful in AF treatments.14Go In that sense, this study is the first to apply time–frequency analysis in new-onset AF and several findings deserve special attention.

First, there is a substantial inter-individual variability in fibrillatory rate and exponential decay which allows this technique to be used for the characterization of the fibrillatory process in the individual patient. Interestingly, this variability is not only present in long-lasting, permanent AF,3Go,15Go but as shown in this study, it is already present in the first detected AF episode. This raises the question for possible determinants of this phenomenon, which can only partially be explained by clinical variables alone (see below). Previous studies have also shown that the connexin 40 genotype16Go or gene expression of ion channels17Go have an influence on atrial refractoriness and its dispersion which in turn should also affect ECG parameters.

Second, fibrillatory rate was independently associated with patients' age and AF duration. The former inverse correlation has already been reported in permanent AF.15Go It can be explained by longer refractory periods and slower conduction—both resulting in slower fibrillatory rates—which are found in ageing atria.18Go The positive correlation between AF duration and fibrillatory rate is in agreement with the previous experimental observations that atrial refractoriness shortens during the first days/weeks—which results in higher fibrillatory rates—if the arrhythmia persists.19Go

Third, a more organized AF expressed by a lower fibrillatory rate was clearly associated with higher chances of AF termination which is in alignment with the previous studies on spontaneous7Go,8Go or drug-induced AF termination.20Go–22Go Previous invasive mapping studies23Go have shown that lower fibrillatory rates indicate more organized atrial activiation with fewer wavefronts. Consequently, there is a higher chance that all wavelets will extinguish simultaneously and AF will terminate either spontaneously or after anti-arrhythmic drug administration. Interestingly, the cut-off of 355 fpm for predicting AF terminations is in close agreement with previous investigations using flecainide or ibutilide and assessing fibrillatory rate either from right atrial 22Go or surface ECG recordings.20Go,21Go

Limitations
These novel findings in new-onset AF need to be interpreted in the light of several study limitations. Although new-onset AF was defined according to the current AF management guidelines24Go and stringent study criteria,11Go asymptomatic AF before the index episode cannot be ruled out with certainty which in turn may also affect the determination of AF duration. Moreover, restriction to persisting, new-onset AF may prohibit generalization of the current findings to other types of AF.

The sample size in this study was relatively small. Consequently, only strong associations such as those between fibrillatory rate and age, AF duration, or AF termination may have been detected. According to the registries protocol, treatment was left to the treating physician's discretion. Unexpectedly, there was a trend towards higher conversion rates with rate-control drugs compared with class I or class III anti-arrhythmic drugs. However, patients treated for rate control had a shorter AF duration and a lower baseline fibrillatory rate, both factors found in univariate analysis to be associated with AF termination. Consequently, no definite conclusion can be drawn for certain treatment strategies, especially since different anti-arrhythmic drugs were also used. Nevertheless, a low fibrillatory rate was the only predictor independent of treatment or other variables.

Finally, the prognostic value of fibrillatory rate for predicting AF termination was identified a posteriori which consequently warrants prospective evaluation.


   Conclusions
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusions
References
 
Fibrillatory rates vary substantially among patients with new-onset AF and are related with patients' age and AF duration. Lower fibrillatory rates indicate higher chances of spontaneous AF termination within 24 h. Taken together, this study emphasizes that time–frequency analysis of AF ECGs may be a useful tool for non-invasive exploration of AF pathophysiology and predicting treatment success.


   Acknowledgments
 
ECG conversion has been perfomed by Mr. K. Intze, GE Healthcare Germany and Mr. A.M. Climent, Polytechnic University of Valencia, Spain for which the authors wish to express their appreciation. The authors wish to thank Charles R. McKay, MD, Division of Cardiology, Harbor-UCLA Medical Center for his support and valuable comments.

This study has been performed in and supported in part by the NordForsk network ‘Electrocardiology in Atrial Fibrillation’. A.B. has been supported by the Max Kade Foundation Inc., New York. D.H. has been supported by the German Cardiac Society. D.H. and M.S. are supported by the Volkswagen Foundation, Germany.

Conflict of interest: S.B.O. is a shareholder of AstraZeneca and Pfizer, and is a consultant with AstraZeneca, Boehringeringelheim and Brisrol-Myer Squibb.


   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusions
 References
 
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[2] Stridh M, Sörnmo L, Meurling CJ, Olsson SB. Sequential characterization of atrial tachyarrhythmias based on ECG time–frequency analysis. IEEE Trans Biomed Eng (2004) 51:100–14.[CrossRef][Web of Science][Medline]

[3] Bollmann A, Husser D, Stridh M, Sörnmo L, Majic M, Klein HU, et al. Frequency measures obtained from the surface electrocardiogram in atrial fibrillation research and clinical decision-making. J Cardiovasc Electrophysiol (2003) 14:S154–S161.[CrossRef][Web of Science][Medline]

[4] Husser D, Stridh M, Cannom DS, Bhandari AK, Girsky MJ, Kang S, et al. Validation and clinical application of time-frequency analysis of atrial fibrillation electrocardiograms. J Cardiovasc Electrophysiol (2007) 18:41–6.[CrossRef][Web of Science][Medline]

[5] Capucci A, Biffi M, Boriani G, Ravelli F, Nollo G, Sabbatani P, et al. Dynamic electrophysiological behavior of human atria during paroxysmal atrial fibrillation. Circulation (1995) 92:1193–202.[Abstract/Free Full Text]

[6] Everett TH IV, Moorman JR, Kok LC, Akar JG, Haines DE. Assessment of global atrial fibrillation organization to optimize timing of atrial defibrillation. Circulation (2001) 103:2857–61.[Abstract/Free Full Text]

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[8] Nilsson F, Stridh M, Bollmann A, Sörnmo L. Predicting spontaneous termination of atrial fibrillation using the surface ECG. Med Eng Phys (2006) 28:802–8.[CrossRef][Web of Science][Medline]

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[10] Holmqvist F, Stridh M, Waktare JE, Sörnmo L, Olsson SB, Meurling CJ. Atrial fibrillatory rate and sinus rhythm maintenance in patients undergoing cardioversion of persistent atrial fibrillation. Eur Heart J (2006) 27:2201–7.[Abstract/Free Full Text]

[11] Zimetbaum P, Ho KK, Olshansky B, Hadjis T, Lemery R, Friedman PA, et al. Variation in the utilization of antiarrhythmic drugs in patients with new-onset atrial fibrillation. Am J Cardiol (2003) 91:81–3.[Web of Science][Medline]

[12] Stridh M, Sörnmo L. Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation. IEEE Trans Biomed Eng (2001) 48:105–11.[CrossRef][Web of Science][Medline]

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[16] Firouzi M, Ramanna H, Kok B, Jongsma HJ, Koeleman BP, Doevendans PA, et al. Association of human connexin40 gene polymorphisms with atrial vulnerability as a risk factor for idiopathic atrial fibrillation. Circ Res (2004) 95:e29–33.[Abstract/Free Full Text]

[17] Brundel BJJM, van Gelder IC, Henning RH, Tieleman RG, Tuinenburg AE, Wietses M, et al. Ion channel remodeling is related to intraoperative atrial effective refractory periods in patients with paroxysmal and persistent atrial fibrillation. Circulation (2001) 103:684–90.[Abstract/Free Full Text]

[18] Sakabe K, Fukuda N, Soeki T, Shinohara H, Tamura Y, Wakatsuki T, et al. Relation of age and sex to atrial electrophysiological properties in patients with no history of atrial fibrillation. Pacing Clin Electrophysiol (2003) 26:1238–44.[CrossRef][Medline]

[19] Wijffels MC, Kirchhof CJ, Dorland R, Allessie MA. Atrial fibrillation begets atrial fibrillation. A study in awake chronically instrumented goats. Circulation (1995) 92:1954–68.[Abstract/Free Full Text]

[20] Bollmann A, Kanuru NK, McTeague KK, Walter PF, DeLurgio DB, Langberg JJ. Frequency analysis of human atrial fibrillation using the surface electrocardiogram and its response to ibutilide. Am J Cardiol (1998) 81:1439–45.[CrossRef][Web of Science][Medline]

[21] Bollmann A, Binias KH, Toepffer I, Molling J, Geller C, Klein HU. Importance of left atrial diameter and atrial fibrillatory frequency for conversion of persistent atrial fibrillation with oral flecainide. Am J Cardiol (2002) 90:1011–14.[CrossRef][Web of Science][Medline]

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