Non-Invasive Localization of Maximal Frequency...

DOI: 10.1161/CIRCEP.112.000167

1

Non-Invasive Localization of Maximal Frequency Sites of Atrial Fibrillation

by Body Surface Potential Mapping

Running title: Guillem et al.; Maximal DF sites of AF identified by BSPM

Maria S. Guillem, PhD1; Andreu M. Climent, PhD1,2; Jose Millet, PhD1;

Angel Arenal, MD, PhD2; Francisco Fernandez-Aviles, MD, PhD2; José Jalife, MD3;

Felipe Atienza, MD, PhD2*; Omer Berenfeld, PhD3,4*

1Universitat Politecnica de Valencia, Valencia; 2Hospital General Universitario Gregorio Marañón, Madrid, Spain; 3Dept of Internal Medicine, Center for Arrhythmia Research, 4Dept of

Biomedical Engineering, University of Michigan, Ann Arbor, MI *shared senior authorship

Corresponding Author:

Dr. María Guillem

BIO-ITACA, Universitat Politècnica de València

Camino de Vera s/n

46022 Valencia

Spain

Tel: (+34) 963877968

Fax: (+34) 963877279.

E-mail: [email protected]

Journal Subject Codes: [5] Arrhythmias, clinical electrophysiology, drugs; [22] Ablation/ICD/surgery; [171] Electrocardiology

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DOI: 10.1161/CIRCEP.112.000167

2

Abstract:

Background - Ablation of high frequency sources in patients with atrial fibrillation (AF) is an

effective therapy to restore sinus rhythm. However, this strategy may be ineffective in patients

without a significant dominant frequency (DF) gradient. The aim of this paper was to investigate

whether sites with high frequency activity in human atrial fibrillation (AF) can be identified

noninvasively, which should help intervention planning and therapy.

Methods and Results - In 14 patients with a history of AF, 67-lead body surface recordings were

simultaneously registered with 15 endocardial electrograms from both atria including the highest

DF site, which was pre-determined by atrial-wide real-time frequency electroanatomical

mapping. Power spectra of surface leads and the body-surface location of the highest DF site

were compared with intracardiac information. Highest DFs found on specific sites of the torso

showed a significant correlation with DFs found in the nearest

The spatial distribution

of power on the surface showed an inverse relationship between the frequencies vs. the power

spread area, consistent with localized fast sources as the AF mechanism with fibrillatory

conduction elsewhere.

Conclusions - Spectral analysis of body surface recordings during AF allows a noninvasive

characterization of the global distribution of the atrial DFs and the identification of the atrium

with the highest frequency, opening the possibility for improved noninvasive personalized

diagnosis and treatment.

Key words: atrial fibrillation, catheter ablation, body surface potential mapping, Fourier analysis.

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Introduction

It has been postulated that atrial fibrillation (AF) in humans is maintained by periodic electrical

sources that activate the atria at exceedingly high frequencies and result in apparently random

and uncoordinated electrical activation patterns (1,2). Intracardiac dominant frequency (DF)

mapping allows the identification of such high-frequency sites in the atria as a guide to

effectively terminate AF by radiofrequency (RF) ablation in a significant number of patients (3).

Although the high-frequency sources are usually located near the pulmonary veins (PVs) (1),

they can also be found anywhere in the atria (3, 4, 5, 6) as is frequently observed in persistent AF

patients (3, 7, 8). Regardless of specific location, atrial activation by high-frequency sources

results in DF gradients whose eradication by RF ablation predicts long-term freedom of AF in

both paroxysmal and persistent AF patients (3). When targeting high-frequency sites, recurrences

of AF are more common in patients without a significant DF gradient at baseline (3). The aim of

this proof-of-concept study was to determine whether body surface potential mapping (BSPM)

can be used to identify high-frequency sources noninvasively during AF. The results suggest that

body surface DF maps obtained prior to the intervention may allow identification of the AF

patients who are most suitable for ablation and be an aid in the planning of the ablation

procedure.

Materials and Methods

Patients

We included patients admitted for ablation of drug-refractory paroxysmal and persistent AF, as

approved by the Institutional Ethics Committee of our institution. All patients gave informed

consent.

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BSPM Recording Protocol

During the entire ablation procedure, patients wore a custom-made, adjustable vest with 67

electrodes covering the entire torso surface (Figure 1). The vest included recording electrodes on

the anterior (N=28), posterior (N=34) and lateral sides (N=2) of the torso. In addition, three limb

leads were recorded and used to obtain the Wilson Terminal. The BSPM vest was placed prior to

the catheterization and fastened anteriorly, allowing access to the patient chest in case external

electrical cardioversion was needed during the course of the study. Surface electrocardiographic

recordings were obtained by using a commercial system for biopotential measurements (Active

Two, Biosemi, The Netherlands) at a sampling frequency of 2048 Hz and stored on hard disk for

off-line analyses (9).

Electrophysiological Study and Intracardiac Spectral Mapping of AF

The electrophysiologic study was performed under general anesthesia and periodic heparin bolus

administrations. The following catheters were introduced via the right femoral vein: 1) a standard

tetrapolar catheter in the right atrial (RA) appendage; 2) a deflectable 4 mm mapping catheter

(Marinr, Medtronic Inc., Minneapolis, USA) in the distal coronary sinus (CS); 3) a decapolar

circular mapping Lasso catheter (Biosense-Webster, Diamond Bar, CA, USA) used to map the

PV-left atrial junctions (PV-LAJ); and 4) a Navistar catheter (3.5 mm tip, 2-5-2 interelectrode

distance, Thermo-Cool, Biosense-Webster, Diamond Bar, CA, USA).

In patients arriving in sinus rhythm, AF was induced by burst pacing. AF episode durations

longer than 5 minutes were required for inclusion in the study. The 3-D geometry of both atrial

chambers and CS was obtained and real-time DF determination of all mapped chambers was

performed using the CARTO navigation system with embedded spectral analysis capabilities

(CARTO XP, version 7.7, Biosense Webster, Diamond Bar, CA) (3, 10). Color-coded DF maps

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during ongoing AF were obtained and superimposed on the atrial shell geometry as previously

described (3, 10).

Once the highest intracardiac DF site was identified, the navigation catheter was placed at this

site and the Lasso catheter was located at the contralateral PV-LAJ. Then a central venous bolus

of adenosine (12-18 mg) was administered to produce significant transient atrioventricular (AV)

block (10). During adenosine infusion, all intracardiac electrograms (EGMs) (at highest DF site,

contralateral PV-LAJ, CS and RA) were simultaneously recorded together with the 67 surface

ECG recordings. A 4-seconds segment surrounding the longest RR interval was used for

comparison of the 15 intracardiac signals and the 67 body surface recordings. In 3 cases, pauses

were shorter than 4 seconds and QRST complexes were cancelled (11).

Signal Analyses

Intracardiac EGM signals from all catheters (i.e. RA, CS, PV-LAJ and ablation catheter) were

processed using Matlab 7.10.0 (The Mathworks Inc, Natick, Massachusetts). Signals were band-

pass filtered between 0.7 and 200 Hz. In order to detect the dominant frequency (DF) of each

recording, signals were processed as previously described (3, 8, 10, 12). Briefly, signals were

band-pass filtered (40-250 Hz), rectified and then low-pass filtered (20 Hz). Welch periodogram

(2 seconds Hamming window with a 3908 point Fast Fourier transform (FFT) per window and

50% overlap) was used to estimate power spectral density. DF was determined as the frequency

with the largest peak in the spectrum. As previously described, spurious DFs were excluded or

corrected, based on the following criteria: 1) poor signal-to-noise ratio; 2) presence of significant

ventricular activity; and 3) erroneous determinations of 2nd or 3rd harmonic peaks as the DF (3).

For signal analyses of body surface recordings, ECGs were processed using Matlab 7.10.0. First,

baseline was estimated and subtracted from the original recording. To estimate the baseline,

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ECG signals were decimated to 51.2 Hz and filtered with a Butterworth 10th order low pass filter

with a cut-off frequency of 2Hz. Baseline was then interpolated to 2048 Hz and subtracted to the

original signal (9).Leads presenting more than 0.5% of their spectral content at 50Hz were

filtered with a 2nd order infinite impulse response notch filter centered at 50 Hz. Then, ECG

signals were low pass-filtered with a 10th order Butterworth filter with a cut-off frequency of 30

Hz. All leads were visually inspected after filtering and leads with noticeable noise were

excluded from further analysis. Power spectral density of all signals free of ventricular activity

was computed using Welch periodogram (2 seconds Hamming window with a 4096 point FFT

per window and 50% overlap) to determine the local DF of the AF and their distribution on the

body surface.

Body Surface DF Maps

To construct body surface DF maps for visual display of data, recorded ECG signals properly

arranged in space were used to estimate ECG signals at non-recorded positions by using cubic

spline interpolation (9). Estimated ECG signals were processed and power spectra and DFs were

computed. Two types of color-coded maps were constructed: 1) DF maps obtained by

superposition of the DF data obtained from surface ECGs and 2) DF difference maps at which

the difference between the surface DFs and maximal intracardiac DF was displayed.

Body surface DF maps were also used to estimate the relationship between activation

frequencies and power spread area. Specifically, the power spectrum of each lead was

normalized to its highest value and the normalized spectra were used to display the total spectral

content at each frequency band and spatial location.

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Results

Patients and Body Surface Mapping Protocol

Fourteen patients undergoing ablation for drug-refractory AF (age: 56±8 years; 93% male; 10

paroxysmal, 4 persistent) were included in the study. Upon arrival to the electrophysiology

laboratory, 10 patients were in sinus rhythm and AF was induced by incremental burst pacing.

Adenosine infusion produced AV block longer than 4 seconds in 11 patients (7.5±2.6 seconds)

and significant bradycardia in the other three patients, each with 2, 3 and 4 QRST complexes

within the 4-second window studied. During adenosine infusion, 12 patients (9 paroxysmal, 3

persistent) presented a DF gradient > 0.5 Hz in the recorded EGMs (see online supplement

Table).

Noninvasive Identification of Atrial DFs during AF

Surface leads and simultaneously recorded intracardiac signals presented closely related spectral

components. In Figures 2-4, we present spectral analyses of data from three representative

patients with varying distribution patterns of activation frequency. The presence of distinct atrial

sites with high-frequency activity could be determined noninvasively, as demonstrated in the

example shown in Figures 2 and 3. In Figure 2A, three representative EGMs illustrate the range

of activation rates across the atria, with DFs ranging from 5.75 Hz in the RA to 7 Hz in the LA

near the left superior pulmonary vein (LSPV). Simultaneously recorded surface leads also

showed different DFs at nearly the same frequency range found in the EGM recordings, as

shown in Figure 2B by three representative leads corresponding to the surface left (SL), surface

posterior (SP) and surface right (SR) regions. Surface DFs mostly correlated with the activation

rate of nearest atrial tissue: the highest endocardial DF point was observed at the nearest point on

the torso surface (central posterior) and the lowest activation rate of the RA was found at the

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nearest portion of the surface ECG (right inferior). Intracardiac CARTO DF maps obtained prior

to adenosine infusion and surface DF distributions obtained during adenosine infusion showed

good correspondence (Figure 2C and 2D), but a direct comparison of frequencies from CARTO

and surface maps was unattainable due to its sequential nature and because adenosine accelerates

AF activation in humans.

In a patient with a right-to-left frequency pattern, DFs of intracardiac recordings were again

present in the spectra of simultaneously recorded surface ECGs (Figure 3A and 3B). Activation

of the RA was at 12.75 Hz and a similar frequency (13.25 Hz) could be found on the right torso.

For this patient, an intracardiac DF map acquired prior to adenosine infusion showed a right-to-

left DF distribution where the highest DF point was located on the posterolateral right atrial wall

(Figure 3C). The surface DF map also showed a right-to-left DF gradient with the highest DF on

the right surface (Figure 3D).

Figure 4C shows an intracardiac DF map of a patient with persistent AF where frequencies

within a 5.25 - 6.25 Hz range were distributed widely across both atria such that a clear DF

gradient could not be found. The body surface map showed very similar spectra profiles in all

leads (Figure 4B). In this patient, therefore, a DF gradient could not be found either by the

intracardiac recordings or by the surface ECGs (Figure 4D).

Previous studies have highlighted the major role of maximal DF (DFmax) sources in the

maintenance of AF in animals and humans (1-8, 10, 13). Arguably, prior knowledge of which

atrium harbors the DFmax source that maintains the arrhythmia may allow better planning of the

ablation procedure, and may accelerate the intracardiac localization of the highest DF point and

reduce ablation time. Thus we explored the ability of the body surface DFs to capture the

intracardiac DFmax.

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In panels A and B of Figure 5, we display difference maps obtained by subtracting DF values on

the surface map from the intracardiac DFmax in patients from Figures 2 and 3, respectively. In

each case, the white arrow points to the red color DFmax domain on the surface, which

corresponds to the region with zero difference with the intracardiac DFmax. These examples

demonstrate that the surface DFmax can accurately detect the value of the intracardiac DFmax

value, regardless of whether the latter is localized to the LA or the RA (white arrows in panels A

and B, respectively). To further analyze the correspondence between intracardiac and surface AF

frequencies in the RA and LA we grouped the intracardiac DFmax values measured in the LA

and RA and correlated them with the DFs of EGMs from matching portions on the body surface.

These matching portions were defined as those in which the difference between the intracardiac

DFmax in each atrium and the surface DFmax was . Panels C and D of Figure 5 show

that when the entire patient population under study was included, there was a good

correspondence between maximum DFs found in each atrial chamber and right and left matching

portions of the body surface, respectively. The correlation between right surface leads and right

intracardiac EGMs was 0.96, whereas correlation for left leads with left intracardiac EGMs was

0.92 (Figure 5E and 5F). The attempted correlation of surface leads with EGMs of the opposed

atrial chamber yielded much lower correlation values (RA body surface leads vs intracardiac LA

EGMs, 0.26; one tail t-test after Fisher r-to-z transform: p<0.0001 vs. r=0.96; left body surface

leads vs intracardiac RA EGMs, 0.46; p=0.0052 vs. r=0.92). Such a strong, side-specific

correspondence of frequencies enabled reliable determination of atrial frequency gradients non-

invasively. In Figure 6 we have plotted LA-to-RA DF gradients on the body surface versus those

measured by the intracardiac EGMs in each individual patient. As shown in panel A, the DF

gradients on the BSPM showed a good correlation with the DF gradients obtained by the

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simultaneous intracardiac EGMs (correlation coefficient = 0.93). On the other hand, in panel B,

surface frequency gradients estimated by the standard ECG leads yielded a poor correspondence

with gradients estimated from the intracardiac EGMs, with a correlation coefficient equal to 0.41

when considering all standard leads, or equal to 0.51 when considering only V1 and V6

(p=0.0051 when compared with the correlation of the full BSPM DF gradients).

To predict the ability of the BSPM to detect intracardiac LA-to-RA DF gradients, we classified

patients with a LA-RA gradient in DF (maximal right and left values differ by more than 0.5 Hz)

versus without LA-RA gradient, otherwise. Overall, the sensitivity of the BSPM to capture

intracardiac EGMs as having LA-RA gradient in DF was 75%, but the specificity of the BSPM

in capturing those gradients was 100%. The sensitivity and specificity of the standard ECG leads

V1 and V6 in detecting a LA-to-RA DF gradient were 67% and 50% respectively. In other

words, while the BSPM was able to identify most, but not all, intracardiac DF gradients, each DF

gradient identified by the BSPM was a true gradient as determined by the intracardiac

recordings. Standard ECG leads, however, allowed the detection of a lower proportion of

patients with LA-to-RA DF gradients as compared to all BSPM leads and also mistakenly

identified LA-RA DF gradients that were not confirmed in the EGM recordings.

These results lead to the conclusion that, unlike high-resolution BSPM, the standard precordial

ECG leads alone are not a useful tool when attempting to localize the site of DFmax sites

responsible for AF maintenance.

Interpretation of Surface DF Maps

The foregoing suggests that BSPM reflects intracardiac DFs of nearest atrial tissue and is able to

capture the regional differences in atrial activation rates. However, it should be noted that the

spatial distribution in DF values on the surface is not continuous and abrupt step-wise transitions

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between adjacent domains with different frequencies are the norm (i.e. Figure 3D). Optical

mapping experiments in sheep heart preparations (14; 15) found that the step-wise transition in

DFs is a result of continuous but opposing changes in power levels at the spatially adjacent DFs;

that is, as the power of a DF in a given domain decreases toward a domain border, the power

under a different frequency grows gradually and eventually becomes dominant. We tested

whether similar transitions in spectral power occur at the level of the BSPM.

In Figures 7A and 7B we illustrate power distribution of three spectral components (7 Hz, 8 Hz

and 13.25 Hz) for the patient analyzed in Figure 3. Panel A shows the power spectra of each of

three sample leads on the right anterior torso (a,b and c) that displayed significant power at 7-8

Hz and 13.25 Hz. From Figure 3 we learned that the power at any given spectral component was

highest for the surface leads that were closest to its intracardiac origin. Figure 7B shows that the

power under the normalized peaks at 7, 8 and 13.25 Hz changes gradually from lead a through c.

Please note that each surface electrode contains frequency components related to the activation

frequency of many atrial regions and not only the closest atrial tissue, which is evident by the

significant peaks on the power spectra of leads a-c in Figure 7A. Abrupt spectral changes

between two neighboring electrodes of the surface DF map displaying different frequency

components can nevertheless be explained as a change in the power under the main DF peak of

each electrode.

Finally, the highest frequency components were typically found occupying small DF domains of

the body surface. In fact, there was -0.92) between the frequency of the

highest DF and the area at which that frequency presented at least 50% of the power of the

highest peak of the spectrum (Figure 7C). The power of high spectral components also showed

higher spatial variability than that of low spectral components, as depicted in Figure 7D, with a

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correlation coefficient equal to 0.66.

Discussion

The main finding of the present study is that spatial gradients of activation frequency in human

AF can be detected and quantified non-invasively by constructing DF maps on the body surface.

Although most surface leads predominantly reflect the rate of activation of the overall atrial

tissue, high-frequency sources can also be reflected on the surface electrodes closest to the atria

harboring the highest DF as measured by intracardiac electrograms. Our results also demonstrate

that the portion of the torso at which these high frequency components are dominant relates

inversely with the frequency and thus the highest DF is reflected in a small area of the body

surface. Estimation of DFs from the limited number of precordial surface leads (i.e. the standard

ECG), therefore, does not reproducibly allow detection of highest DF sites.

Frequency Mapping in Human AF

Real-time DF mapping in AF is an effective guidance tool for AF ablation. High frequency sites

within the atria have been associated with driving sources responsible for the maintenance of AF

(16). Sanders et al. (8) demonstrated that in most cases arrhythmia termination was associated

with ablation of high-DF sites. Atienza et al. (3) also showed that patients with LA-to-RA

frequency gradients benefited from RF ablation with improved long-term outcome when such

gradients were eliminated, whereas success rate was significantly lower in patients without left-

to-right DF gradient prior to ablation. Those results were consistent with the existence of high

frequency drivers. Therefore, it seems reasonable to suggest that their identification prior to the

ablation procedure may be crucial for delivering a successful therapy for AF. Although the

highest frequency sources are most commonly located in the junction of the left atrium with the

pulmonary veins (1), they have also been identified elsewhere in the atria (3, 4, 5, 6, 8).

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Atrial Frequency Estimation from the Surface ECG

Previous studies have demonstrated that the surface ECG can be used as an estimator of the atrial

electrical activity. In fact, several studies have shown that spectral features of surface ECG

recordings are highly correlated with intracardiac recordings (17, 18) and have been be used to

monitor the response to antiarrhythmic drugs (19), to predict patient outcome after cardioversion

(20) and to assess the global effect of different strategies of AF ablation (21). The general

acceptance of the existence of high-frequency sources responsible for the maintenance of AF

motivated more recent studies to investigate whether distinct frequencies present in right and left

atria could be identified on surface ECG recordings. Dibs et al. (22) studied the reflection of

atrial frequencies obtained sequentially from multiple sites in both atria and the standard ECG.

Although they reported a good correspondence between mean RA frequencies with lead V2 and

LA frequencies with lead V6, extrapolation of these results is hampered by the little differences

found between mean frequencies from RA and LA. Petrutiu et al. (23) also reported a good

correspondence between measurements from RA with lead V1 and LA with a posterior lead, but

they had access to just one recording site in each atrium reflecting, most likely, the mean

activation rate of each chamber. None of the above studies answered the question of whether

high frequency activity, that may involve the activation of small portions of atrial tissue, can be

found on the surface ECG. Finally, although the inverse ECG imaging technique also utilizes a

multitude of surface recordings and has been used to characterize some AF waves (24), to our

knowledge, it has not been used for frequency characterization to date.

Some of the limitations of the above mentioned studies have been overcome in the present study

by 1) performing real-time frequency mapping that allowed us to use an intracardiac catheter to

locate the highest DF site in each patient simultaneously with catheters at other locations with

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lower frequencies, 2) recording the surface ECG at multiple locations on the body surface that

helped the detection of the high frequency components that were dominant at only a small

number of surface electrodes, 3) infusion of adenosine to transiently increase AF frequency and

accentuate DF gradients (14) while at the same time slow AV conduction, which allowed us to

obtain spectra free of ventricular components.

BSPM and mechanisms of maintenance of atrial fibrillation

The observations of spectral power dispersion shown in Figure 7 are consistent with

experimental DF data on fibrillation and fibrillatory conduction in isolated sheep heart

preparations: in wedge preparations of ventricular walls, Zaitsev et al. (14) showed that the

spatial variability in DF values during fibrillation increases in proportion to the value of the

maximal DF. In isolated RA preparation Berenfeld et al. (25) demonstrated that when the atria

are paced at increasing rates there exists a critical rate above which fibrillatory conduction

emerges the moment at which the normally uniform DF response breaks-down into multiple,

spatially distributed DF domains whose frequency is lower than the pacing frequency. Also

importantly, in whole isolated sheep experiments of AF, the LA was found to have not only

higher DFs than the RA, but also the largest DF domain areas (15), suggesting that atrial sources

for the highest DF could be sufficiently large to be detected on the body surface. Thus, as

evidenced by the cumulative data in Figure 7, during AF in humans the DF distribution on the

BSPM replicates the epicardial distribution of DFs and can identify small areas containing the

high-frequency sources that result in fibrillatory conduction to the reminder of the atria.

Study Limitations

Real-time intracardiac frequency mapping was performed sequentially and prior to the adenosine

infusion, which did not allow a direct comparison of CARTO DF maps and body surface DF

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maps because we could not rule out temporal alteration in the AF characteristics. However,

overall distributions of DFs calculated from 15 simultaneous endocardial EGMs (i.e. location of

the highest DF site) were comparable and showed highly significant correlation.

Most patients enrolled in our study (8 out of 14) had up to 3 previous ablation procedures (see

online supplemental Table). Therefore, the heterogeneously modified substrate in the atria of our

patients did not allow us to compare our results to other studies in the literature regarding the

presence of DF gradients in paroxysmal and persistent AF, or in terms of the outcome of their

respective ablation procedure.

Conclusion and Clinical Implications

Global atrial non-invasive frequency analysis is feasible in AF patients and may allow the

identification of high frequency sources prior to the arrival at the electrophysiology laboratory

for ablation. This mapping procedure may help in patient selection since patients without inter-

chamber DF gradient have lower ablation success rates. Additionally, a priori knowledge of the

chamber responsible of the maintenance of the arrhythmia in those patients presenting a DF

gradient may help in planning and performing the ablation procedure, decreasing the time

required for the search and elimination of the highest DF site.

Funding Sources: Supported in part by: the Spanish Society of Cardiology (Becas Investigación Clínica 2009); the Spanish Ministry of Education and Science under TEC2009-13939; the Universitat Politècnica de València through its research initiative program; the Generalitat Valenciana Grants (AP-145/10, PROMETEO/2010/093 and BEST/2011); the Ministerio de Economia y Competitividad, Red RECAVA; the Centro Nacional de Investigaciones Cardiovasculares (proyecto CNIC-13); the Coulter Foundation from the Biomedical Engineering Department (University of Michigan); the Gelman Award from the Cardiovascular Division (University of Michigan); the National Heart, Lung, and Blood Institute grants (P01-HL039707 and P01-HL087226), and the Leducq Foundation.

n and Clinical Implications

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Conflict of Interest Disclosures: FA served on the advisory board of Medtronic and has received research funding from St. Jude Medical Spain. JJ is a consultant for Topera, Inc., and for Rhythm Solutions, Inc. He is a co-investigator in a research grant from Gilead, Inc. OB is a co-founder and chief scientist of Rhythm Solutions, Inc. None of the companies disclosed financed the research described in this manuscript.

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p yyyy

recillaaaa EEEEG,G,G,G, AAAArerererenanananal l l l AA,ernández Avilés F Berenfeld O Real-time dominant frequency mapping and abn im

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e , , Mohanty , Sanchez J, Mohanty , Horton , Gallinghouse G

ernánnndededdezzzz-AvAAA illlléséséés F, Berenfeld O. Real-timmmme ee dominant frequuuueneneency mapping and abnt frfrfrf eeequenccccyy yy siiitttet s in atrial fibrillation withhh llleft-to-righttt ffffrequency gradients predimmmmaiiiintenance oooof siiinununun s s ss rhrhrhrhytytytythmhmhmhm. HeHeHeaaart RhRhR ythmhmhm. 202020200909099;6;;; :3:33-33-4040400.

Jaaaaïsïsïsïs PPP,,, KeKeeKeananananeeee DDDD,,,, WhWhWhWhartototoon nn JMJMJMJM, DeDeDeDeisisisisenenennhohohohofefefeferrrr I,I,I,I, HHHHocococo ininini i M,M,M,M, SSSShahaahah hh DCDCDCDC,,, SaSaSaS ndndndderererersss P,P,P,P, SSSooriyyyya aa R,,, CCClélélémeeentntnty y yy J,,, Haïaïaïssaggguerre MMM. AtAtAAtriririal fibibrirr llation orororigggginatttatinining gg frff om ppppersistor veeenananaa cccavavavavaaa... CiCiCiC rcrcrcrculuulatatata ioioioi nnnn. 2020202 044044;1;1090909::::82888 888-8-8-83232322....

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J. Lefeffftttt-totot -r-rigiigi hthththt gggraraaadidididieeart CiCiCiCircrcrcrcululululatatatatioioioionnnn

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edominant excitation fr uencies reveal hidden or nization in atrial fibrillation ff perf sed sheep heart J C di El t h i l 2000;11:869 879

2633331-1-1--262626263636363 .

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eld OOO,O MMMMandadadad papapap titiii RRRR, DiDiDiD iixit t SSS,S SSSSkkkak nes ACACACC, ChChChC en JJJJ, MaMMM nsssour MM,MM JJJ lalliffffe JJJ. SSSpapapaatititiiaalaa lyyy dominantntntn eeeexcxcxcxcitititatatatatiooioion nnn frffrfreqeqeqqueueueuencncncncieiieiessss rerererevevevevealallal hhhididididdeddd n n n orororrgagagaganinninizazazaatitittionononon iiin n n n atatatatriiririalalall ffffibibibibrillation ffffff pe ffrf s dded shhheep hhhea ttrt JJJ CCC dddiii EEElll tt hhh iii lll 202020000000 11;1111:868686999 878787999


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25. Berenfeld O, Zaitsev AV, Mironov SF, Pertsov AM, Jalife J. Frequency-dependent breakdown of wave propagation into fibrillatory conduction across the pectinate muscle network in the isolated sheep right atrium. Circ Res. 2002;90:1173-1180.

Figure Legends:

Figure 1. The setup of the body surface recording electrodes. (A) Anterior and posterior views

of the custom-made vest with recording electrodes along vertical blue strips. (B) Schematic

location of surface electrodes. Circles represent the location of recording electrodes. Electrodes

representing the standard ECG precordial leads are denoted as black circles. (C) X-Ray image

displaying the locations of the intracardiac recording catheters together with the surface leads.

Abl: ablation catheter; PV: Circular mapping at the ridght superior pulmonary vein; RA: right

atrium; CS: coronary sinus.

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DOI: 10.1161/CIRCEP.112.000167

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Figure 2. Recorded EGMs and ECGs and their DF distribution in a sample patient with a left-to-

right DF gradient. (A) Three examples of EGMs recorded at different atrial sites and their

corresponding power spectra. (B) Selected surface BSPM leads: Surface Left (SL), Surface

Posterior (SP) and Surface Right (SR), and their corresponding power spectra. (C) Intracardiac

DF map. Black arrow points to the LA region with highest DF at the LSPV. (D) DF map on the

torso surface with superimposed locations of electrodes from (B).

Figure 3. Recorded EGMs and ECGs and their DF distribution in a sample patient with a right-

to-left DF gradient. (A) EGMs recorded at different atrial sites and their corresponding power

spectra. (B) Selected surface BSPM leads and their corresponding power spectra. (C)

Intracardiac DF map. Black arrow points to the RA region with highest DF at the RA. (D) DF

map on the torso surface with superimposed locations of electrodes from (B).

Figure 4. Recorded EGMs and ECGs and their DF distribution in a sample patient without a

significant left-right DF gradient. (A) EGMs recorded at different atrial sites and their

corresponding power spectra. (B) Selected surface BSPM leads and their corresponding power

spectra. (C) Intracardiac DF map. Black arrows point to the various regions with high DF. (D)

DF map on the torso surface with superimposed locations of electrodes from (B).

Figure 5. Correspondence between intracardiac and surface DFs. (A,B) Differences between

maximum intracardiac EGM DF and local surface DF represented in a color scale for two

different patients. The red color domain on the surface (white arrows) represents the region with

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DOI: 10.1161/CIRCEP.112.000167

20

zero difference between the intracardiac and surface DFs. (A) Patient from Figure 2 with a left-

to-right DF gradient. (B) Patient from Figure 3 with right-to-left DF gradient. (C,D) Summary

maps showing the percent patients with the surface DFs less than 0.5 Hz different than the

maximal left (C) and maximal right (D) intracardiac DFs. Areas outlined by the dashed curves

represent the portion of the torso with a best correspondence with left and right EGMs. (E)

Correlation plot showing highest DFs found in left EGMs vs. highest DFs found on the left

portion of the torso. (F) Correlation plot showing highest DFs found in right EGMs vs. highest

DFs found on the right portion of the torso.

Figure 6. Regional differences (gradients) of intracardiac DF and their reflection on the body

surface. (A) Frequency gradients calculated as highest left DF minus highest right DF. BSPM

gradients are plotted against those from the EGMs. (B) Frequency gradients from the standard

ECG are plotted against those from the EGMs . For the standard ECG’s DF gradient, highest left

frequency was selected among aVL, V4, V5 and V6 leads, and highest right frequency was

selected among aVR, V1 and V2.

Figure 7. Spatial representation of the spectral power on the surface and characteristics of spatial

distribution of power at highest surface DF. (A) Power spectra of three surface leads in sites

labeled a, b and c. (B) Maps of the normalized spectral content at three discrete frequencies.

Yellow dashed contours delineate the 50% maximal power borders. (C) Percentage area

presenting >50% maximal power at the maximum DF found on the surface of each patient. (D)

Standard deviation of the power map at the maximum DF found on the surface.

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Jalife, Felipe Atienza and Omer BerenfeldMaría S. Guillem, Andreu M. Climent, Jose Millet, Angel Arenal, Francisco Fernández-Avilés, José

Potential MappingNon-Invasive Localization of Maximal Frequency Sites of Atrial Fibrillation by Body Surface

Print ISSN: 1941-3149. Online ISSN: 1941-3084 Copyright © 2013 American Heart Association, Inc. All rights reserved.

Dallas, TX 75231is published by the American Heart Association, 7272 Greenville Avenue,Circulation: Arrhythmia and Electrophysiology

published online February 26, 2013;Circ Arrhythm Electrophysiol.

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The online version of this article, along with updated information and services, is located on the

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is online at: Circulation: Arrhythmia and Electrophysiology Information about subscribing to Subscriptions:

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document. Permissions and Rights Question and Answerinformation about this process is available in the

requested is located, click Request Permissions in the middle column of the Web page under Services. FurtherCenter, not the Editorial Office. Once the online version of the published article for which permission is being

can be obtained via RightsLink, a service of the Copyright ClearanceCirculation: Arrhythmia and Electrophysiology Requests for permissions to reproduce figures, tables, or portions of articles originally published inPermissions:


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SUPPLEMENTAL MATERIAL

Table 1. Patients’ characteristics and outcome

Patient#

Age

Gender

Medications

Structural

heart disease

Duration

of AF (years)

AF Type

# previous ablations

DF gradient

in EGMs

DF gradient in

BSPM

DF guided ablation

SR conversion during ablation

1 47 M Flecainide, Bisoprolol

No 3 Parox 3 Yes Yes No Yes

2 54 M Flecainide No 7 Parox 1 Yes Yes No Yes 3 64 M Flecainide,

Atenolol No 4 Parox 1 Yes Yes DF abl. after CPVI Yes

4 49 M Atenolol No 3 Parox 3 Yes Yes Yes No 5 51 M Flecainide,

Atenolol No 2 Parox 1 Yes Yes No No

6 60 M Digoxine No 3 Persist 0 Yes Yes No No 7 61 M Proprafenone No 17 Parox 2 Yes Yes DF abl. after CPVI No 8 47 M Carvediol,

Digoxine TCM 3 Persist 1 Yes No Yes (followed by CPVI) No

9 59 M Atenolol No 5 Persist 0 No No No No 10 65 M Atenolol No 5 Persist 1 Yes No No No 11 68 F Atenolol No 7 Parox 0 Yes Yes No No 12 67 M Flecainide,

Bisoprolol No 10 Parox 1 No No DF abl. after CPVI No

13 47 M Bisoprolol Ischemic 1 Parox 0 Yes Yes No Yes 14 49 M Bisoprolol No 2 Parox 0 Yes No No Yes

M: male; F: female; TCM: tachycardiomyopathy; Parox: paroxismal AF; Persist: persistent AF; SR: sinus rhythm

Non-Invasive Localization of Maximal Frequency...

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