![](bg4.png)
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 fibrillation. 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 fibrillation 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.
DominantFrequency,RegularityandOrganizationIndexesResponsetoPreprocessingFilterVariationsonSimulated
ElectrogramsDuringAtrialFibrillation
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