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Tsai and Lin, J Biocatal Biotransformation 2012, 1:2
http://dx.doi.org/10.4172/2324-9099.1000e108
Journal of Biocatalysis &
Biotransformation
Editorial
a SciTechnol journal
The Frequency Analysis and the
Atrial Fibrillation
Wen-Chin Tsai1,2 and Yenn-Jiang Lin3,4*
Atrial fibrillation (AF) is the most common cardiac arrhythmia in
clinical practice. It is well known that AF depends on the interaction
between the “triggers” and “substrate”, and the elimination or isolation
of the “trigger” can cure AF. In recent years, growing knowledge
of atrial substrate mapping has helped in learning more about the
maintenance of AF, and to identify the critical atrial substrate for
catheter ablation, in spite of the elimination of all triggers initiating
AF. However, how to identify the critical arrhythmogenic atrial
substrate remained unclear. The question will be addressed in the
present article.
Atrial fibrillation (AF) is the most common cardiac arrhythmia
in clinical practice. It is well known that AF is characterized by a
spatiotemporal organized activation, with a variable frequency and
spectral morphology throughout the atria. The complex activation
patterns of AF can result in fractionated signals, separated by short
and variable intervals that prohibit a reliable identification of the
precise local activation rate or interval (Figure 1). Furthermore, it is
difficult to represent sustained episodes of AF by only a small sample
of fibrillatory electrograms, within a limited period of time. One
reasonable method for a transformation is to transform the timedomain electrogram signal into a frequency-domain electrogram
signal, i.e. to elucidate the data beyond the time-domain electrogram.
Frequency-domain signals provide a feasible method to analyze the
temporal and spatial distribution of fibrillatory waves during AF. The
current review focuses on the basic principles and methodology of the
frequency domain analysis using the Fast Fourier Transform (FFT),
and the further clinical implications during catheter ablation of AF.
With each “normal” heartbeat (sinus rhythm), an electrical signal
arising from the sinus node spreads to the right and left atria, and
therefore, causes atrial depolarization. Then, the electrical impulse
spreads through the atrioventricular node and further initiates
ventricular depolarization. As the signal travels, it causes the heart to
contract and pump blood. AF is characterized by a rapid and irregular
activation of the atrium, for example, 400-600 pulses of chaotic
contractions of the atrium per minute. The ventricular rate during AF
is thus, no longer under the physiological control of the sinus node.
An electrocardiogram (ECG) recording is necessary to diagnose
AF. The diagnosis requires an ECG or rhythm strip demonstrating:
1) “absolutely” irregular RR intervals, 2) no distinct P waves on
the surface ECG, and 3) an atrial cycle length (when visible) that is
usually variable and less than 200 ms [1]. A brief period of AF may
cause palpitations, chest discomfort and light-headedness. Sustained
*Corresponding author: Yenn-Jiang Lin, Division of Cardiology, Department
of Medicine, Taipei Veterans General Hospital, 201, Sec. 2, Shih-Pai Road,
Taipei, Taiwan, Tel: +886-2-2875-7156; Fax: +886-2-2873-5656; E-mail:
[email protected]
Received: September 12, 2012 Accepted: September 25, 2012 Published:
September 29, 2012
International Publisher of Science,
Technology and Medicine
AF with an uncontrolled ventricular response rate can cause severe
congestive heart failure [2].
It is well known that the development of AF requires a “trigger”
and an anatomic or functional substrate capable of both initiation and
perpetuation of AF [3]. Pulmonary Vein (PV) focal firing may trigger
AF, or act as a rapid driver to maintain the arrhythmia. Catheter
ablation strategies for the cure of AF have targeted isolation of the PVs,
whereas additional substrate modification is required in 20% to 40%
of patients with paroxysmal AF, and most with non- paroxysmal AF
[4,5]. Therefore, it is important to identify the critical arrhythmogenic
atrial substrate responsible for AF perpetuation, and for a further
catheter ablation strategy. Three-dimensional (3D) electroanatomic
mapping systems were introduced into catheter ablation of AF more
than a decade ago. Currently, the Ensite NavX (St Jude Medical,
Minnetonka, MN, USA) and Carto (Biosense Webster Inc, Diamond
Bar, CA, USA) systems are the most popular 3D mapping systems
used worldwide, because they provide accurate visualization of the
atrial anatomy and identification of the atrial substrate properties for
catheter ablation of atrial arrhythmias (Figure 2).
One of the important approaches used to guide AF ablation is
an analysis of the local atrial electrogram properties. A time-domain
analysis refers to an evaluation of the inter-electrogram intervals
by a local cycle length criterion. This involves a determination of
the intervals between the local activations, with signal amplitude
cutoffs and refractory period blanking, used to assess the “true”
local firing and avoid contamination by far-field events and double
sampling of complex signals. A frequency-domain analysis refers
to the quantification of the local Dominant Frequency (DF) by
spectral analysis methods incorporating the FFT [6]. Regarding
the electrogram morphology analysis, all data were digitalized at 1
KHz and analyzed by a Matlab computer program. The frequency
resolution was 0.54 Hz. Time-domain bipolar signals were sampled
at 1200 Hz and filtered with a bandwidth from 32 to 300 Hz. Then,
the data was exported to an external computer program for an
electrogram analysis. The FFT analysis was performed using a
Hanning window function on each segment from all recording sites
in the Left Atrium (LA). The Dominant Frequency (DF) was defined
as the frequency with the maximum power in the frequency range. To
ensure the reliability of the DF detection, the lowest-noise signal was
chosen for the analysis. The DF peaks were determined when the area
ECG
Intracardial
recording
200 ms
Figure 1: Schematic presentation of the surface electrocardiogram and
intracardial recordings in a patient with AF. The electrogram shows complex
and fractionated signals, separated by short and variable intervals.
All articles published in Journal of Biocatalysis & Biotransformation are the property of SciTechnol, and is protected by
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Citation: Tsai WC, Lin YJ (2012) The Frequency Analysis and the Atrial Fibrillation. J Biocatal Biotransformation 1:2.
doi:http://dx.doi.org/10.4172/2324-9099.1000e108
A
Endoscopic view
Fluoroscopic view
B
5 mV
5 mV
0 mV
0 mV
Figure 2: 2A. Endoscopic (left) and fluoroscopic (right) views showing the
catheter location during the AF ablation procedure. The 3-dimensional
mapping system enables simultaneous visualization of epicardial and
endocardial views of the LA. 2B. Electroanatomic bipolar voltage mapping
of the LA (left, anterior view and right, posterior view). The range of colors
displays a bipolar voltage of ≤0.5 mV as gray to a voltage of >2.0 mV as
purple.
example in figure 3, the color-coated electroanatomic reconstruction
of the LA depicted CFAE maps based on the algorithm of the ICL
(Figure 3A) and ACI (Figure 3B). The former map could highlight
the rapid repetitive electrograms and the latter one could emphasize
the continuous and disorganized electrograms. The area coated with
the color orange represented high CFAE sites, based on the timedomain analysis in both maps. On the other hand, the intracardiac
bipolar electrograms and corresponding frequency spectra of the
high CFAE sites showed a high DF in the ICL map and low HI in
the ACI map, respectively. Further analysis demonstrated that the
ICL value correlated with the DF (R=0.51, P<0.001), whereas the ACI
value correlated better with the HI (R=0.38; P<0.01). There was no
correlation between the ICL value and HI, or the FI value and DF
(p>0.05) (Figure 4).
A
B
2.00
Interval Confidence Level
1-2-ReMap 5 sec CFAE TSAI > 174 Points
115.50ms
Average Complex Interval
1-2-ReMap 5 sec CFAE TSAI > 174 Points
38.00
of the dominant frequency peak over the total spectrum was higher
than 0.3.
Volume 1 • Issue 2 • 1000e108
AP
1.25 cm
200.0 nm/sec
200.0 nm/sec
R7-R8 3.38
R7-R8 3.38
H1-H2 0.20
H1
H1-H2 0.20
1.00
H1
0.25
1.00
0.08
+
0.07
0.2
0.15
+
0.1
0.06
: 8.1667 Hz
: 7.5 Hz
: 8.8333 Hz
: 16.3333 Hz
: 7.8333 Hz
: 6.8333Hz
: 6.3333 Hz
: 7.3333 Hz
0.05
0.04
0.03
HI=0.30579
0.02
HI=0.55916
0.05
0.01
0
0
2
4
6
8
10
Frequency (Hz)
0
12
0
2
4
6
8
10
Frequency (Hz)
12
Figure 3: The regional distribution of the CFAEs sites in a patient with
AF. The intracardiac bipolar electrograms and corresponding frequency
spectra of the high DF sites are shown. 3A. CFAE map based on the ICL.
The high CFAE site (yellow arrow) exhibits a high DF (8.17 Hz). 3B. CFAE
map based on the ACI. The high CFAE site (yellow arrow) exhibits a low
HI (0.31).
A
14
B
r = 0.51 p < 0.001
1.0
12
0.8
10
0.6
8
0.4
6
0.2
4
0.0
0
C
r = 0.38 p < 0.001
HI
Frequency (Hz)
10
20
30
40
50
ICL
1.0
50
D
p = 0.33
60
70
80
90
100
110
120
ACI (ms)
p = 0.71
14
12
0.8
Frequency (Hz)
In this laboratory, the relationship between the different detection
algorithms (ICL vs. ACI) and CFAE identification was investigated
based on both time-domain and frequency-domain approaches.
Time-domain CFAE mapping of the LA was performed based on
the detection algorithms with the ICL and ACI under a recording
duration of 5 seconds. The ICL characterized the repetitiveness of the
fractionated atrial electrograms. The ACI that displayed the average
value for all intervals was presented with the fractionated interval (FI).
CFAEs was defined as an FI between 50-120 ms, or an ICL>5 [9]. The
method of the FFT has been described previously [10]. A 6.82-second
segment of data was exported to an external computer program. The
FFT analysis (sampling rate, 1200 Hz; resolution, 0.14 Hz, with a
Hanning window function) was performed from all recording sites
(CFAEs site). The largest peak frequency of the resulting spectrum
was identified as the DF. The area under the DF peaks over the total
areas was defined as the Harmonic Index (HI). As shown in the
1.25 cm
AP
0.6
HI
Nademanee et al. [7] first described a new ablation approach
to target the Complex Fractionated Atrial Electrograms (CFAEs),
which were thought to indicate areas of slow conduction and/or
pivot points of reentry of wavefronts. Using this approach, the critical
atrial substrate could be modified and it could achieve procedural
AF termination, restoration of normal sinus rhythm, and long-term
maintenance of sinus rhythm. Till now, most of the electroanatomical
mapping systems offered an automatic algorithm to display the CFAE
distribution based on a time-domain method [8]. In 2009, Grzeda
et al. [6] presented the results of an interesting theoretical analysis
of time-domain versus frequency-domain approaches to the CFAE
identification. The study demonstrated that the time-domain methods
in targeting the AF ablation sites had a good predictive value, but
were easily prone to degradation. In contrast, the frequency-domain
method seems to be much more robust. The 3D electroanatomic
mapping system provided specific software with a detection algorithm
for CFAE identification, which included an Interval Confidence Level
(ICL) and Average Complex Interval (ACI).
51.00ms
0.4
0.2
10
8
6
0.0
0
10
20
30
ICL
40
50
4
50
60
70
80
90
100
110
120
ACI (ms)
Figure 4: Frequency analysis demonstrated that the ICL value correlated
with the DF (R=0.51, P<0.001) whereas the ACI value correlated better
with the HI (R=0.38; P<0.01). There was no correlation between ICL value
and HI, or the FI value and DF (p>0.05).
• Page 2 of 3 •
Citation: Tsai WC, Lin YJ (2012) The Frequency Analysis and the Atrial Fibrillation. J Biocatal Biotransformation 1:2.
doi:http://dx.doi.org/10.4172/2324-9099.1000e108
In this laboratory, the substrate modification was guided by a
continuous CFAE ablation post PV isolation. Critical CFAEs in the
vicinity of the highest DF sites correlated with a higher procedural
AF termination rate for the CFAE ablation [11]. This finding
indicated that a combined analysis of both the fractionation and
frequency analysis may allow for a more specific approach, to identify
the critical atrial substrate that remains after PV isolation, without
unnecessary ablation of an extensive atrium myocardium. Different
automatic algorithms identify critical atrial substrates with different
electrogram characteristics. Higher ICL values indicated areas with
rapid repetitive activities (high DF values), whereas the lower ACI
values indicated areas with continuous fractionation with more
disorganized signals (low HI values).
References
1. Camm AJ, Kirchhof P, Lip GY, Schotten U, Savelieva I, et al. (2010) Guidelines
for the management of atrial fibrillation: the Task Force for the Management
of Atrial Fibrillation of the European Society of Cardiology (ESC). Eur Heart J
31: 2369-2429.
4. Haissaguerre M, Sanders P, Hocini M, Hsu LF, Shah DC, et al. (2004)
Changes in atrial fibrillation cycle length and inducibility during catheter
ablation and their relation to outcome. Circulation 109: 3007-3013.
5. Lin YJ, Kao T, Tai CT, Chang SL, Lo LW, et al. (2008) Spectral analysis
during sinus rhythm predicts an abnormal atrial substrate in patients with
paroxysmal atrial fibrillation. Heart Rhythm 5: 968-974.
6. Grzeda KR, Noujaim SF, Berenfeld O, Jalife J (2009) Complex fractionated
atrial electrograms: properties of time-domain versus frequency-domain
methods. Heart Rhythm 6: 1475-1482.
7. Nademanee K, McKenzie J, Kosar E, Schwab M, Sunsaneewitayakul B, et
al. (2004) A new approach for catheter ablation of atrial fibrillation: mapping
of the electrophysiologic substrate. J Am Coll Cardiol 43: 2044-2053.
8. Tsai WC, Lin YJ, Tsao HM, Chang SL, et al. (2009) The optimal automatic
algorithm for the mapping of complex fractionated atrial electrograms in
patients with atrial fibrillation. J Cardiovasc Electrophysiol.
9. Tsai WC, Wang JH, Lin YJ, Tsao HM, Chang SL, et al. (2012) Consistency
of the Automatic Algorithm in Detecting Complex Fractionated Electrograms
using an Electroanatomical Navigation System. Pacing Clin Electrophysiol
35: 980-989.
2. Aldhoon B, Melenovsky V, Peichl P, Kautzner J (2010) New insights into
mechanisms of atrial fibrillation. Physiol Res 59: 1-12.
10.Lin YJ, Tai CT, Kao T, Tso HW, Higa S, et al. (2006) Frequency analysis in
different types of paroxysmal atrial fibrillation. J Am Coll Cardiol 47: 14011407.
3. Haissaguerre M, Jais P, Shah DC, Takahashi A, Hocini M, et al. (1998)
Spontaneous initiation of atrial fibrillation by ectopic beats originating in the
pulmonary veins. N Engl J Med 339: 659-666.
11.Lin YJ, Tai CT, Kao T, Chang SL, Lo LW, et al. (2009) Spatiotemporal
organization of the left atrial substrate after circumferential pulmonary vein
isolation of atrial fibrillation. Circ Arrhythm Electrophysiol 2: 233-241.
Author Affiliation
Top
Division of Cardiology, Tzu-Chi General Hospital, Hualien, Taiwan
Institute of Medical Sciences, Tzu-Chi University, Hualien, Taiwan
3
Division of Cardiology, Department of Medicine, Taipei Veterans General
Hospital, Taipei, Taiwan
4
Institute of Clinical Medicine and Cardiovascular Research Institute, National
Yang-Ming University, Taipei, Taiwan
1
2
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