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Technical Considerations for Dominant Frequency AnalysisJASON NG, PH.D., ALAN H. KADISH, M.D., and JEFFREY J. GOLDBERGER, M.D.

From the Bluhm Cardiovascular Center and the Division of Cardiology, Feinberg School of Medicine, Northwestern University,Chicago, Illinois, USA

Considerations for Dominant Frequency Analysis. Introduction: Dominant frequency (DF) anal-ysis of atrial electrograms has been used to characterize atrial fibrillation (AF). The aim of this study was toexplore technical issues that may affect the estimation of local activation rate during AF using DF analysis.

Methods and Results: Epicardial atrial electrograms recorded during AF from 10 dogs were used toevaluate the effects of unipolar versus bipolar recordings, bipolar electrode spacing, postrecording pro-cessing, far field ventricular depolarizations, ventricular template subtraction, and signal duration on DFanalysis. Simulated electrograms were used to evaluate the effect of far field ventricular depolarizationsand signal-to-noise ratio. DFs were compared with activation rates obtained by manual marking and thereproducibility of the DFs was evaluated. Bipolar electrograms were found to be preferable to unipolarelectrograms. Preprocessing was a necessary step for bipolar signals, but also aided analysis of unipolarrecordings. Ventricular far field depolarizations significantly affected DFs. Ventricular template subtrac-tion helped DF analysis in signals with both minimal and significant ventricular components. A recordingduration above 2 seconds was required for reliable DF measurements. Signal-to-noise ratios below 13 dBcould also affect DF, particularly for signals with significant amplitude and frequency variation.

Conclusions: Various factors affect DF analysis. Proper interpretation of DF analysis requires carefulevaluation of the AF signals and robust processing techniques. (J Cardiovasc Electrophysiol, Vol. 18, pp.757-764, July 2007)

spectral analysis, dominant frequency, fibrillation, arrhythmia, electrograms

Introduction

To better understand the pathophysiology of atrial fibril-lation (AF), new approaches to characterizing AF are neces-sary. One such approach is to map activation rates throughoutthe atria; sites with high frequency activation may serve asdrivers of AF. An alternative approach is to use spectral anal-ysis of endocardial or epicardial recordings, specifically forthe estimation of atrial activation rates during AF.1-10Atrialactivation rate can be estimated as the frequency containingmaximum power in the frequency domain, often referred toas “dominant frequency” or “DF.” The ability of DF analysisto reflect the atrial activation rate is dependent on how wellthe electrogram signal can be approximated by a single sinusoidal function with a frequency equal to the activation rate.

Often, situations arise that are less than ideal for DF anal-ysis to be performed. We have previously shown that prop-erties of the atrial activation, such as variability of amplitudeand frequency of the electrogram recording and the com-plexity of the electrogram morphology, could alter the powerspectrum so that the DF is more reflective of the complexityof the signal than the atrial activation rate.11 Beyond thesefactors intrinsic to AF, there are a variety of technical fac-tors that likely affect DF. DF analysis has been applied in avariety of recording modalities, such as bipolar and unipolarrecordings from endocardial and epicardial electrodes,1,6-9

Address for correspondence: Jeffrey J. Goldberger, M.D., Division ofCardiac Electrophysiology, Northwestern University Feinberg School ofMedicine, 251 East Huron, Feinberg Pavilion, Chicago, IL 60611. Fax: 312-926-2707; E-mail: j-goldberger@northwestern.edu

Manuscript received 11 October 2006; Revised manuscript received6 February 2007; Accepted for publication 12 February 2007.

doi: 10.1111/j.1540-8167.2007.00810.x

optical mapping,2-5 and noncontact mapping.10 Recordingsfrom each of these modalities have their own unique signalcharacteristics that may not provide equivalent results whenDF analysis is applied. The quality of the electrogram signalsmay vary as electrode contact and recording location may af-fect the signal-to-noise ratio (SNR) and the presence of farfield ventricular components. As this technique has clinicalsignificance with regard to understanding the pathophysiol-ogy of AF, it is important to evaluate the technical factors thatcould affect the results of DF analysis. In this study, we eval-uated the optimal settings and methodology, such as record-ing mode and signal processing, for performing DF analysisto obtain reliable and reproducible estimation of activationrates.

Methods

Study Design

The technical issues relating to the recording of the sig-nal (i.e., unipolar vs. bipolar, interelectrode spacing) andpostrecording processing were studied. Properties relatingto the signal quality, such as SNR and the presence of farfield ventricular depolarizations were also studied. Each ofthese factors was evaluated using either actual electrogramsor by computer simulation, as indicated.

Determining DF

A series of standard signal processing operations is re-quired in DF analysis of digitally recorded signals.12-14 Thesignal processing steps used for the DF calculation of signalsin this study were implemented with MATLAB (Mathworks,Inc., Natick, MA, USA) and include:

1. Bandpass filtering at 40–250 Hz

758 Journal of Cardiovascular Electrophysiology Vol. 18, No. 7, July 2007

2. Absolute value (i.e., rectification)3. Lowpass filtering at 20 Hz4. Windowing (Hanning)5. Zero padding to obtain a 10-second signal (frequency

resolution of 0.1 Hz)6. Fast Fourier transform7. Power spectrum

DF is then detected as the frequency with the maximumpower in the power spectrum.

The Influence of Mode of Recording on DF

To explore the influences of recording and processingmethods on DF analysis, signals during AF induced by burstpacing in 10 normal mongrel dogs were analyzed. Unipo-lar epicardial electrograms were recorded using rectangularelectrode arrays (8 × 14) sutured on the right and left atrialappendages. The inter-electrode distance of the array was2.5 mm. Recordings from the 224 unipolar leads and the sur-face ECG limb leads were amplified and stored in analogform on videotape, using a cardiac mapping system (MapTech, Maastricht, The Netherlands), from which digital fileswere later acquired for offline analysis. AF episodes of atleast 16 seconds were chosen for analysis. Four consecutive4-second duration signals were recorded and sampled at 1kHz with bandpass filtering at 1 and 500 Hz.

A set of three adjacent signals from the same row or col-umn was obtained from each dog. The unipolar electrogramswere chosen to have minimal far field ventricular depolariza-tions. Examples of these signals are shown in the first column

Figure 1. Two sets of atrial electrograms,with one set having minimal far field ventricu-lar depolarizations (left column) and one hav-ing significant far field ventricular depolar-izations (right column). The unipolar signalsA, B, and C, were obtained from sites 2.5 mmapart. The derived signals A–B and B–C arebipolar recordings with 2.5 mm spacing andthe signals A–C are bipolar recordings with5.0 mm spacing. V = ventricular electrogram.

of Figure 1. Manual marking of the unipolar signals at thepoint of the maximum negative slope was performed. Theactivation rate for each signal was calculated as the mean ofthe reciprocal of the cycle lengths.

The bipolar electrograms were created by taking the dif-ference of two unipolar recordings and then applying a30-Hz highpass filter, which is commonly used for intrac-ardiac recording of bipolar signals. Thus, from each groupof three unipolar recordings, two bipolar recordings of 2.5mm spacing and one bipolar recording of 5 mm spacing wereobtained.

The Influence of Far Field VentricularDepolarizations on DF

Far field ventricular depolarization may be recorded ata number of sites within the atrium including near the an-nulus, coronary sinus, appendage, and pulmonary veins. Toexamine the influence of far field ventricular depolarizationson DF analysis, a second set of electrograms was obtainedfrom the canine AF recordings previously described. Signalswere selected that had ventricular components with ampli-tudes greater than that of the atrial signal both in the unipo-lar and bipolar recordings (example in the second columnof Fig. 1). Manual marking of the unipolar signals was per-formed and activation rate was computed as described above,but the ventricular depolarization, easily identified in the timedomain signals, was not marked.

Simulated signals were also used to study the effectsof far field ventricular depolarizations. Two sets of sim-ulated atrial signals were generated: one with constant

Ng et al. Considerations for Dominant Frequency Analysis 759

rate (equivalent to a DF = 6.5 Hz) and one with variablerate (equivalent to mean DF of 6.0 ± 1.0 Hz). Simulated ven-tricular electrograms with cycle lengths ranging from 500to 1000 ms and amplitudes ranging from 0.5 to 4 times theatrial amplitude were superimposed to the simulated atrialsignals. We identified the situations where the DF of the sig-nals with added ventricular components were different fromthose of the signals before the ventricular components wereadded.

The Evaluation of Ventricular TemplateSubtraction for DF Analysis

Subtraction techniques to remove ventricular componentsof atrial recordings have been described.15,16 We aimed toevaluate the effects of cancellation on unipolar and bipolarsignals with both minimal and significant far field ventriculardepolarizations. In this analysis, the algorithm first detectedventricular activations from the QRS complexes of a simul-taneous surface ECG lead using the steepest negative slopeof the QRS as the fiducial point. Next, a median ventricularcomplex for each of the unipolar and bipolar recordings wascalculated. The minimum RR interval was chosen as the win-dow size for creating the template. The onset was defined as30% of the RR interval before the QRS fiducial point, whereasthe offset was defined as 70% of the RR interval after the QRSfiducial point, so the window will include the T wave. Lastly,a remainder signal was generated by subtracting the mediancomplex aligned to the ventricular complexes of the originalsignal. These steps are illustrated in Figure 2. DF was deter-mined for the processed and unprocessed remainder signal.

The Effect of Signal Duration on DF

The DF for each of the 4-second signals was calculated. Inaddition, each of the 4-second signals was divided into two2-second segments and four 1-second segments. DFs werethen calculated for the total eight 2-second and 16 one-secondsegments.

The Influence of SNR on DF Analysis

To study the effects of noise, a representative bipolar acti-vation deflection was replicated under different conditions ofamplitude and frequency variation. These conditions includeconstant amplitude and constant frequency (6 Hz), variableamplitude and constant frequency, constant amplitude andvariable frequency (mean activation rate of 6 ± 1 Hz), andvariable amplitude and frequency. The first two conditionsmight be noted at the site of a high frequency driver. The lat-ter condition is typical for recordings of AF. Gaussian whitenoise was added to the signals to obtain SNRs from −40 to40 dB at 1 dB steps (decibel measurements of −40, −20, 0,20, and 40 dB correspond to SNRs of 1/100, 1/10, 1, 10, and100, respectively, in a logarithmic scale). Each simulationat each SNR was repeated 100 times with a new randomlygenerated noise signal. For each SNR, the mean absolute dif-ference between the DF of the signal with noise and the signalwithout noise was calculated. To evaluate the frequency andextent of significant differences in DF, we identified the sig-nals with noise whose DF differed from the DF of the signalwithout noise by greater or equal to 0.1 Hz (called “non-zerodifferences”). The mean absolute difference between the DF

Figure 2. Illustration of ventricular template subtraction for unipolar elec-trograms with significant far field ventricular depolarizations. The upperplot shows the surface ECG with the QRS complexes marked at the point ofmaximum negative slope. The middle plot shows the simultaneous unipolarelectrogram with significant ventricular depolarizations. The bottom plotshows the remainder unipolar electrogram following ventricular templatesubtraction.

of these signals with noise and the signals without noise wasalso calculated.

Statistical Analysis

Data are reported as mean ± standard deviation. Pairedand unpaired data were compared using Student’s t test. Re-producibility of the measurements was measured with theintraclass correlation coefficients (ICC). A P value <0.05was considered statistically significant.

Results

Using the time domain markings of the 4-second unipo-lar recording, the computed activation rates were a mean of9.4 ± 1.3 Hz over all episodes from each dog. The activationrates also demonstrated high reproducibility over the foursegments with an ICC of 0.91. After the pre-FFT process-ing of bandpass filtering, rectification, and low pass filtering,the mean absolute difference between DF and activation ratewas 0.3 ± 0.7 Hz for the unipolar recordings, 0.3 ± 0.7 Hzfor the 2.5 mm bipolar recordings, and 0.3 ± 0.7 Hz (P <0.0001) for the 5 mm bipolar recordings. ICC was 0.45 forthe unipolar recordings, 0.45 for the 2.5 mm, and 0.46 for the5 mm bipolar recordings.

Effect of Far Field Ventricular Depolariztions on DF

Far field ventricular depolarizations were shown to sig-nificantly affect DF analysis (Table 1). The recordings hadmean RR intervals of 285 ± 92 ms. The mean absolute

760 Journal of Cardiovascular Electrophysiology Vol. 18, No. 7, July 2007

TABLE 1

Absolute Difference Between the DFs (Frequency Domain) and MeanActivation Rates (Time Domain) With Minimal and Significant Far Field

Ventricular Depolarizations (FFVD)

Type Minimal FFVDs Significant FFVDs

Unipolar (Hz) 0.3 ± 0.7 (0.45) 2.4 ± 2.5 (0.23)∗Bipolar (2.5 mm) (Hz) 0.3 ± 0.7 (0.45) 2.0 ± 2.4 (0.26)∗Bipolar (5.0 mm) (Hz) 0.3 ± 0.7 (0.46) 2.1 ± 2.6 (0.19)∗∗

∗P < 0.0001 vs. minimal FFVDs,∗∗P < 0.0002 vs. minimal FFVDs.Intra-class correlation coefficients of the DF are shown in parentheses.

difference between DF and activation rate was increased from0.3 ± 0.7 to 2.4 ± 2.5 Hz (P < 0.0001) for the unipolar record-ings, from 0.3 ± 0.7 to 2.0 ± 2.4 Hz (P < 0.0001) for the2.5 mm bipolar recordings, and from 0.3 ± 0.7 to 2.1 ± 2.6Hz (P < 0.0002) for the 5 mm bipolar recordings. The ICCwas decreased when significant far field ventricular depolar-izations were present in the unipolar recordings (from 0.45to 0.23) and in the 2.5 and 5 mm bipolar recordings (from0.45 to 0.26 and from 0.46 to 0.19, respectively).

The DFs from the simulated signals with constant atrialrate were not affected by the added ventricular componentswhen the amplitudes of the ventricular components relativeto the atrial amplitudes were less than or equal to one. Whenrelative amplitude was above one, changes in DF were ob-

Figure 3. Examples of simulated atrial elec-trograms and far field ventricular depolariza-tions with the corresponding power spectrumand dominant frequency. A: Constant rateatrial electrograms with no ventricular com-ponents. B: Constant rate atrial electrogramswith ventricular components (cycle length of500 ms and relative amplitude of 3). C: Con-stant rate atrial electrograms with ventricularcomponents (cycle length of 600 ms and rela-tive amplitude of 1.5). D: Variable rate atrialelectrograms with no ventricular components.E: Variable rate atrial electrograms with ven-tricular components (cycle length of 600 msand relative amplitude of 0.5).

served in two different ways. In some cases, DFs were ob-served at double the original DF (13 Hz vs. 6.5 Hz). Thissituation was more likely to happen at higher relative am-plitudes. Ventricular rate was not a factor; rather the timingof the ventricular components was important in causing theharmonic frequency to dominate. Similar effects were seenin our previous study of the effect of double potentials on DF.A second scenario of DF change was when a harmonic of theventricular rate rather than of the atrial rate dominated. Inthese cases, DFs were lower than the original DFs (4.0, 3.3,3.8, and 3.3 Hz for ventricular cycle lengths of 500, 600, 800,and 900 ms, respectively). These situations are illustrated inFigure 3A–C.

For the simulation where the atrial rate was variable, theDF was 6.0 Hz without clear harmonics, but multiple peaksnear the DF before ventricular components were added. DFwas sensitive to the added ventricular components with onlya relative amplitude of 0.5 needed to affect the DF. DFs of 6.6,7.1, 6.5, and 6.6 Hz were found for ventricular cycle lengthsof 600, 700, 800, and 900 ms, respectively. Ventricular cyclelengths of 500 and 1000 ms did not have an effect. Thesesituations are illustrated in Figure 3D,E.

Effect of Ventricular Template Subtraction on DF

Performing ventricular template subtraction helped lessenthe effect of the far field ventricular depolarizations(Table 2). For the signals with significant far field ventricular

Ng et al. Considerations for Dominant Frequency Analysis 761

TABLE 2

Absolute Difference Between the DFs (Frequency Domain) and Mean Activation Rates (Time Domain) With and Without Ventricular Template Subtraction

Type No Subtraction With Subtraction

Unipolar–minimal FFVDs (Hz) 0.3 ± 0.7 (0.45) 0.3 ± 0.8 (0.44)Bipolar (2.5 mm)–minimal FFVDs (Hz) 0.3 ± 0.7 (0.45) 0.2 ± 0.3 (0.90)Bipolar (5.0 mm)–minimal FFVDs (Hz) 0.3 ± 0.7 (0.46) 0.3 ± 0.4 (0.93)Unipolar–significant FFVDs (Hz) 2.4 ± 2.5 (0.23) 1.2 ± 1.4 (0.16)∗Bipolar (2.5 mm)–significant FFVDs (Hz) 2.0 ± 2.4 (0.26) 0.5 ± 0.9 (0.60)∗∗Bipolar (5.0 mm)–significant FFVDs (Hz) 2.1 ± 2.6 (0.19) 0.5 ± 0.7 (0.65)∗∗∗

∗P < 0.0001 vs. no subtraction, ∗∗P < 0.001 vs. no subtraction, ∗∗∗P < 0.0003 vs. no subtraction.Intra-class correlation coefficients of the DF are shown in parentheses.

depolarizations, template subtraction reduced the absolutedifferences from 2.4 ± 2.5 Hz to 1.2 ± 1.4 Hz (P < 0.0001)for the unipolar recordings, from 2.0 ± 2.4 Hz to 0.5 ± 0.9 Hz(P < 0.001) for the 2.5 mm bipolar recordings, and from 2.1± 2.6 Hz to 0.5 ± 0.7 Hz (P < 0.0003) for the 5 mm bipolarrecordings. Performing subtraction on the signal with min-imal ventricular far field components did not significantlyaffect DF analysis for the three modes of recording in termsof absolute difference from the activation rate. The ICC of theunipolar signals was also virtually unchanged from 0.45 be-fore subtraction to 0.44 after subtraction. However, the ICCsof the bipolar recordings were significantly improved from0.45 to 0.90 for 2.5 mm and from 0.46 to 0.93 for 5.0 mm.These results suggest that even small ventricular electrogramscan affect the DF on bipolar recordings and that subtractioncan improve DF analysis in this situation.

The Effect of Signal Duration on DF

The effect of signal duration on DF analysis was also ex-amined (Table 3). The ICCs for the activation rates of 4-second, 2-second, and 1-second segments were 0.90, 0.85,and, 0.81, respectively. Assuming ventricular template sub-traction and pre-FFT processing, the absolute differences be-tween DF and activation rate were not significantly differentbetween segments of 4-second duration and 2-second dura-tion in either the unpolar or bipolar configuration. However,the absolute differences between DF and activation rates werehigher for the 1-second segments than either the 2-second or4-second recordings for the unipolar signals (P < 0.07) andthe 2.5 mm bipolar signals (P < 0.02).

SNR

Figure 4 shows the results of the addition of Gaussianwhite noise to the simulated electrograms for the differentsituations of amplitude and frequency variation. The sim-

TABLE 3

Absolute Difference Between the DFs (Frequency Domain) and Mean Activation Rates (Time Domain) With Different Signal Durations

Type 1 second 2 seconds 4 seconds

Unipolar–minimal FFVDs (Hz) 0.5 ± 0.9 (0.54) 0.3 ± 0.5 (0.76) 0.3 ± 0.8 (0.44)Bipolar (2.5 mm)–minimal FFVDs (Hz) 0.4 ± 0.8 (0.62) 0.3 ± 0.3 (0.83) 0.2 ± 0.3 (0.90)Bipolar (5.0 mm)–minimal FFVDs (Hz) 0.4 ± 0.8 (0.70) 0.3 ± 0.3 (0.83) 0.3 ± 0.4 (0.93)Unipolar–significant FFVDs (Hz) 1.2 ± 1.7 (0.15) 1.0 ± 1.5 (0.23)∗ 1.2 ± 1.4 (0.16)∗Bipolar (2.5 mm)–significant FFVDs (Hz) 0.7 ± 1.1 (0.29) 0.5 ± 0.8 (0.53)∗∗ 0.5 ± 0.9 (0.60)∗∗Bipolar (5.0 mm)–significant FFVDs (Hz) 0.7 ± 1.2 (0.50) 0.5 ± 0.8 (0.47)∗∗ 0.5 ± 0.7 (0.65)∗∗

∗P < 0.07 vs. 1 second, ∗∗P < 0.02 vs. 1 second.Intra-class correlation coefficients of the DF are shown in parentheses.

ulated signals without frequency modulation had dominantfrequencies of 6.0 Hz without noise. For the signals withconstant amplitude and frequency, there were no changes inDF due to noise for SNRs greater than 0 dB. For the sig-nals with constant frequency but variable amplitude, therewere no changes in DF due to noise for SNRs greater than1 dB. Changes in DF were encountered more frequently asthe noise level increased (right panel of Fig. 4). The first dif-ference encountered for SNRs below 0 dB had a magnitudeof 6 Hz because the first harmonic was chosen as the DF.Increasing the noise led to other nonharmonic peaks in thespectrum (i.e., closer to the activation rate) that could domi-nate, which in turn led to both an increase in the probabilityof non-zero differences and a decrease in the mean absolutedifferences. Figure 5A,B shows two examples of the sim-ulated electrograms with constant amplitude and frequencyand added noise (SNR = −2 dB = 0.6). Example 5A hasa DF of 6 Hz, the same as the noiseless DF. Example 5Bhas a DF = 12 Hz, a harmonic frequency, despite having thesame SNR. Thus, identical noise levels may produce differentdominant frequencies. Thus, for any given noise level, thereis a probability that the DF will be altered by the noise.

For the signals with constant amplitude but variable fre-quency, the DF was 6 Hz without noise. For the signals withvariable amplitude and frequency, the DF was 6.5 Hz withoutnoise. For both sets of signals there were no DF changes dueto noise for SNRs greater than 13 dB (=4.5). The probabilityof differences in DF and the mean magnitude of the differ-ences both increased as SNRs were decreased from 13 dB.At the higher SNRs, peaks near the true activation rate werebeing selected, giving rise to only a small non-zero differ-ence. The likelihood of DFs further away from the actualactivation rate increased as noise levels increased. Selectionof harmonics as the DF was not a significant factor in theDF analysis of the frequency-modulated signal. The exam-ple in Figure 5C,D show the differing DFs of two signals with

762 Journal of Cardiovascular Electrophysiology Vol. 18, No. 7, July 2007

Figure 4. Results of the analysis of signal-to-noise ratios for the different conditions ofamplitude and frequency variation. The plotson the left show the mean absolute differencebetween the DF with noise and the DF with-out noise for the 100 simulations per signal-to-noise ratio. The solid line shows the to-tal mean absolute difference and the dottedline shows the mean absolute difference onlyfor the simulations where a non-zero differ-ence occurred. The plots on the right show theprobability of a non-zero difference in DF cal-culated from the 100 simulations per signal-to-noise ratio.

amplitude and frequency modulation and both have SNRs of10 dB (=3.1).

Discussion

Frequency analysis of electrograms during AF has been in-creasingly used to help understand the pathophysiology andmechanisms of the arrhythmia.1-10,17,18 In this report, wedemonstrate that although DF analysis can be reliably usedto measure local activation rate in ideal situations, severalfactors can contribute to the unpredictability of DF analysisrelated to recording techniques and signal properties. Thisstudy underscores the need for the use of well-defined sig-nal processing techniques and careful evaluation of the timedomain signals that are being processed.

Marking atrial activations on unipolar or bipolar electro-grams and measuring intervals is the optimal method for de-termining local activation rate. Yet, the variation in activationintervals and electrogram morphology makes this processtedious in AF. DF analysis provides an alternative to timedomain analysis. However, some intrinsic signal propertiesevaluated in the current study may create problems in thefrequency domain analysis that are actually less problematicin time domain analyses. Finally, specific factors related tothe FFT analysis could affect the DF measurements: signalprocessing, mode of recording, and signal duration.

Far field ventricular depolarizations can be a significantconfounding influence on DF analysis in both unipolar and

bipolar recordings. Depending on their amplitude, these de-polarizations can be “ignored” if atrial electrograms are man-ually marked in the time domain to determine atrial activationrate. However, if frequency analysis is performed, the pre-FFT processing that enhances atrial electrograms similarlyamplifies ventricular depolarizations; this “artifact” thereforemakes the calculated DF as an estimate of atrial activationrate in these signals unreliable. It has been demonstrated thatventricular template subtraction can “uncover” the atrial sig-nal in wide bipolar signals.15 This process can be aided ifa ventricular reference signal, such as the surface ECG, isavailable from which fiducial points of the ventricular beatscan be obtained. Improvements in DF analysis were seen withtemplate subtraction not only when the ventricular compo-nents were significant, but also when the components wereminimal.

There was a marked improvement of atrial-to-ventricularsignal ratio when bipolar electrograms were derived fromthe unipolar electrogram. The simulations showed the im-portance of high atrial-to-ventricular amplitude for reliableDF measurements. Thus, closely spaced bipolar electrogramsare recommended in areas where far field ventricular activ-ity is known to be a problem, as well as ventricular templatesubtraction. Although double potentials in the signal are pos-sible when bipolar recordings are used and can affect DFanalysis,11 we believe the benefits of reducing the relativeventricular contribution to the signal outweighs the possibil-ity of introducing double potentials.

Ng et al. Considerations for Dominant Frequency Analysis 763

Figure 5. Examples of two sets of signals withidentical signal-to-noise ratios but differingDFs. The top two signals are electrogramswith constant amplitude and frequency andsignal-to-noise ratios of −2 dB (=0.6). Thesignal on the left has a DF of 6 Hz while thesignal on the right had a harmonic frequencyof 12 Hz dominate. The bottom two signalsare electrograms with variable amplitude andfrequency with signal-to-noise ratios of 10 dB(=3.1). The signal on the left has a DF of6.5 Hz while the signal on the right had a DFof 6 Hz.

Various groups have used different signal durations foranalysis of DF, ranging from 2 to 10 seconds.1-10 In this study,we evaluated durations from 1 to 4 seconds and found thatdurations of 2 seconds or greater were required for reliableDF measurements. Our previous study showed that averag-ing multiple DFs of short durations could further improvethe reproducibility of the measure.11 Our previous studyalso showed that increasing recording duration from 4 to 16seconds did not significantly affect the reproducibility of DFanalysis.11

DF analysis was shown to be generally robust againstnoise, particular when very regular activation intervals werepresent. However, DFs could be affected even with SNRscommonly obtained in clinical or experimental electrophys-iology laboratories, particularly for signals with varying am-plitude and frequency. In the time domain, the observer mark-ing signals with adequate SNRs visually filters out the noise(Fig. 5C,D). However, as demonstrated in this study, whitenoise is included in the FFT analysis and contributes to deter-mination of the DF in two ways. First, at low levels of noise,there is some probability that a harmonic could be mistak-enly chosen as the DF depending on the phase relationshipof the noise to the signal. In other words, at the same SNR,two different Gaussian noise signals may sum with the realsignal such that one may cause a shift of the DF to a harmonicof the true DF, whereas the other does not. The probabilityof this error was inversely related to the SNR. For the sig-nals with frequency variation, broader peaks were seen inthe power spectrum with harmonic peaks of smaller powerwhen compared with the spectra of the signals with regularrate. When noise was added to this signal, the probabilityof error was high compared with signals with regular rate,but the actual error was smaller because the harmonics were

rarely amplified enough to become the DF. Instead, noise maycause other peaks near the atrial activation rate to be selectedas the DF. Thus, two methodologies can be implemented toreduce the impact of noise on DF analysis. High SNRs re-duce the probability of error. Furthermore, averaging the DFfrom multiple shorter segments will reduce the probabilisticerror that could be introduced from evaluating the DF froma single recording.

Limitations

Epicardial canine electrograms and simulated signals wereused in this study. Although the principles demonstrated inthis study are also applicable to human endocardial record-ings, the relative importance and magnitude of these de-scribed effects on DF may differ. Other signal processingmethods to condition the signal before FFT analysis may bepossible, but were not examined in this study. The particularmethodology used in this article are simple to implement andcan be used for both offline and real-time analysis.

In addition to DF analysis, other methods have been pro-posed for the estimation of activation rate. Ravelli et al. use atime domain method that adaptively adjusts the threshold inwhich activations are detected.19 Narayan et al. proposed theuse of autocorrelation to estimate atrial cycle length that ap-pears to be advantageous to DF analysis when double poten-tials are present.20 Wavelet analysis could be another potentialapproach to detect activations. However, it is not clear howthese methods would perform in situations where far fieldventricular depolarizations or noise are present. Similarly,other ventricular cancellation techniques were not evaluatedin this study.

764 Journal of Cardiovascular Electrophysiology Vol. 18, No. 7, July 2007

Conclusions

Based on the findings of this study, the following rec-ommendations for the use of DF analysis for AF should beimplemented:

1. The time domain electrogram signal being studied shouldbe carefully evaluated for factors that might affect DF,such as far-field ventricular depolarizations and noise;

2. Both unipolar and bipolar recordings can be utilized, butnarrowly spaced bipoles will result in better identificationof local activation;

3. Signal processing needs to be performed before FFT anal-ysis and should be validated with a set of signals withknown frequencies;

4. Signal durations of at least 2 seconds should be used;5. Manual interval marking for a subset of activations is rec-

ommended to confirm the results.

The recommendations made here complement those madein our previous publication.11 Adopting both sets of guide-lines are important to obtain meaningful results that are re-producible and an accurate reflection of the atrial fibrillatoryrate.

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