Daniela Husser, MD*,†, Martin
Stridh, PhD‡, Leif Sornmo, PhD‡, S. Bertil Olsson,
MD, PhD§, Andreas Bollmann, MD*,†,§
Department of Cardiology, Good Samaritan Hospital* and Harbor-UCLA
Medical Center†, Los Angeles, USA
Departments of Electroscience‡ and Cardiology§,
Lund University, Lund, Sweden
Address for correspondence:
Andreas Bollmann, MD, Department of Cardiology, Good Samaritan Hospital,
1225 Wilshire Blvd., Los Angeles, CA 90017, USA. Email:
andreas.bollmann@kard.lu.se
Abstract:
Atrial fibrillation (AF) is the most common arrhythmia encountered in
clinical practice. Neither the natural history of AF nor its response to
therapy are sufficiently predictable by clinical and echocardiographic parameters.
Atrial fibrillatory
frequency (or rate) can reliably be assessed from the surface electrocardiogram
(ECG) using digital signal processing (filtering, subtraction of averaged
QRST complexes, and power spectral analysis) and shows large inter-individual
variability. This measurement correlates well with intraatrial cycle length,
a parameter which appears to have primary importance in AF domestication
and response to therapy. AF with a low fibrillatory rate is more likely to
terminate spontaneously, and responds better to antiarrhythmic drugs or
cardioversion while high rate AF is more often persistent and refractory
to therapy.
In conclusion,
frequency analysis of AF seems to be useful for non-invasive assessment
of electrical remodeling in AF and may subsequently be helpful for guiding
AF therapy.
Key words: atrial fibrillation – ECG signal processing
– electrical remodeling – antiarrhythmic drug monitoring – cardioversion.
In recent years, mechanisms leading to atrial fibrillation
(AF) induction and maintenance have begun to be explored and rapidly evolving
therapies including new antiarrhythmic drugs, antitachycardia pacing algorithms
and catheter ablation techniques have been introduced in clinical practice.
Nevertheless, outcome of AF patients is still unsatisfactory which - to
the greatest extent - can be attributed to the progressing nature of this
arrhythmia and high AF recurrence rates following restoration of sinus rhythm.
Moreover, AF treatment (e.g. rate vs. rhythm control, choice of antiarrhythmic
drugs or device therapy, curative ablation) may be viewed as being based
on trial and error, since no test is able to predict the natural history
of this arrhythmia or its response to treatment. Subsequently, current AF
management guidelines
1 provide
no treatment recommendations that “take the various mechanisms and patterns
of AF into account”. Thus, it seems desirable to develop tests that quantify
AF disease state and guide AF management
2.
Virtually in
every patient with AF, a standard surface electrocardiogram (ECG) is acquired,
the main purposes being confirmation of arrhythmia presence and determination
of ventricular rate. Fibrillation waves have, moreover, various appearances
ranging from fine to coarse and from disorganized to organized, flutter-like.
Interestingly, the mechanisms behind the various appearances of the fibrillatory
process on the ECG and the possible prognostic information contained within
fibrillation waves have just very recently begun to be explored.
The aim of
this article is twofold; (1) to describe novel frequency analysis techniques
of the surface ECG for characterization of the fibrillatory process, and
(2) to present possible applications of these methods, namely for (a) exploring
autonomic modulation of AF, (b) monitoring and predicting antiarrhythmic
drug effects, and (c) predicting AF recurrence following restoration of sinus
rhythm. For this purpose, Medline-listed, peer-reviewed studies are included
in this review.
Rationale for Using Atrial Fibrillatory Rate to Characterize
Human AF
Shortening
of atrial refractoriness and maladaptaion to rate are hallmarks of atrial
electrical remodeling in AF
3.
During AF, re-excitation occurs during the repolarization phase of the preceding
electrical wave, implying that local excitation almost always occurs without
any obvious latency beyond the refractory period. Subsequently, the average
atrial fibrillatory rate is likely to reflect averaged refractoriness at
any part of the tissue involved. This assumption has been independently
verified in both animal
4 and human
AF
5,6. Hence,
the length of the averaged atrial fibrillatory cycle (which is inversely related
to fibrillatory frequency or rate) can be used as an index of the averaged
atrial myocardial refractoriness and subsequently AF organization
7. The importance of shortened refractoriness
and subsequently its transformation into short atrial cycles or high rates
as assessed by direct intraatrial measurements for AF progression and response
to therapy has been well established. Results will be reported as cycle length
in milliseconds (ms) if they were obtained from intraatrial recordings. If
they were obtained from surface recordings, they will be reported as fibrillatory
rate in fibrillations per minute (fpm, the rationale to use this expression
is discussed in depth below).
Firstly, spontaneous
arrhythmia behavior is related to baseline fibrillatory cycle length. Asano
et al induced AF with rapid pacing in 30 patients undergoing electrophysiologic
study
8. Patients in whom AF terminated
spontaneously (n=20) had an average fibrillatory cycle lenghth of 176 ms
(341 fpm), significantly lower than the 157 ms (382 fpm) recorded in the
group of patients where the arrhythmia persisted (n=10). Similar observations
were made by Boahene et al who also measured the fibrillatory cycle length
from the right atrium in 55 patients with Wolff-Parkinson-White syndrome
9. These investigators also found that patients
with sustained AF (n=45) had shorter mean cycle lengths compared to patients
with non-sustained AF (n=10).
Secondly, response
to antiarrhythmic drug therapy has been shown to be associated with baseline
cycle length and drug-induced cycle length changes. Stambler and colleagues
identified a mean right atrial cycle length of 160 ms (375 fpm) as a valuable
cutoff-point for conversion to sinus rhythm with ibutilide
10. No patient with shorter cycle length (higher
rate) was converted by ibutilide, whereas conversion occured in 64 % of
those patients with longer cycle length (lower rate). Similarly, Fujiki et
al
11 by using spectral analysis
of right atrial electrograms reported 100 % AF conversions with cibenzoline
or procainamide if the baseline atrial cycle length was > 168 ms (<
357 fpm) as opposed to only 17 % in patients with shorter cycle lengths (higher
rates). Moreover, AF conversion was associated with cycle length prolongation.
AF terminated after class I drug administration in 88 %, if the atrial cycle
length had been prolonged to > 210 ms (< 285 fpm), in contrast to
only 10 % if the post-drug cycle length was shorter.
Finally, there
exists a close relation between the degree of electrical remodeling expressed
as refractoriness changes and AF recurrence following cardioversion. Manios
et al found that patients who failed to shorten the monophasic action potential
duration to less then 195 ms at a pacing cycle length of 350 ms were more
likely to have AF recurrence
12.
This finding was explained by a more abnormal rate adaptation curve, which
is in agreement to an early study performed by Attuel and colleagues showing
that poor or absent rate adaptation of atrial refractory periods (that is,
the normally close correlation between stimulation cycle length and refractory
period was lost) is related with vulnerability to AF
13. These findings have more recently been
replicated in patients undergoing internal cardioversion
14. In their study, Biffi et al identified
both a shorter atrial effective refractoriness and abnormal rate adaptation
as independent predictors for AF relapses, the latter being the strongest
predictor.
Prompted by
the likely usefulness of non-invasive test that assesses the average fibrillatory
rate from the surface ECG for exploration of AF pathophysiology and AF managment,
frequency analysis techniques were independently introduced by our groups
15,16, while two earlier
studies proposed to use this technique to subdivide AF and atrial flutter
17 or automatically identify AF among different
rhythms
18.
Frequency Analysis Techniques
Methodological Considerations
Using digital
recording techniques, traditional ECG signals are recorded. In most studies
atrial fibrillatory rate has been obtained by spectral analysis techniques
of resting ECG recordings such as standard 12-lead
15,16 or (modified) orthogonal
recordings
19,20,21. The method has, however, also been applied
to ambulatory ECG recordings using conventional ambulatory leads
22,23,24.
The accuracy
of frequency analysis techniques strongly relies on the fact that the largest
possible fibrillatory waves should be present for further signal processing.
Therefore, it was suggested to analyze lead V1 when using a standard ECG
or, as proposed previously by Waktare et al
25,
a “bipolar modification of V1” covering the atria (low C1) when applying
different ECG recording systems (e.g. Holter ECG)
20.
Since the atrial
and ventricular activities overlap spectrally, linear filtering techniques
are not suitable for extraction of the fibrillatory signal from the surface
ECG. Instead, subtraction of averaged QRST complexes needs to be performed
producing a remaining atrial fibrillatory signal for further analysis (
Figure
1). Originally, a fixed averaged QRST-complex was used for cancellation
in individual leads. Further development of the method allowed an improved
cancellation of the QRST-complex by taking changes in QRS morphology due
to alterations in the electrical axis of the heart into account (spatiotemporal
QRST cancellation)
26.
Figure 1: Frequency analysis of AF. Two
seconds (out of a 60 second recording) of an ECG signal from a patient with
AF (upper panel), and the same interval after QRST cancellation (middle
panel, amplitude scale is magnified five times). This fibrillatory signal
is then subjected to Fourier analysis. Time-frequency distribution (left
box), power frequency spectrum in which the dominant fibrillatory rate is
determined (middle box), frequency trend (right box).
Following QRST
cancellation, a power spectrum is obtained by using a windowing technique
and Fourier analysis to process the remainder ECG. This as well as the
associated windowing technique (window type, length and overlap) determine
the appearance of the frequency power spectrum
15. Variants of Fourier transform based methods
including conventional Fourier analysis and spectral averaging techniques
based on short overlapping segments have been applied to ECG segments ranging
from 10 seconds to 5 minutes
15,16,27,28,29. Typically a distinct spectral peak is
obtained which corresponds to the most dominant fibrillatory rate of nearby
endocardial sites
15,16,
but bi- or multimodal peaks are also sometimes observed
15,30.
More recently,
a time-frequency analysis approach has been developed by which the time-frequency
distribution is decomposed into a number of descriptive functions reflecting
second-to-second variations in fundamental frequency and waveform morphology
31,32. The main advantage
of using this approach is that the local signal quality and structure is
also assessed. The spectral profile differs from the conventional spectrum
in that the different local spectra have been frequency aligned before averaging.
This results in more distinct peaks and a more clearly discernable harmonic
structure. With its second-to-second resolution, the method can rapidly
detect changes in frequency, regularity, amplitude, waveform and signal structure
and quality (
Figure 1).
To date, the dominant
spectral peak has been presented as either dominating atrial cycle length
(in ms), fibrillatory rate (in fpm) or fibrillatory frequency (in Hz). Since
it has become evident that the results of this method might be of practical
clinical importance, we believe that the results should be expressed in
a way which is closest to the nomenclature of other surface ECG rate-variables
(e.g. sinus rate, atrial tachycardia or flutter rate)
33. The expression “fibrillatory rate”, with
its unit “fibrillations per minute - fpm” may therefore be most appropriate
20. Moreover, the calculation of cycle length
from the frequency power spectrum with its standard unit Hz (cycle length
in ms = 1000/frequency in Hz) seems somewhat troublesome
34.
While this calculation is appropriate for single measurements, its application
to multiple simultaneous or repeated measurements such as pre- and post-drug
states may introduce significant statistical and subsequently scientific
errors, especially if the baseline frequency exhibits large inter-individual
variability. The reason is, that the same frequency difference results in
larger cycle length differences for low frequencies compared to high frequencies.
Method Validation and Reproducibility
A direct comparison
between endocardially recorded electrograms and body surface recordings
clearly evidences the validity of fibrillatory rate obtained from surface
ECG as an index of the average atrial fibrillatory cycle and subsequently
atrial refractoriness. Fibrillatory rates calculated from lead V1 substitute
the right atrial free wall
15,16
whilst rates from an oesophageal lead reflect atrial septal and left atrial
activity
15.
Figure 2 illustrates
the close agreement of right atrial and coronary sinus fibrillatory rates
with rates obtained from simultaneously recorded surface ECG lead V1 in
persistent AF. Interestingly, rate differences and variability between ECG
and electrograms increase with growing anatomical distance (right atrium
– coronary sinus – pulmonary veins) to V1. Approximately 50 % of V1 and superior
pulmonary vein rates are also within close range. In the other cases, the
latter has been found to be substantially faster but also slower than V1 (right
atrium), indicating that superior pulmonary veins might not necessarily be
the driving source of the fibrillatory process.
Figure 2: Agreement of fibrillatory rates
from the right atrium (upper left), coronary sinus (upper right), superior
pulmonary veins (lower) with simultaneously recorded fibrillatory rates
from surface ECG lead V1 (Bland-Altman method).
Although the
gross atrial fibrillatory pattern, studied by multiple simultaneous epicardial
recordings, is reproducible already in repeated measurements of only 8 seconds
duration
35, calculation of fibrillatory
rates during steady state conditions from even longer time intervals discloses
a true variability. Thus, increasingly longer recording times of up to 30
minutes result in a successive decrease of this variability. The reproducibility
of the method is thus enhanced by prolonging the recording time during steady
state conditions, allowing integration of atrial fibrillatory activity over
longer periods. For practical reasons, steady state recordings may be restricted
to one to five minutes, yielding rate variation coefficients of 2.1 %
15.
In persistent
AF, there is minor short-term rate variability
15,16,27
and considerable diurnal variability (for details see below)
23,24, whilst repeated daily
frequency measurements at identical medication at the same time under similar
conditions discloses an insignificant fibrillatory rate variability
36. In contrast, rate variability in paroxysmal
AF seems to be related to its natural course with a rate increase within
the first five minutes of an AF episode
22
and a rate decrease prior to termination
22,28.
Exploring Autonomic Modulation of AF
Vagal as well
as sympathetic stimulation have been shown to reduce atrial refractory periods
and increase their heterogeneity
37,38,39. In the human atrium
beta stimulation has been found to predominate over vagal stimulation
38. Moreover, invasive studies performed in
subjects with sinus rhythm have suggested a circadian pattern in atrial
refractoriness with longer refractory periods during night time and refractory
period shortening during daytime
40,41 which supports the role of the autonomic
nervous system in modulating atrial electrophysiologic properties.
Subsequently,
atrial fibrillatory rate seems to be ideal for monitoring the effect of
autonomic tone changes – either spontanenous (circadian) or by autonomic
maneuvers provoked – on atrial electrophysiology. Indeed, the circadian variability
of atrial fibrillatory rate has been explored in independent studies
23,24. Fibrillatory rate obtained
from Holter ECG’s with persistent AF showed a significant decrease at night
and an increase in the morning hours
23,24. In 6 out of 30 individuals studied by our
group
23, dominant nocturnal
fibrillatory rate increased, however, concomitantly with a decrease in ventricular
rate, while the opposite change occurred in the morning hours.
The second
area of investigation concerns effects on atrial fibrillatory rate following
vagal or sympathetic stimulation during experimental conditions. Carotid
sinus massage, supposed to mainly induce vagal stimulation, resulted in a
variable effect on fibrillatory rate in 19 patients
42. A reproducible decrease was noted in 9
individuals, whilst a rate increase occurred in 8 and no change was observed
in two patients. Interestingly, calcium-channel blocker treatment was the
only variable effecting the rate response to carotid sinus massage. Calcium-channel
blockers were more frequently used in patients with a decrease in fibrillatory
rate compared to patients with a rate increase. The effect of adrenergic stimulation
via head-up tilting on fibrillatory rate was studied in 14 patients with
long-lasting AF
43. In 12 patients
head-up tilting increased fibrillory rate significantly, while there was
no rate change in the remaining two.
With the availability
of instantaneous fibrillatory rates obtained from time-frequency analysis,
their second-to-second variation can be explored by spectral analysis techniques,
similar to those used for analyzing heart rate variability. By applying
this approach, Stridh et al
44
noted a spectral peak in 2 out of 8 patients with permanent AF at the breathing
frequency of 0.125 Hz during controlled respiration that disappeared after
atropine injection.
All these findings
together clearly highlight the complexity of autonomous nervous system effects
on atrial electrophysiology and justify further exploration.
Monitoring and Predicting Antiarrhythmic Drug Effects
Class I and
III antiarrhythmic drugs have been shown to increase atrial cycle length
(decrease fibrillatory rate) which coincides with increased refractoriness
and decreased conduction velocity
45.
These mixed effects might explain why atrial cycle length is closely related
with baseline refractoriness
6
but not with refractoriness after antiarrhythmic drug administration
46. Even though the individual contribution
of refractoriness prolongation or conduction slowing might currently not
be differentiated, monitoring drug action on atrial fibrillatory rate seems
well fit to be explored by analysing the surface ECG. Indeed, a substantial
reduction in atrial fibrillatory rates following several different intravenously
or orally administered class I and III antiarrhythmic drugs as well as following
verapamil or magnesium has been observed (
Table 1).
Table 1. Summary of available literature on
frequency analysis techniques (for details see text).
|
Primary
Investigator
Investigation
|
Bollmann |
Olsson
|
Stridh
&
Sornmo
|
Others
|
Validation
|
Comparison
with intracardiac EGM (N=54)16
|
Comparison
with intracardiac and esophageal EGM (N=8)15
|
|
Comparison
with asystolic ECG segment (N=12)27
|
Method improvement
|
|
|
Spatiotemporal
QRST cancellation26
Time-frequency analysis31,32
|
|
Reproducibility
|
Short-term-reproducibility
(N=6)16
|
Short-term-reproducibility
(N=10)15
Long-term-reproducibility (N=10)36
|
|
Short-term-reproducibility
(N=5)27
|
Circadian
rate variation
|
Every 6 hours
(N=30)23
|
Every hour
(N=20)24
|
|
|
Spontaneous
AF termination
|
Onset vs.
mid-episode vs. termination (N=11)22
|
|
|
10 min before
termination vs. termination (N=19)28
|
Monitoring
and predicting atrial drug effects
|
i.v. ibutilide
(N=15)16
p.o. amiodarone (N=5)22
p.o. sotalol (N=3)22
p.o. flecainide (N=18)19
p.o. verapamil (N=27)62
|
i.v. sotalol
(N=5)15
p.o. verapamil (N=10)36
i.v. MgSO4 (+GIK) (N=13)63
|
|
i.v. cibenzoline
(N=5)28
i.v. procainamide (N=3)28
p.o. bepidril (N=22)29
i.v. ibutilide (N=19)64
|
Monitoring
autonomic maneuvers
|
Carotid sinus
massage (N=19)42
|
Head-up tilt
test (N=14)43
|
Controlled
breathing + i.v. atropine (N=8)44
|
|
Predicting
ADFT
|
Internal CV
(N=19)57
|
|
|
External CV
(N=29)58
|
Predicting
AF recurrence
|
Internal CV
(N=19)57
External, internal or drug-induced CV (N=44)21
|
|
|
|
Heart rhythm
differentiation
|
|
|
|
Sinus rhythm
vs. atrial fibrillation18
|
CV = cardioversion, EGM = electrogram, GIK = glucose,
insulin, potassium, ADFT = atrial defibrillation threshold
Two examples
of drug monitoring are presented in
Figure 3 showing the transition
from a high rate (less organized) to a low rate (more organized) fibrillation
following acute intravenous sotalol infusion or 3-day oral flecainide administration.
Figure 3: Examples of antiarrhythmic
drug monitoring. Atrial fibrillatory rate obtained from time-frequency-analysis
during intravenous infusion of dl-sotalol. The solid black colour
indicates the actual fibrillatory rate. Initially, the atrial rate is at
the level of 390 fpm (6.5 Hz), but decreases successively to the level of
330 fpm (5.5 Hz) during 20 minutes (upper panel). Time-Frequency-analysis
at baseline (middle) and after 3-days oral flecainide intake (bottom).
Frequency power spectrum (left) and frequency trend (right). Please note
the more pronounced dominant and harmonic peaks after drug administration
as well as atrial rate reduction and rate stabilization.
Besides direct
monitoring of antiarrhythmic drug effects, it seems also possible to identify
suitable patients for pharmacological cardioversion. A baseline fibrillatory
rate of 360 fpm was highly sensitive and specific for prediction of AF
termination following intravenous ibutilde
16
or oral flecainide
19 (
Figure
4). In contrast, Fujiki et al noted no baseline fibrillatory rate difference
between patients who converted to sinus rhythm and those who did not following
oral bepridil administration. Instead, larger rate increases were associated
with AF termination. While this concept is interesting and well established
in experimental AF
45, it needs
to be emphasized that these authors calculated atrial cycle length from
the frequency spectrum, which may have introduced the aforementioned statistical
errors
34.
Figure 4: Relation between baseline fibrillatory
rate and response to intravenous ibutilide (left)
16 and oral flecainide (right)
19. AF with slower rates is more likely to respond
to antiarrhythmic drug therapy, while faster rates are more often found in
drug-refractory AF.
Patients with
a low fibrillatory rate may have a small number of wavelets (long wavelength),
whereas those with higher rates have multiple wavelets (short wavelength)
7. In the former group class I or III antiarrhythmic
drugs by decreasing fibrillatory rate may have increased wavelength (or
the excitable gap) and therefore reduced the number of wavelets that could
coexist. This would have increased the statistical chance that all wavelets
might extinguish simultaneously and terminate the fibrillatory process
47.
One previous study suggested that
a stepped conversion regimen of first-line ibutilide followed by electrical
cardioversion for patients who fail to convert is less expensive and has
a higher conversion rate than first-line electrical cardioversion
48. Given the expense of antiarrhythmic therapy
and the risk of side effects including ventricular proarrhythmia, a test
that differentiates responders from non-responders is likely to be even
more cost-effective. In addition, drug monitoring using frequency analysis
techniques may be useful for finding optimal drug dosages and timing of
interventions in the individual patient.
Predicting AF Recurrence
Previous investigations
49,50 have shown that most
AF relapses occur within the first weeks after cardioversion with decreased
but constant recurrence rates thereafter. Early vulnerability to AF re-initiation
within this time period is related to electrophysiological abnormalities,
while structural abnormalities seem to be primarily responsible for later
AF recurrences
51. This time
course might be explained by the fact that reversal of the electrical remodeling
process occurs rapidly once sinus rhythm is restored
12,52,53,
while structural changes persist for longer periods
51. Subsequently, characterization of atrial
electrophysiology has been suggested for identification of patients at
risk for early AF recurrence
2.
Atrial premature
beats with short coupling intervals have been shown to promote early AF
reinitiation following cardioversion
54,55. AF reinduction by an atrial premature
beat relies on the fact that a relatively short atrial wavelength (conduction
velocity x refractory period) must be present
56.
As highlighted before, atrial fibrillatory rate reflects atrial refractoriness
5,6, and might subsequently
represent a marker for early AF susceptibility following restoration of
sinus rhythm. Indeed, previous studies reported higher rates in relapsed
patients immediately prior internal
57
or external cardioversion
21
when compared with non-relapsed patients and also close relationships between
fibrillatory rate and defibrillation thresholds
57,58.
Two previous
studies
21,59
have investigated the combined predictive value of fibrillatory rate and
echocardiographic left atrial parameters. In one study
59, the authors calculated an index combining
shortest cycle length from oesophageal or V1 lead and standard left atrial
diameter and were able to show that patients with recurring AF had significantly
lower values than patients who remained in sinus rhythm. Another study
21 demonstrated that the combination of fibrillatory
rate and systolic left atrial area predicted early AF recurrence after succesful
cardioversion with a high accuracy and was able to provide individual risk
estimates (
Figure 5).
Figure 5: Outcome following cardioversion stratified
by an “electromechanical index” (EMI) according to the regression equation
(EMI = 0.176 systolic left atrial area + 0.029 fibrillatory rate – 17.674)21.
These parameters
seem therefore well suited to describe the individual atrial remodeling.
The concept of ECG-guided cardioversion may gain even greater importance
in the light of previous findings from two studies comparing rate- with rhythm-control
strategies
60,61.
In these studies, rhythm-control seemed not to be superior than rate-control
in asymptomatic, mostly eldery patients with recurring AF and structural
heart disease. This highlights the need to select candidates for cardioversion
not only based on clinical judgement but also on measures that are able
to determine the likelihood of maintaining sinus rhythm such as rate parameters
obtained from the surface ECG.
Conclusions
Atrial fibrillatory
rate and its variability can be reliable obtained from the surface ECG in
AF patients using spectral analysis techniques. These parameters exhibit
a significant interindividual variability allowing individual quantification
of the atrial electrical remodeling process. Frequency analysis of AF might
prove useful in identification of underlying AF pathomechanisms, and prediction
of therapy efficacy (drug-induced conversion, maintenance of sinus rhythm,
selecting antiarrhythmic drugs, identifying candidates for non-pharmacological
AF therapy). Further larger studies are necessary to determine the role
of these techniques in different AF management strategies in order to select
and time the appropriate therapy for the individual patient.
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