Dominant Frequency, Regularity and Organization Indexes Response
to Preprocessing Filter Variations on Simulated Electrograms
During Atrial Fibrillation
Andr´es Orozco-Duque
1
, Juan P. Ugarte
1
, Catalina Tob´on
2,3
, Carlos A. Morillo
4
, Javier Saiz
2
and John Bustamante
1
1
Centro de Bioingenier´ıa, Universidad Pontificia Bolivariana, Medell´ın, Colombia
2
Grupo de Bioelectr´onica, i3BH, Universidad Polit´ecnica de Valencia, Valencia, Spain
3
Instituto Tecnol´ogico Metropolitano, Medell´ın, Colombia
4
Arrhythmia & Pacing Service, Department of Medicine, McMaster University-HHSC-PHRI, Hamilton, Canada
Keywords:
Atrial Fibrillation, Dominant Frequency, Electrograms, Organization Index, Regularity Index, Digital Filters.
Abstract:
Atrial fibrillation (AF) is the most common tachyarrhythmia. An accurate AF diagnosis is based on electro-
grams (EGM) interpretation. In this work, we evaluated different preprocessing filters to calculate dominant
frequency (DF), organization index (OI) and regularity index (RI) for EGM during AF, this comparative analy-
sis has not been reported previously. EGM were obtained from AF simulated by a three-dimensional model of
human atria. The preprocessing stage with a low-pass filter, rectification and high pass filter was implemented.
Additionally, a test over 60 simulated signals using different filters with different orders was developed. It was
found that some filters affect the DF calculation and cause a bimodal distribution in the DF histogram. Cal-
culation of DF, OI and RI by Fast Fourier Transform analysis of preprocessing signals has high sensibility to
filters settings. OI and RI analysis works properly in EGM with single potentials, however, DF is not necessary
related with cycle length in fragmented EGM and OI calculation becomes inaccurate with high DF.
1 INTRODUCTION
Atrial fibrillation (AF) affects about 2% of the pop-
ulation and its incidence is increasing. The AF is
characterized by rapid and chaotic electrical activity
over the atria without the ability to generate effec-
tive atrial contraction. The accurate AF diagnosis is
based on the interpretation of electrograms (EGM)
recroded from different atrial zones. Cardiovertion,
antiarrhythmic drugs and radiofrequency ablation are
used like therapeutic methods in AF. Ablation could
be improved with techniques of cardiac mapping ob-
tained from intracardiac EGM recorder on atrial tissue
in order to find areas with arrhythmogenic substrate.
Thus, it is necessary to found a relationship between
electrical propagation and EGM; however this is not
entirely resolved.
Recent studies focus on the location of complex
fractionated atrial electrograms (CFAE) as AF sub-
strates looking for the ablation of these areas, spe-
cially for chronic AF, however nowadays this is an
open problem. In order to find the relation between
EGM and electrical propagation, some indexes
have been developed, attempting to measure spatio-
temporal organizationof AF, which is related to repet-
itive morphology and high periodicity of the signals.
To measure the extent of spatial organization of
atrial activation during AF, ()Botteron1995 proposed
a preprocessing method for EGM. This is described
as follows: First, Band-passed filtering with digital,
zero phase, third-orderButterworth filter with cut-offs
of 40-250Hz. Second, Absolute value (rectification).
Third, Low-pass filter with Butterworth filter with
cut-off of 20 Hz. This method has been used since
1995 to calculate different indexes: the dominant fre-
quency (DF), organization index (OI) and regularity
index (RI) (()Everett2001). (Everett et al., 2001) de-
termined that to improve the Fast Fourier Transform)
FFT performance in the preprocessing stage is neces-
sary to use 4s or greater windowed signals.
DF, OI and RI are currently being used in clin-
ical practice. However different authors have dis-
cussed the practical use of these indexes, because they
are sensitive to the method of recording, signal-to-
306
Orozco-Duque A., P. Ugarte J., Tobón C., A. Morillo C., Saiz J. and Bustamante J..
Dominant Frequency, Regularity and Organization Indexes Response to Preprocessing Filter Variations on Simulated Electrograms During Atrial
Fibrillation.
DOI: 10.5220/0004231903060309
In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2013), pages 306-309
ISBN: 978-989-8565-36-5
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
noise ratio (SNR), far field ventricular depolarization
or complexity of the signal ((Elvan et al., 2009)). In
this work, the sensitivity of these indexes was studied
when the filters are setting in different ways in pre-
processing stage and DF, OI and RI calculation was
analyzed during CFAE.
2 METHODS
2.1 Atrial Fibrillation and Electrograms
A chronic AF episode was simulated in an anatomi-
cally realistic 3D model of human atria that includes
fibers orientation, electrophysiological heterogeneity
and anisotropy. Unipolar atrial EGM at 60 points of
the left atrial surface (0.2 mm from the surface) were
simulated each millisecond during 8 seconds (Tob´on
et al., 2010).
2.2 Dominant Frequency, Organization
and Regularity Indexes
The DF was calculated using the definition given by
(Everett et al., 2001), DF is the maximum peak in
the power spectrum after apply a FFT with hamming
window to the output of preprocessing stage define by
(Botteron and Smith, 1995).
OI (Everett et al. 2001) is the ratio between the
area under DF ±0.75Hz including the areas under
harmonic frequencies to DF between 3 to 20Hz and
the total area as shown in the following equation:
OI =
A(DF ± 0.75) +
3
i=2
A[(DF i) ± 0.75]
A(3 Hz to 20 Hz)
(1)
where A is a function that calculates the area and
the harmonics are include only if the peak is less than
20Hz.
We use the RI index defined by (Sanders et al.,
2005) like the ratio of spectral power in a 0.75Hz band
centered at the DF and the total power between 3Hz
and 20Hz.
3 RESULTS
The preprocessing method was tested with four differ-
ent types of filters, with different orders, implemented
in MatLab
R
. Butterworth, Chebyshev 1, Chebyshev
2 and elliptic filters were used. Low-pass filters be-
tween 2nd-order to 20th-order and band-pass filters
between 4th-order to 40th-order were used. In each
combination the band-pass order was twice the order
of low-pass filter.
Figure 1 shows the histogram of DF calculation
when were applied 76 combinations filters to the 60
simulated signals. The histogram shows that DF are
mainly concentrated between 5.5 Hz and 6.5 Hz, how-
ever there are results between 11 and 13 Hz wich cor-
respond to DF of five signals with high degree of frag-
mentation and with some specific filters.
4 6 8 10 12 14
0
20
40
60
80
Frequency (Hz)
Repetitions
Figure 1: General histogram of DF calculation with76 com-
bination filter applied to 60 EGM signals.
Figure 2 shows four AF signals with their respec-
tive spectrum and DF histogram. The signal in Fig-
ure 2(a) is a regular EGM with single potential, DF
calculation had the same result for each combination
of filters, as seen in its histogram. Figure 2(b) shows
two DF results depending on the used filters, however,
the difference between them is barely 0.33 Hz. Fig-
ure 2(c) and 2(d) are CFAE signals corresponding to
particulars combination of filter resulted in high DF,
greater than 10Hz. Their power spectrums correspond
to the set with high DF. These results show that re-
sponse of DF analysis on CFAE signals is sensible to
filters configuration.
2nd-order filters or higher than 6th-order filters in
the low pass stage results in low DF in CFAE sig-
nals, consequently there is no relation between DF
and CFAE signals. Also the relation between DF and
cycle length is not clear. 3rd-Order and 4th-order But-
terworth, Chebyshev 1 and Chebyshev 2 filters pro-
duced high DF in CFAE signals, which is appropri-
ated if it is looking for a relation between DF and
CFAE.
Table 1 shows the DF, OI and RI means for all
filter combinations applied to signals showed in fig-
ure 2. EGM 2(c) and EGM 2(d) were separated in
two sets, according to DFs value showed in their his-
tograms. The variation of DF values yields a varia-
tion of OI and RI. In signals where DF > 10 Hz are
not possible to calculate OI because the harmonicsare
greater than 20Hz.
Figure 3(b) shows the power spectrum of the pre-
processing EGM for an AF signal with CFAE using
DominantFrequency,RegularityandOrganizationIndexesResponsetoPreprocessingFilterVariationsonSimulated
ElectrogramsDuringAtrialFibrillation
307
0 0.5 1 1.5 2 2.5 3 3.5 4
−5
0
5
(a)
T ime (s ec)
Amp . ( mV)
0 2 4 6 8 10 12 14 16 18 20
0
0.05
0.1
Fr e que nc y (Hz )
Powe r
0 2 4 6 8 10 12 14
0
20
40
60
80
Fr e que nc y (Hz )
Re p et i ti ons
0 0.5 1 1.5 2 2.5 3 3.5 4
−5
0
5
(b)
T ime (s ec)
Amp . ( mV )
0 2 4 6 8 10 12 14 16 18 20
0
0.02
0.04
0.06
Fr e que nc y (Hz )
Powe r
0 2 4 6 8 10 12 14
0
20
40
60
Fr e que nc y (Hz )
Re p et i ti ons
0 0.5 1 1.5 2 2.5 3 3.5 4
−10
−5
0
5
(c)
T ime (s ec)
Amp . ( mV )
0 2 4 6 8 10 12 14 16 18 20
0
0.02
0.04
0.06
Fr e que nc y (Hz )
Powe r
0 2 4 6 8 10 12 14
0
20
40
60
Fr eq u en c y ( H z )
Re p et i ti ons
0 0.5 1 1.5 2 2.5 3 3.5 4
−10
−5
0
5
(d)
T ime (s ec)
Amp . ( mV )
0 2 4 6 8 10 12 14 16 18 20
0
0.02
0.04
Fr e que nc y (Hz )
Powe r
0 2 4 6 8 10 12 14
0
20
40
60
Fr e que nc y (Hz )
Re p et i ti ons
Figure 2: Four different EGM, their respective power spectrum and histogram.
Table 1: DF, OI and RI for signals showed in figure 2.
DF (Hz) OI RI
EGM 3a 5.98 0.45 0.35
EGM 3b 6.06 0.43 0.35
EGM 3c-set1 11.72 0.17
EGM 3c-set2 5.48 0.36 0.18
EGM 3d-set1 5.66 0.34 0.15
EGM 3d-set2 11.84 0.14
a 2th-order Butterworth filter and Figure 3(c), a 4th-
order Butterworth filter. Figure 3 indicate the sensi-
tivity of the power spectrum when it was changed the
order of the filter. This causes variations in the max-
imum peak affecting DF, OI and RI values presented
in Table 2.
Table 2: DF, OI and RI for signal showed in figure 3.
Butterworth DF (Hz) OI RI
2nd-order filter 6.35 0.26 0.14
4th-order filter 13.06 0.12
4 DISCUSSION
Several authors have used different filters in the pre-
processing stage, (Everett et al., 2001) used a 2nd-
order butterworth filters. (Barbaro et al., 2000) used
40th-order FIR filters, (Nguyen et al., 2009), other
authors used the pre-processing method defined by
(Botteron and Smith, 1995) but they did not define
the order and type of filters. The present work has
evidenced for the first time the importance of estab-
lishing the type and the order of the band-pass and
low-pass filters in the pre-processing stage.
DF definition is based in the results of (Skanes
et al., 1998) who concluded that DF is determined by
the cycle length of AF spatio-temporal periodic activ-
ity. DF greater than 10 Hz resulted from CFAE has
no physiological meaning related with cycle length,
since high frequencies could be related with the com-
plexity of the signal. This is supported by other au-
thors who showed that there is a poor correlation be-
tween the EGM cycle length and DF in patients with
persistent AF ((Elvan et al., 2009)). (Ng et al., 2007)
demonstrated that although DF analysis can be reli-
ably, used to measure local activation rate in ideal sit-
uations, several factors can contribute to the unpre-
dictability of DF analysis related to recording tech-
niques and signal properties. They suggested the use
of well defined signal processing techniques and care-
ful evaluation of the time domain signals.
In CFAE, independently of the filter used in the
preprocessing stage, DF is not necessary related with
cycle length. Depending on filters settings, DF could
be related or not with complexity of the EGM, in any
case the DF has no correlation with morphology of
signals. DF by itself could not make any difference
between a CFAE and a high frequencyEGM with sin-
gle potentials. RI defined by (Sanders et al., 2005) as
an auxiliary index to ensure reliability in DF detec-
tion, such that DF calculation is accepted only if RI
> 0.2 in order to reject signals with poor SNR. How-
ever, despite of using simulated free-noise EGM in
present work, results gave RI < 0.2 in some cases,
which correspond to signals with CFAEs. The OI was
defined by (Everett et al., 2001) who showed that this
index has a correlation with the periodicity and mor-
phology of AF signal. However, in CFAE where DF
> 10 Hz, It is not possible to calculate OI because the
harmonics are greater than 20Hz.
As a future work we are testing DF, OI and RI on
different simulated and real tachyarrhythmia signals.
Additionally we are developing other indexes corre-
lated with the morphology of the signals to character-
ize CFAE.
BIOSIGNALS2013-InternationalConferenceonBio-inspiredSystemsandSignalProcessing
308
0 0.5 1 1.5 2 2.5 3 3.5 4
−10
−5
0
5
(a)
Ti me (sec)
Am p. (m V)
0 2 4 6 8 10 12 14 16 18 20
0
0.01
0.02
0.03
0.04
(b)
Frequency ( Hz )
Powe r
FD=6.34
0 2 4 6 8 10 12 14 16 18 20
0
0.01
0.02
0.03
0.04
(c)
Frequency ( Hz )
Powe r
FD=13.06
Figure 3: (a). EGM. (b). EGM power spectrum using 2-order Butterworth filters. (c). signal power spectrum using 4-order
Butterworth filters.
5 CONCLUSIONS
Calculation of DF, OI and RI by FFT analysis of
preprocessing signal using the (Botteron and Smith,
1995) method has high sensitivity to filters settings.
Our results suggest that it is necessary to specify the
filter configuration used in the pre-processing stage in
order to avoid ambiguous results. OI and RI analysis
works properly in EGM with single potentials, how-
ever, DF is not necessary related with cycle length in
CFAE while OI calculation has problems with high
DFs. In order to study CFAE we recommend keep
researching and developing of other analysis tools.
REFERENCES
Barbaro, V., Bartolini, P., Calcagnini, G., Censi, F.,
Michelucci, a., and Morelli, S. (2000). A high-
temporal resolution algorithm to quantify synchro-
nization during atrial brillation. Proceedings of
the 22nd Annual International Conference of the
IEEE Engineering in Medicine and Biology Society.,
4:2959–2962.
Botteron, G. W. and Smith, J. M. (1995). A technique for
measurement of the extent of spatial organization of
atrial activation during atrial brillation in the intact
human heart. IEEE transactions on bio-medical engi-
neering, 42(6):579–86.
Elvan, A., Linnenbank, A. C., van Bemmel, M. W., Misier,
A. R. R., Delnoy, P. P. H. M., Beukema, W. P., and
de Bakker, J. M. T. (2009). Dominant frequency of
atrial fibrillation correlates poorly with atrial fibrilla-
tion cycle length. Circulation. Arrhythmia and elec-
trophysiology, 2(6):634–44.
Everett, T. H., Kok, L. C., Vaughn, R. H., Moorman, J. R.,
and Haines, D. E. (2001). Frequency domain algo-
rithm for quantifying atrial fibrillation organization to
increase defibrillation efficacy. IEEE transactions on
bio-medical engineering, 48(9):969–78.
Ng, J., Kadish, A. H., and Goldberger, J. J. (2007). Tech-
nical considerations for dominant frequency analy-
sis. Journal of Cardiovascular Electrophysiology,
18(7):757–764.
Nguyen, M., Schilling, C., and D¨ossel, O. (2009). A
new approach for frequency analysis of complex frac-
tionated atrial electrograms. Conference proceed-
ings : 2009 Annual International Conference of the
IEEE Engineering in Medicine and Biology Society.,
2009:368–71.
Sanders, P., Berenfeld, O., Hocini, M., Ja¨ıs, P.,
Vaidyanathan, R., Hsu, L.-F., Garrigue, S., Takahashi,
Y., Rotter, M., Sacher, F., Scav´ee, C., Ploutz-Snyder,
R., Jalife, J., and Ha¨ıssaguerre, M. (2005). Spec-
tral analysis identifies sites of high-frequency activity
maintaining atrial fibrillation in humans. Circulation,
112(6):789–97.
Skanes, a. C., Mandapati, R., Berenfeld, O., Davidenko,
J. M., and Jalife, J. (1998). Spatiotemporal periodic-
ity during atrial fibrillation in the isolated sheep heart.
Circulation, 98(12):1236–1248.
Stiles, M. K., Brooks, A. G., Kuklik, P., John, B., Dim-
itri, H., Lau, D. H., Wilson, L., Dhar, S., Roberts-
Thomson, R. L., Mackenzie, L., Young, G. D., and
Sanders, P. (2008). The relationship between electro-
gram cycle length and dominant frequency in patients
with persistent atrial fibrillation. Journal of cardio-
vascular electrophysiology, 20(12):1336–1342.
Tob´on, C., Ruiz, C., Rodr´ıguez, J., Hornero, F., Ferrero, J.
M. J., and S´aiz, J. (2010). Anatomical realistic 3D
model of human atria. Application to the study of vul-
nerability to atrial arrhythmias. Supplement to Heart
Rhythm, (75S):S289–S290.
DominantFrequency,RegularityandOrganizationIndexesResponsetoPreprocessingFilterVariationsonSimulated
ElectrogramsDuringAtrialFibrillation
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