quantitative-electrogram-based methods for guiding catheter ablation in atrial fibrillation how to...

INV ITEDP A P E R

Quantitative-Electrogram-BasedMethods for Guiding CatheterAblation in Atrial FibrillationHow to identify points for catheter ablation to stop atrial fibrillation?

This paper summarizes challenges and recent advances in signal

processing guided ablation.

ByMathias Baumert, Senior Member IEEE, Prashanthan Sanders, and Anand Ganesan

ABSTRACT | Atrial fibrillation (AF) is the most common

cardiac arrhythmia in humans, with an estimated lifetime

prevalence of 25%. It is characterized by irregular and disor-

ganized electrical activation of the atria. In the past decade,

catheter ablation, i.e., focally burning targeted areas of car-

diac muscle, has emerged as a potentially curative therapy

for AF. Accompanying this development there has been an in-

creasing interest in quantitative intraprocedural signal analy-

sis to guide the selection of ablation targets. In this review,

we provide an overview of quantitative signal processing ap-

proaches for mapping and ablation of AF.

KEYWORDS | Atrial fibrillation (AF); catheter ablation;

electrogram

I . BACKGROUND

Atrial fibrillation (AF) is the most common cardiac ar-

rhythmia in humans, with an estimated lifetime preva-lence of 25% for adults over the age of 40 [1], [2].

Globally, AF is increasing in prevalence with more than

34 million individuals suffering from the arrhythmia [3].

AF is characterized by irregular and disorganized electri-

cal activation of the atria that results in rapid and irregu-

lar contractions of the ventricles. AF typically manifests

clinically as palpitations and chest discomfort. The

downstream consequences of AF are highly significant

and include heart failure [4], stroke [5], dementia [6],

and a doubling in mortality [3]. The burden of AF is am-

plified by health-related costs associated with hospitaliza-

tion. In the United States alone, over 400000 patients

are hospitalized for AF annually, with total costs exceed-

ing $US 3.5 billion [7]. Pharmacological control of AF is

frequently ineffective and/or poorly tolerated and may beassociated with significant side effects [1]. In the past de-

cade, there has been a growing interest in the utilization

of catheter ablation as a potentially curative therapy for

AF. Using percutaneously introduced catheters, targeted

areas of cardiac muscle are burned focally [8]. Accompa-

nying the development of AF ablation there has been a

concomitantly increasing interest in quantitative intrapro-

cedural signal analysis approaches to guide the selectionof ablation targets. The objective of the current review is

to provide an overview of the field of quantitative signal

processing approaches for mapping and ablation of AF.

II . ATRIAL FIBRILLATION:PHYSIOLOGY, MECHANISMS, ANDTREATMENT

A. Cardiac Anatomy and ElectrophysiologyThe human heart comprises four chambers, consist-

ing of paired upper left and right atria, and lower left

and right ventricles. Functionally, the primary pumping

function of the heart is carried out by the left and right

ventricles, which pump blood to the organs of the body

and the lungs, respectively. During normal cardiac con-

traction, the atria have an important role in coordinating

flow of blood through the heart in different phases of the

Manuscript received July 5, 2015; revised October 22, 2015; accepted November 27,2015. Date of current version January 19, 2016.M. Baumert is with the School of Electrical and Electronic Engineering, TheUniversity of Adelaide, Adelaide, S.A. 5000, Australia (e-mail: [email protected]).P. Sanders and A. Ganesan are with the Centre for Heart Rhythm Disorders, SouthAustralian Health and Medical Research Institute, University of Adelaide and RoyalAdelaide Hospital, Adelaide, S.A. 5000, Australia (e-mail: [email protected]).

Digital Object Identifier: 10.1109/JPROC.2015.2505318

0018-9219 Ó 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

416 Proceedings of the IEEE | Vol. 104, No. 2, February 2016

cardiac cycle. During ventricular contraction, the atriaact as a functional reservoir for incoming blood to the

heart. In the late of phase of ventricular filling, atrial

contraction enhances inflow of blood into the ventricle.

Coordinated sequential contraction of the atria and ven-

tricles is regulated by specialized areas of electrically ex-

citable tissue within the heart. The sino–atrial node,

located in the upper lateral wall of the right atrium,

serves as the intrinsic cardiac pacemaker. In normalphysiologic rhythm (i.e., sinus rhythm) electrical im-

pulses arising from the sino-atrial node pass as waves of

electrical activation through the excitable atrial muscular

tissue to the atrio–ventricular node, located at the junction

between the atria and ventricles. The atrio–ventricular

node is the only physiological pathway for electrical propa-

gation to flow from the atria to ventricles, as the atria and

ventricles are otherwise electrically isolated from eachother by the fibrous rings of the atrio–ventricular valves.

Electrical activation passes through the atrio–ventricular

node to the ventricles utilizing a specialized conduction tis-

sue known as the His–Purkinje system, which spreads out

as an arborized network of fibers throughout the ventricle.

The His–Purkinje system in turn activates the ventricular

muscle and ensures an effective synchronous contraction

of the ventricular walls, maximizing the effective expulsionof blood from the heart.

During AF, this sequence of cardiac electrical activa-

tion and subsequent coordinated ventricular contraction is

disrupted and the atrial activation frequency increases dra-

matically from 1–2 Hz (i.e., 60–120 beats/min during nor-

mal sinus rhythm) to 6–10 Hz. The timing of activation in

different parts of the chamber becomes spatially asynchro-

nous, resulting in irregular and uncoordinated atrial con-tractions and the loss of the atrial contribution to late

ventricular filling. Despite the presence of the atrio–

ventricular node that acts as a low-pass filter to high-

frequency atrial impulses, ventricular rates during AF may

increase to 140–180 beats/min, typically with temporally

irregular contractions. Increased ventricular rates are

believed to be responsible for most AF-related symptoms,

such as palpitations, chest pain and can lead in somepatients to chronic weakness of the ventricular muscle

(cardiomyopathy). Stasis of blood within the atrium during

AF is thought to contribute to the increased risk of stroke

that is clinically associated with the arrhythmia.

B. Mechanisms of Atrial FibrillationDespite over a century of research there remains an on-

going debate regarding the fundamental mechanisms un-derlying atrial fibrillatory dynamics [9]. At present, a

consensus has emerged, proposing that AF is the conse-

quence of an incompletely understood interplay between

triggering mechanisms and substrate [10], [11]. Based on

clinical and experimental findings, the predominant trig-

gering mechanism for AF is believed to be an ectopic elec-

trical discharge arising from the pulmonary veins [8],

[12]–[14]. Atrial fibrillation commonly arises in the con-text of a substrate of electrophysiological and structural re-

modeling that is characterized by a combination of slowed

conduction velocity and altered electrical excitability [15].

These substrate-based physiological changes are typically

heterogeneously distributed throughout the atrium [15]. At

the histomolecular level the disease substrate is believed to

be attributable to a combination of changes in the expres-

sion of ion channels in cardiac myocytes and the develop-ment of scarring (fibrosis) that acts as a barrier to

electrical propagation in the atrial tissue [11].

A number of wave propagation mechanisms have

been proposed to explain fibrillatory dynamics in AF [9].

The multiple wavelet theory postulates that AF is main-

tained by the precession of a discrete number of electri-

cal waves. It arises as a consequence of functional

inhomogeneity in the local electrical excitability of atrialtissue [16]–[18]. The formation of multiple wavelets may

be facilitated by endo-epicardial dissociation, whereby

differences in activation times between the epicardial

and endocardial layers of atrial tissue are thought to lead

to transmyocardial breakthrough activations [19], [20].

The principal alternative hypothesis postulates that

AF is maintained by predominantly localized sources,

whereby arrhythmia maintenance occurs by a single orsmall number of drivers that sustain on-going AF [21]. A

series of studies has demonstrated that these focal

drivers are frequently rapidly spinning local-reentrant

circuits called rotors that emit curved spiral waves [22],

[23]. The rotor hypothesis of cardiac fibrillation has been

extensively tested, both experimentally [21] and in com-

putational simulations (Fig. 1) [24]. According to this

theory, the disorganization and spatio–temporal irregu-larity of electrical activity seen in AF arises as a conse-

quence of the interaction of these waves with functional

or anatomical boundaries [25], [26].

Computational models and simulations have signifi-

cantly informed mechanistic understanding of the fibril-

latory process [24]. Early works by Moe and Abildskov

were critical to the development of the multiple wavelet

hypothesis for AF [16] and involved cellular automata formodeling myocardial wave propagation in horizontal

grids of cells [24]. Models have evolved significantly over

the past few decades and feature biophysically detailed

cell electrophysiology, incorporating ionic channels and

pumps of atrial myocytes in realistic 3-D anatomical ar-

rangements. Computational models of AF have been

valuable in elucidating cellular mechanisms involved in

the pharmacological control of heart rate, for devisingpacing algorithms for AF termination and optimizing

catheter ablation strategies [24].

C. Clinical Ablation ProceduresCatheter ablation procedures are performed in spe-

cialized cardiac catheterization laboratories suited for the

aseptic introduction of catheters into the heart. The

Vol. 104, No. 2, February 2016 | Proceedings of the IEEE 417

Baumert et al. : Quantitative-Electrogram-Based Methods for Guiding Catheter Ablation in Atrial Fibrillation

catheters used in ablation procedures are typically 1.5 m

long and have platinum-alloy electrodes at the tip, de-

signed for percutaneous introduction into the body, most

commonly via the femoral vein at the upper leg. Duringcatheter ablation procedures, physicians manually ma-

neuver catheters within the heart to specific locations

under the guidance of X-ray fluoroscopy. In recent years,

however, a variety of electro-anatomic mapping technolo-

gies have evolved to enable 3-D anatomical reconstruc-

tion of the cardiac chambers, thereby reducing ionizing

radiation exposure, both for the patient undergoing the

ablation procedure and the physician performing it [27],[28]. In contemporary practice, laboratories simulta-

neously record electrical signals from intracardiac cathe-

ters, called electrogram (EGM) and from the body

surface, called electrocardiogram (ECG). The EGM and

ECG signals are displayed in real time on computer

screens, enabling the cardiac electrophysiologist to inter-

pret cardiac activity and identify ablation targets.

Historically, the mainstay of catheter-based therapy

has been to encircle the pulmonary veins via a series of

burns [pulmonary vein isolation (PVI)] to prevent ec-

topic impulses from this anatomical location from trig-gering AF episodes (Fig. 2) [8], [29]. Significant

evidence has accumulated, however, suggesting that

long-term outcomes in patients with longer lasting, per-

sistent forms of AF are modest, reflected in a long-term

failure rate of anatomically-based PVI as high as 50%

[30], [31].

In this context, quantitative EGM-guided strategies

are emerging as a tool for AF ablation. By utilizing intra-procedurally acquired EGM, meaningful information is

extracted to assists cardiac electrophysiologists in identi-

fying regions of the atrium critical to the maintenance

of AF.

There are several technical challenges in developing

and implementing quantitative EGM ablation strategies

in the electrophysiological laboratory. First, diagnostic

Fig. 1. Examples of spiral waves obtained with an electrophysiological model of chronic atrial fibrillation. (a) Simulated action

potentials in chronic atrial fibrillation conditions, where Ito and ICaL currents are reduced without IK1 upregulation (CAF1) and for the

same model, but with IK1 increased (CAF2). (b) Action potential duration (APD) versus the diastolic interval (DI) in control condition

and chronic AF. (c) Snapshots (2.4-s interval) of spiral waves and tip meander in CAF1 and CAF2. Adapted from [144] with permission.


Baumert et al.: Quantitative-Electrogram-Based Methods for Guiding Catheter Ablation in Atrial Fibrillation

catheters that are introduced into the heart usually re-

cord signals from a limited number of electrodes. There-

fore, mapping of activation patterns in AF is typically

performed sequentially instead of simultaneously map-

ping the entire atrial surface. Second, the catheters allow

recording of signals only from the endocardial surfaceand not from the entire volume of the atrial muscle.

Third, the EGMs during AF exhibit complex spatial and

temporal dynamics and are vulnerable to variations in

the local myocardium (such as tissue fibrosis or local

scarring), causing difficulties in identifying appropriate

fiducial points for accurate annotation of activation tim-

ing. Fourth, the spatial accessibility of mapping proce-

dures is limited by the fact that the catheters aremaneuvered within enclosed cardiac chambers. Finally,

practical and logistical constraints restrict clinical map-

ping to discrete regions of the atrium to relatively short

periods of time.

III . SIGNAL ACQUISITION ANDPREPROCESSING

A. Intracardiac Electrogram AcquisitionCritical to the development of quantitative intrapro-

cedural EGM analysis is an understanding of the acquisi-

tion and preprocessing of intracardiac signals. The

process of EGM creation is illustrated schematically in

Fig. 3. Fundamentally, transmembrane currents in the

extracellular space create potential differences during de-polarization of the cardiac muscle due to differences in

the axial voltage gradient at the border zone between ac-

tivated and inactivated cells.

Cardiac EGM recordings are typically obtained by dif-

ferential amplifiers with a high input impedance and a

good common mode rejection. By convention, in the uni-

polar recording configuration, the recording electrode on

the tip of the intracardiac catheter and in physical con-tact with the cardiac muscle is connected with the an-

odal input of the amplifier. The cathodal input is

connected to an indeterminate electrode that registers

minimal cardiac signal in theory. This recording configu-

ration creates a characteristic EGM morphology as planar

wavefronts pass toward the recording electrode (Fig. 3).

Initially, a small positive deflection may be seen as the

depolarization wavefront moves toward the catheter tipelectrode. As the wavefront reaches and passes beyond

the electrode, the wavefront deflection becomes steeply

negative, creating a characteristic biphasic complex. The

timing of the maximum negative slope is concurrent

with the timing of the depolarization of cardiac tissue di-

rectly underneath the electrode. A limitation of the uni-

polar EGM is the vulnerability of the recorded electrical

signal to external electromagnetic interference (e.g.,electrical mains activity), or electrical depolarization of

other parts of the cardiac chambers, i.e., far-field activ-

ity. In the case of atrial EGM recordings, the primary is-

sue is overlap with electrical activity caused by

ventricular depolarization. To a large extent, these issues

have hindered the utilization of unipolar EGMs for clini-

cal mapping during AF, although an extensive literature

has developed around the use of unipolar recording inresearch settings [18], [32], [33].

In clinical AF mapping, most EGMs are recorded in

the bipolar mode. Bipolar EGMs are created by subtract-

ing two unipolar EGMs recorded at proximate sites, typi-

cally from adjacent poles of an intracardiac catheter.

Bipolar EGMs are usually preferred in clinical settings,

as the far-field contribution of ventricular depolarization

is largely eliminated. However, compared to unipolar re-cordings, the timing of local electrical activation is less

well defined [34].

B. Electrogram Morphology in Atrial FibrillationThe EGM morphology during sinus rhythm and AF

is an important consideration in the development of

quantitative analytical approaches. In sinus rhythm, uni-

polar endocardial EGMs display predominantly negativedeflections and a relatively uniform morphologic ap-

pearance. Similarly, bipolar atrial EGMs during sinus

rhythm have discrete complexes separated by periods of

isoelectric activity. In contrast, EGMs acquired during

AF are frequently irregular with complex morphology.

Wells et al. described three types of bipolar EGM mor-

phologies during AF [35] (Fig. 4). Type 1 AF is

Fig. 2. Example of an electroanatomic map of the left atrium

created with the NavX Fusion system. Adapted from [145] with

permission. In contemporary electrophysiological procedures, a

3-D virtual geometry of the pulmonary veins is commonly created.

Encircling ablation lesions (red dots) are used to surround the ostia

of the pulmonary veins (arrows) which are believed to be the

source of ectopic impulses known to trigger AF.



characterized by discrete atrial complexes, of variable

morphological appearance, but with a discrete isoelec-tric baseline. Type 2 AF is characterized by discrete

beat-to-beat atrial complexes, but differentiated from

Type 1 by the fact that the baseline is not isoelectric.

Type 3 AF is characterized by complex and highly irreg-

ular atrial EGMs that fail to exhibit either discrete com-

plexes or isoelectric intervals.

An important concept in the morphological descrip-

tions of EGMs during AF is that of fractionation. Al-though no precise consensus definition exists, the

concept of fractionation is used to describe EGMs that

may have multiple components of high frequency that

may be of relatively low amplitude and may be of pro-

longed duration compared to “organized” EGMs. A num-

ber of different physiological processes are thought to

contribute to EGM fractionation observed in human AF.

These include slow conduction through areas of scar-

ring, curved or turning wavefronts and so-called aniso-tropic conduction in areas of tissue where conduction is

more rapid longitudinally along muscle fibers, than per-

pendicularly between adjacent muscle strands [36].

Fractionation can also occur for reasons other than ab-

normal tissue properties, e.g., in regions with anatomi-

cally overlaying areas of tissue, It can be introduced

artificially by inappropriate signal high-pass filtering or

external electromagnetic interference [36]. Separatingthe features pertinent to the maintenance of AF that are

relevant as ablation targets is an ongoing challenge in

the field.

C. Electrogram FilteringAs with other bioelectrical signals, EGMs are contam-

inated with noise, originating from various internal and

Fig. 3. Schematic illustrating the formation of unipolar and bipolar electrograms in cardiac electrophysiology. Reprinted from [146]

with permission. Horizontal bars represent a sheet of myocardium with depolarization propagating from left to right. Theoretical

electrograms are shown in boxes. (a) Unipolar recording: As the wavefront propagates toward the electrode, a positive deflection, an

R wave, is inscribed. As the wavefront propagates past the recording electrode, an S wave is inscribed and thus an RS complex occurs.

Recording from the initial site of depolarization (origin at the left side of the tissue) produces a QS complex as the wavefront moves

away from the recording electrode. Recording at the right side of the tissue produces a monophasic R wave. (b) Bipolar recording:

Electrode 1 is connected to the positive input of the amplifier and electrode 2 is connected to the negative input. Compared to

the signal from electrode 1 (Uni-1), the signal from electrode 2 (Uni-2) is slightly delayed (because the wavefront reaches it later) and

is inverted because it is attached to the negative input of the recording amplifier. Adding these two signals together generates the

bipolar signal (Bi 1-2) that removes much of the far-field signal. Differentiating the signal (Uni-1 filtered) decreases the far-field

component and produces a signal quite similar to the bipolar signal but slightly shifted with respect to time. Differentiating the bipolar

signal (bipolar: filtered) produces additional deflections and further complicates the signal. Indifferent electrode configurations for

unipolar recordings are shown in (c) (RA: right arm; LA: left arm; LL: left leg; IVC: inferior vena cava).



external sources that act across various frequency ranges.

For EGMs, the frequency band of interest is around 40–

250 Hz and zero-phase band-pass filters are commonly

applied to bipolar recordings to remove baseline shiftsand high-frequency noise. Because artefacts often show

power within the frequency band of interest, additional

strategies have been devised. Averaging procedures that

consider EGMs during ventricular activation times have

been used to generate ventricular waveform templates

that are subtracted from atrial EGMs to suppress the in-

fluence of ventricular far-field artefacts [37].

Many EGM analysis techniques require a strong sim-plification of the atrial signal, primarily aiming at the ex-

traction of the envelope of the signal. Therefore,

Botteron and Smith have proposed to rectify the band-

pass filtered signal, followed by low-pass filtering at a

cutoff frequency of 20 Hz [38]. This process results in a

time-varying waveform proportional to the amplitude of

the high-frequency components (40–250 Hz).

IV. APPROACHES TO ELECTROGRAMANALYSIS IN ATRIAL FIBRILLATION

A variety of approaches have been utilized to extract

EGM features pertinent to clinical AF ablation. In the

following section, a technical summary of available ap-

proaches is provided.

A. Analysis of Fibrillatory Rate

1) Local Activation Times: Estimation of fibrillatory rate

in the time domain, i.e., the AF cycle length (AFCL), isbased on the interval between consecutive local activation

times (LAT). The precise annotation of LAT from bipolar

EGMs, in particular during AF, is difficult and a number

of algorithms have been proposed for delineating relevant

fiducial points. Basic approaches to detect LAT include

identifying the time when the upstroke of bipolar EGM

reaches a 45� angle [39], the time of the maximum bipo-

lar EGM excursion from baseline, the time of the maxi-mum slope in either direction, or the time the sloping

segment containing the maximum slope in either direc-

tion crossed the baseline (fastest zero crossing) [40]. Ex-

amples of more complex algorithms that produce more

reliable results are outlined below.

A morphology-based algorithm has been proposed [41],

where a baseline value within the analysis window is com-

puted by averaging the EGM outside a 120-ms subwindowcentered at the extreme value in the analysis window. Ex-

trema associated with notches narrower than 4 ms are ex-

cluded. The LAT is determined from the waveform

morphology by defining primary and secondary phases.

The primary phase is obtained by finding the peak excur-

sion from the baseline and searching backward for the start

of the phase and forward for the end. The temporal limits

Fig. 4. Electrogram morphology analysis applied to bipolar electrograms of Wells’ types: type I, type II, and type III AF. Filled triangles

indicate the time of local activation waves detection using the barycenter method described in [42]. On the right, superposition of the

normalized local activation waves obtained from the signals of the left panels. Reprinted from [42] with permission.



of phases are defined by a change in slope sign, baselinecrossing, or a flat line. Additional phases are defined by

scanning from 55 ms before the start of the primary phase

to 55 ms after the end of the primary phase for additional

excursions from the baseline of at least 25% of the primary

peak amplitude. The LAT for monophasic, biphasic, and

triphasic responses, respectively, are equal to the time of

occurrence of the peak, zero crossing, and peak of the mid-

dle phase. If more than three phases are found, the LAT isassigned midway between the first two peaks that exceed

50% of the amplitude of the primary phase, or, if a second

phase of sufficient amplitude (in addition to the primary

phase) is not found, the LAT is assigned the time of the

peak of the primary phase.

Faes et al. proposed to estimate LAT from the barycen-

ter of LAWs in bipolar EGMs [42]. After ventricular arte-

fact rejection and application of the signal filteringprocedure proposed by Botteron and Smith [38] to the

atrial signal sðnÞ, resulting in swðnÞ, LAWs are detected by

adaptive thresholding, using the last ten detected peaks

and exponentially decreasing weights combined with a

55-ms blanking period. The barycenter of each LAW is cal-

culated as the time that divides the local area of the modu-

lus of the signal in two equal parts. A noncausal moving

average filter with 90 coefficients is applied to the modu-lus of sðnÞ: sf ðnÞ ¼

P44i¼0 jsðn� iÞj �P45

i¼1 jsðnþ iÞj. Theactivation time is set on the positive zero crossing of sf ðtÞthat is closer to the local peak of swðnÞ.

Ng et al. recently devised an iterative method for mea-

suring LAT and AFCL [43]. Following preprocessing simi-

lar to the one proposed by Botteron and Smith, the peak

with the highest magnitude is detected as the first LAT. Af-

ter excluding all neighboring peaks within a 50-ms blank-ing period, the next largest peak is detected and added to

the set and the blanking period applied again. This process

is iterated until the mean cycle length is G 275 ms and ei-

ther 1) the mean CL is less than the median CL plus 5 ms;

or 2) the magnitude of the current peak is 20% less than

that of the previously detected peak. For activation times

9 1:5� median CL, the largest peak within the interval

that is not within 50 ms of another peak is included in theset and the procedure repeated until there are no more in-

tervals 9 1:5� median CL with peaks between them.

2) Dominant Frequency Analysis: Measuring fibrillatory

rates in the frequency domain is a popular alternative to

estimating AFCL from LAT [25], [44]. It usually involves

filtering the EGM according to Botteron and Smith and

edge tapering to reduce spectral leakage before applyingthe discrete Fourier transform to decompose N samples

of swðnÞ into k frequency components [45], [46]

SwðkÞ ¼XNn¼0

swðnÞe�2�jkn

N :

Typical time frames for frequency domain analysis rangebetween 2.5 and 8 s. While shorter time frames offer in-

creased ability to track signal variations in nonstationary

conditions typical of EGM recordings, they may also

compromise frequency resolution. The EGM frequency

spectrum during AF is characterized by a more or less

well-pronounced peak, the so-called dominant frequency

(DF) that mainly reflects timing of the narrow deflec-

tions in bipolar EGM [47] and is considered a surrogatefor the local activation rate [23] (Fig. 5). A so-called reg-

ularity index, or organizational index, is often computed

alongside to avoid ambiguity in DF detection in regions

with low signal-to-noise ratio [48]. Although definitions

vary across studies, the underlying rationale is to relate

the EGM power at the DF to the power of background

activity or harmonics [49]–[51]. Not all investigators

have utilized this approach [46], [52], [53] and further-more, analytical treatment proves that it is biased at low

DF [47]. Sanders et al. have fixed the frequency window

of interest to 3–15 Hz, limiting it to regions of physiolog-

ical activation rates [54]. An alternative approach for

Fig. 5. Bipolar electrograms and corresponding power spectra

obtained from four atrial sites in a patient with spontaneous

paroxysmal AF. Each site shows distinct dominant frequency (DF)

and regularity index (RI) values. Reprinted from [54] with

permission.



EGM power density spectrum estimation involves time av-eraging of EGMs for different window lengths and subse-

quent power computation of the averaged signal, where the

window length is inversely related to the frequency [55].

The nonstationary temporal dynamics of EGM signals

impose limitations on the applicability of frequency do-

main analysis for estimating DF [53] and a poor correlation

of DF with the local cycle length measured in the time do-

main has been reported [56]. In view of AF ablation map-ping, a significant concern is the spatio–temporal stability

of mapped DF locations, and the presence of a surrounding

centrifugal gradient around putative AF sources. While

some authors have observed stable DF for short periods

(two subsequent 10-s epochs) and have demonstrated cen-

trifugal gradients surrounding sites of high DF [57], others

have reported significant instability of DF during pro-

longed recordings [58], [59] and have failed to observe acentrifugal gradient around DF sites [59].

Wavelet analysis has been employed to cope with the

nonstationarity of EGM data by providing time-varying

frequency information. Using the continuous wavelet

transform

Wð�; aÞ ¼ 1ffiffiffia

pZ1�1

xðtÞ t� �

a

� �dt

where sðtÞ is the atrial signal, is the mother wavelet (a

function with compact support; typically orthonormal),

and a is the scaling factor that dilates the wavelet, unipo-

lar EGMs have been analyzed, using the first derivativeof the Gaussian function as the mother wavelet, resem-

bling activation waves seen on unipolar EGM. It has

been argued that local and far-field activation waves in

EGM are separable on low- and high-frequency scales,

using scale-dependent weights [60]. The LAWs were ex-

tracted with an algorithm that comprises template

matching, using a library of automatically generated tem-

plates, and a thresholding procedure [60]. The continu-ous wavelet transform approach combined with an

alternative set LAW detection criteria has been used to

track wavefront propagation on 2-D maps of the left atria

[61]. The discrete wavelet transform, using the Coiflet 4

mother wavelet, combined with thresholding of detail co-

efficients, has also been used for EGM denoising as well

as for classifying the degree of fractionation [62].

B. Analysis of Electrogram Complexity

1) Wave Morphology Similarity: In 2002, Faes et al. pro-posed to evaluate the complexity of EGMs by measuring

the degree of morphological repetitiveness of the LAWs

in the signal [42]. Following LAT detection using the

barycenter method, LAWs xi are extracted over a window

of �45–45 ms with respect to the fiducial point and sub-sequently normalized to their standard norm:

x̂i ¼ xi=jxij. Morphological dissimilarity between pairs of

standardized LAWs are computed as the angle of associ-

ated vectors: dðx̂i; x̂iÞ ¼ cos�1ðx̂i � x̂jÞ. The similarity in-

dex is then defined as

�ð"Þ ¼ 2

NðN � 1ÞXNi¼1

XNj¼iþ1

� �� dðx̂i; x̂iÞð Þ

where � is the Heaviside function [�ðxÞ ¼ 0 for x G 0

and �ðxÞ ¼ 1 for x � 0], " is the threshold value, empiri-

cally set to " ¼ �=3 and N is the number of LAWs (Fig. 4).

The similarity index has been usually derived from 10-s

EGMs [63], but is reliable for signal windows down to

five atrial depolarization waves and has been shown to

distinguish different degrees of similarity and trackchanges over time [42]. In patients with paroxysmal AF,

beat-to-beat analysis of similarity has been able to identify

deteriorating regularity during the first few minutes of

AF [64]. Spatial distribution maps of bipolar EGM simi-

larity have been constructed and distinct regions of high

similarity that anchored to anatomy were demonstrated

in patients with paroxysmal AF, while maps of low simi-

larity were observed in maps of patients with chronic AF[65]. Fusion of computed tomography images with simi-

larity index and AFCL maps have allowed identifying ana-

tomical locations of rapid and repetitive sources of

activity in patients with persistent AF [17]. Dichotomized

AFCL and similarity index values combined via a logic

AND operation in a single map captured points of rapid

activation and high similarity that are deemed critical to

AF [63]. Testing and application of the logical operatormaps have been recently published [66].

Recently, similarity analysis based on the cross correla-

tion of individual LAWs measured over 100-ms windows

was proposed to study the temporal repetition of LAW

morphologies, yielding so-called recurrence plots [67],

where LATs were estimated using the iterative method de-

scribed above. Periodic EGM recurrence was present at all

recording sites and highly repetitive LAW morphologieswere found to be important for maintaining AF.

2) Entropy: Shannon entropy, the classical measure of

information theory, is defined as SE¼�PMi¼1 pðiÞ log2 pðiÞ,

where M is the number of discrete values the variable can

assume and pðiÞ is the probability of assuming the ithvalue. From a finite set of N observations Shannon entropy

can be estimated as

bSE ¼ �XMi¼1

p̂ðiÞ log2 p̂ðiÞ þM� 1

2N



where p̂ðiÞ is the maximum-likelihood estimate and M isthe number of bins with nonzero probability. The bSE of

time delays, obtained using the barycenter method, has

been proposed to measure the synchronization of AF [68].

To relate the dispersion of the time delays with the

strength of synchronization in the activation process, a syn-

chronization index has been defined as

Sy ¼ 1�bSE

log2 N:

Higher Sy values of time delays have been related to higher

complexity of wave propagation patterns in computer sim-

ulations, and to higher EGM complexity in patients with

paroxysmal AF [68].

The SE has been used to measure AF complexity di-rectly in bipolar EGMs thereby circumventing the need

for delineating LATs and related issues [69]. Pivots of ro-

tational atrial activations have been shown to coincide

with high SE values [70]. Based on the results of com-

puter simulations, multielectrode array recordings of iso-

lated rat atria, epicardial plaque recordings of

hypertensive sheep, and high density mapping of AF pa-

tients, SE mapping has been proposed to assist in AF ro-tor identification [69]. In 2-D spiral wave simulations

obtained with different cell models, SE has been consis-

tently highest at the pivot across all investigated models

and independent of electrode spacing, electrode orienta-

tion, and EGM filtering [70]. Shannon entropy has been

further validated in a study of spiral wave EGM features

extracted from human ventricular fibrillation epicardial

recordings [71].Approximate entropy (ApEn) is a regularity metric

that measures the logarithmic likelihood that runs of pat-

terns similar to each other will remain similar in the

next incremental comparison and is defined as

ApEnðSN;m;rÞ ¼ lnCmðrÞCmþ1ðrÞ

where m is the pattern length, r is the criterion of simi-

larity, and CmðrÞ the prevalence of repetitive patterns of

length m in the sequence SN. High ApEn has been corre-

lated with EGM fractionation in a database of AF record-

ings [72], [73]. In a 3-D electro-anatomical computermodel of human atria, high ApEn values have been able

to identify the pivot of stable and meandering rotors,

providing further theoretical support for the potential of

entropy estimation for rotor mapping [73]. Sample en-

tropy (a modified version of ApEn) obtained from unipo-

lar right epicardial EGM has been shown to correlate

with DF and could be estimated from surface ECG [74].

A regularity index defined by means of conditionalentropy (CE) that represents the amount of information

carried by the most recent sample of a series s when its

past L� 1 samples are known has been defined as

CEðLÞ¼ �XMj¼1

p sJL�1

� �XNi¼1

psi

sJL�1

!log p

si

sJL�1

!

where sJL�1 represents the Jth pattern of length L� 1,

pðsJL�1Þ is its probability, and pðsi=sJL�1Þ is the conditional

probability of the sample si given the pattern sJL�1 [75].

After introducing a corrective term and normalization,

resulting in the normalized corrected conditional entropy(NCCE) an index of regularity can be defined as

Rs ¼ 1�minðNCCEðLÞÞ. Rs has been shown to discrimi-

nate different degrees of AF complexity [76].

Clinical tests of entropy informed ablation procedures

are currently pending. A recent study compared several

complexity measures that have been successfully applied

to surface ECG in AF on epicardial EGM mapping in an

animal model [77]. The measures were based on princi-pal component analysis [78], where multichannel EGM

were decomposed into their orthogonal constituents, dis-

tribution of power across the frequency spectrum as well

as sample entropy [77] and, when combined, showed

perfect classification of short-term versus long-term AF.

3) Nonlinear Dynamics: Phase-space characterization of

EGM data has been proposed to capture the nonlineardynamics of AF [79], [80]. According to Taken’s theorem

[81], the phase space of a deterministic system can be

constructed from empirical data, using the method of de-

lays, where the phase space is represented as a vector

SðnÞ¼ sðnÞ; sðn�LÞ; sðn�2LÞ; . . . ; s n�ð��1ÞLð Þ½ �T

and the embedding dimension � and reconstruction de-lay L have to be empirically determined. The noisy, non-

stationary nature of EGMs and unknown dimensionality

generally thwart faithful reconstruction of the dynamics

of the underlying system. Recurrence quantification anal-

ysis (RQA), which is based on recurrence plots for visu-

alizing system dynamics, has been utilized to measure

the complexity of EGM signals during AF [82]. The

phase space was reconstructed rather pragmatically. Amore sophisticated implementation of RQA has been

used to detect EGM fractionation [83].

4) Electrogram Fractionation: Ablation based on EGM

fractionation was introduced in 2004 [84], where a com-

plex fractionated atrial electrogram (CFAE) was defined

as an EGM composed of two or more deflections and/or



perturbation of the baseline with continuous deflection,or an EGM with a very short AFCL (G 120 ms), averaged

over a 10-s period [84]. In the initial description, CFAEs

were identified by inspection, and thus the technique

was not strictly quantitative.

In an attempt to standardize CFAE-based selection of

ablation targets, automated methods, utilizing a variety

of quantitative approaches have been introduced, and

implemented in commercial electro-anatomic mappingsystems [85]–[87]. However, even with automated soft-

ware, commercially available algorithms require signifi-

cant end-user inputs for thresholds, and parameter

definitions, which has limited replicability of CFAE mea-

surements. Scherr et al. have analyzed CFAE EGMs using

software implemented in the CARTO (Biosense Webster,

USA) system and assessed the number of inter-deflection

intervals falling into the range of 70–120 ms, obtainedfrom sequentially acquired 2.5-s EGM maps. Verma et al.have performed a clinical analysis of automated CFAE

detection on the Ensite NavX (St Jude Medical, USA)

platform, using sequentially acquired 5-s EGMs, where

the algorithm measures the time between discrete EGM

peaks above a user-defined threshold.

A further issue is the spatio–temporal instability of

these sites. Studies using sequential mapping of AF havesuggested temporal stability of the majority of CFAE lo-

cations between sequential maps [88]–[90], but basket

catheter recordings with a stable position have demon-

strated that CFAE sites are highly temporally unstable

and sequential maps failed to identify CFAE sites in one

third of cases [58]. The issue of spatial and temporal sta-

bility of CFAE sites was examined in a recent systematic

review, which demonstrated relative stability of 81% be-tween sequentially acquired maps [91]. However, CFAE

stability was seen on average of 75% in AF recordings

with a mean duration of 1.25 min.

Although the properties of CFAEs have been exten-

sively studied, including the influence of recording time

period [92], the spatial relationship between CFAE and

DF locations [26], [50], [51], [93], and signal properties

of CFAE sites [94]–[111], no consensus has emerged re-garding the optimal definition of CFAE [112] and the

clinical application of CFAE-based ablation is currently

limited to selected laboratories.

C. Multivariate Electrogram AnalysisWhile some of the techniques described above have

been used to synthesize spatial maps from EGMs that

were sequentially obtained from different atrial sites,multivariate signal processing regimes that use multielec-

trode recordings aim at assessing the spatio–temporal re-

lationship of atrial activity.

1) Cross-Correlation and Nonparametric Coherence Esti-mation: Early attempts to measure spatial organization of

atrial activation in the time domain involved computing

the cross-correlation functions between LAWs at differ-ent recording sites [38]. The maximum cross-correlation

between brief EGM segments, preprocessed according to

Botteron and Smith, has been shown to decrease with in-

creasing spatial distance [38]. Cross correlation between

EGM from different sites was found to be higher in sinus

rhythm compared to AF and to deteriorate with in-

creased complexity of AF [76]. To deal with nonstatio-

narity and spatio–temporal instability, time-frequencyanalysis of the coherence function of two EGM has been

proposed, using a multitaper method for spectrum esti-

mation [113].

2) Multivariate Autoregressive Models: Autoregressive

(AR) models are frequently used to describe stochastic

processes. Simultaneous recordings of multiple EGMs

sðnÞ ¼ ½s1ðnÞ; . . . ; sNðnÞ�T , acquired from N differentcatheter poles can be modeled as a multivariate autore-

gressive process of order m

sðnÞ ¼Xmk¼1

Aksðn� kÞ þ wðnÞ

where each Ak is an N � N matrix comprising the AR co-

efficients aijðkÞ, i; j ¼ 1; . . . ;N and wðnÞ ¼½w1ðnÞ; . . . ;wNðnÞ�T is a multivariate white noise process

characterized by the diagonal covariance matrixP

w, in

which each diagonal element �2ii defines the variance of

wiðnÞ [114]. From this multivariate AR model, the partial

directed coherence between pairs of EMG channels can

be derived

�i;jðfÞ ¼1�iiAijðfÞffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPN

k¼11�2kk

AkjðfÞ2q

where Aij represents the ði; jÞ-entry of the matrix AðfÞ,being the Fourier transform of the coefficients Ak. Thepartial directed coherence indicates the coupling from

sjðnÞ to siðnÞ at frequency f , viewed in relation to the di-

rect coupling strength of sjðnÞ to all other signals

skðnÞ; k 6¼ j at that frequency. This approach has been

employed for studying spatio–temporal propagation of

atrial activation, using EGMs preprocessed according to

Botteron and Smith. Incorporation of the distance be-

tween recording sites as constraints in the model param-eter estimation has been shown to improve the

identification of propagation patterns in comparison to

conventional least square estimation [115].

3) Hilbert Phase Mapping: Obtaining the instantaneous

phase of atrial signals in multichannel EGM recording is

useful for mapping the spatial propagation of LAWs



across the atria. The instantaneous phase ’ðnÞ of a signalsðnÞ can be obtained from the analytic signal s�ðnÞ, given by

s�ðnÞ ¼ srðnÞ þ jsiðnÞ

where the imaginary part siðnÞ is the Hilbert transform of

the measured signal srðnÞ: ’ðnÞ ¼ arctanðsiðnÞ=sðnÞÞ. TheHilbert transform is characterized by the transfer function

Hð!Þ ¼ �j : 0 G ! �j : �� G ! G 0

�

which results in a 90� shift of the signal at its output. As

meaningful interpretation of ’ðnÞ requires the signal to beof single mode, the complicated nature of EGM waveforms

does not lend itself to Hilbert phase estimation. Empirical

mode decomposition (Hilbert–Huang transform) is a data-

driven approach that has been used decompose the EGM

signal into intrinsic mode functions, which can be individu-

ally subjected to Hilbert transform. A modified version of

empirical mode decomposition for phase-space reconstruc-

tion has been developed, where the intrinsic mode functionrelevant to AF is obtained based on the knowledge of the

DF [116].

Phase mapping techniques have provided the first de-

piction of spiral wave rotors as drivers of cardiac fibrilla-

tion [117], [118]. Spiral waves have been demonstrated

in optically mapped ventricular fibrillation and atrial fi-

brillation in experimental model systems [23], [117],

[118]. Recently, reconstruction of the EGM signal, com-prising few sinusoidal wavelets with a duration of one

AF cycle was proposed for estimating instantaneous

phase using Hilbert transform (Fig. 6). In this approach,

wavelets are generated at instants where the unipolar

EGM waveform slopes downward, reflecting passing of

the activation wave, synthesizing a simple representation

of the activation cycle [119]. The phase coherence in

EGM, estimated using this technique, has been shown todecline with increasing spatial distance [120].

Hilbert-transform-based phase mapping has been sub-

sequently adapted for the mapping of ventricular fibrilla-

tion, both in explanted hearts [121] and in vivo using

sock-electrode-based recording studies in human hearts

[122]. In each of these studies, cardiac ventricular fibril-

lation was shown to include spatio–temporally unstable

rotors as the drivers of the fibrillatory process.

4) Other Techniques: Linear prediction between pair-

wise EGM has been used to measure the degree of ran-

domness within bipolar EGMs [123]. Other approaches

utilizing the concept of linear prediction were based on

single EGMs [76], [96]. Measures of synchronization

have been exploited to quantify the relationship between

simultaneously recorded EGMs, including a conditional-

entropy-based approach [76], and a multivariate expansion

of the waveform similarity index [124]. Other techniques

involve the reconstruction of wavefronts based on the dis-

tance of LATs [125].

V. QUANTITATIVE ELECTROGRAMANALYSIS IN CLINICAL RESEARCH

A paradox in the clinical practice of quantitative EGM

guided ablation is that ablation techniques have largely

evolved in the clinical electrophysiology laboratory ahead

of mechanistic understanding, because the complexity of

AF has largely prevented detailed mapping of the arrhyth-

mia mechanism in individual patients. Contemporaryquantitative EGM-guided ablation has thus developed as a

collection of alternative approaches implemented around

the common endpoint of achieving AF termination during

a catheter ablation procedure [126], [127].

A. Clinical Results of Dominant Frequency AblationDominant frequency analysis has been adapted and

utilized prospectively in a number of studies as a quanti-tative tool to guide AF ablation. In a study utilizing real-

time DF mapping in persistent AF patients, Atienza et al.showed that ablation of sites with high DF was associated

with a reduction in the left-to-right DF gradient, a reduc-

tion in DF following ablation, and a reduced risk of atrial

arrhythmia recurrence [49]. The AF termination was

achieved in 72% of paroxysmal AF patients, but only in

Fig. 6. Schematic of the “sinusoidal recomposition” transformation.

(a) Original signal. (b) Sinusoidal wavelets are created for each

time point of the original signal (signal was downsampled for

clarity). (c) Recomposed signal is a sum of all sinusoidal wavelets.

(d) Corresponding instantaneous phase. Reprinted from [119] with

permission.



11% of persistent AF patients. Verma and coworkers pro-spectively applied DF mapping in persistent AF patients,

where AF termination occurred in two out of 30 patients

[51]. The largest study to assess DF ablation to date, the

RADAR-AF trial, randomized 232 patients to strategies

of high DF site ablation and/or conventional PVI [128].

In persistent AF patients, freedom from atrial arrhythmia

was seen in 67% of patients who underwent PVI plus

high DF site ablation, similar to the 63% of patients whounderwent PVI alone [128]. In paroxysmal AF patients,

the role of DF ablation is an area of ongoing clinical

investigation.

B. Clinical Results of CFAE AblationWide variation in the results of CFAE guided ablation

has been noted in clinical studies. In the initial study,

ablation confined to the CFAE regions led to AF termina-tion in 95% (115/121) of patients and 91% of the patients

were free of atrial arrhythmia and symptoms one year af-

ter the ablation procedure [84]. However, replication of

these promising results has been challenging, with a

number of studies failing to demonstrate substantial in-

cremental benefit with CFAE ablation [129]–[132]. Two

systematic reviews collating results of randomized con-

trolled trials utilizing adjunctive CFAE have demon-strated positive results in nonparoxysmal AF [133], [134].

A meta-analysis of seven trials with 622 patients compar-

ing PVI plus CFAE to PVI alone showed that adjunctive

CFAE ablation yielded a small but statistically significant

increase in sinus rhythm maintenance [133]. In trials in-

cluding paroxysmal AF patients, no benefit was seen in

terms of increasing sinus rhythm maintenance [133]. In

trials reporting nonparoxysmal AF outcomes, there was asignificant benefit with adjunctive CFAE ablation [133].

The net benefit was not uniform, however, with at least

two trials showing no benefit of adjunctive CFAE abla-

tion in nonparoxysmal AF [131], [135].

The role of CFAE ablation was most recently exam-

ined in the STAR-AF 2 trial, which randomized 589 per-

sistent AF patients to PVI, PVI plus CFAE ablation, or

PVI plus linear ablation in the atrium [136]. In contrastto earlier studies, no benefit was seen with adjunctive

CFAE ablation with freedom from atrial arrhythmia of

59% in those undergoing PVI alone, compared to 49%

with PVI plus CFAE [136]. The role of CFAE ablation as

an adjunctive EGM-guided ablation strategy is an area of

ongoing investigation, with further research required to

improve the reproducibility and efficacy.

C. Clinical Results of Hilbert Phase MappingA phase mapping approach has recently been utilized

by Narayan et al. for AF ablation. In this approach, known

as focal impulse and rotor modulation (FIRM), basket

catheters are introduced into the heart for unipolar EGM

recording [137], [138]. The method involves reconstruc-

tion of phase maps based on the Hilbert transform, but

details of the signal processing have not been disclosed[138]. Utilizing FIRM-based ablation, rotors have been

identified in human AF in a number of case series [137],

[139]–[141]. In an initial case series that compared 71 pa-

tients undergoing FIRM-based ablation to 36 patients un-

dergoing conventional PVI, the acute endpoint of AF

slowing or termination was achieved in 86% of FIRM-

guided cases, compared to 20% of FIRM-blinded patients

[137]. At long-term clinical follow-up (median 890 days)patients receiving FIRM-guided ablation maintained a

higher freedom from AF than those undergoing conven-

tional PVI (78% versus 39%) [141]. In one independent

case series, FIRM-guided ablation was associated with a

one year single procedure freedom from AF of 81% [140].

However, in a second independent case series, the positive

results of the FIRM investigators were not confirmed. In

a study of 24 patients undergoing FIRM-guided ablation,AF termination/organization or slowing was achieved in

50% of patients [142]. The EGM characteristics at FIRM

suggested target sites showed neither high DF nor high

Shannon entropy, leading the authors to conclude that ad-

ditional validation of the FIRM technique is required [142].

VI. CONCLUSION AND FUTUREOUTLOOK

There is substantial evidence that AF wavefronts propa-

gate in a nonrandom manner through the atrium and

that there are critical sources that drive AF. The past

decade has seen the evolution of a wide variety of

EGM-based quantitative signal processing approaches. Al-

though significant developments have occurred, transla-tion of each of the techniques into widespread clinical

utilization has been slow, with difficulties encountered in

replication of promising results from single laboratories

in multicenter studies. A systematic comparison of signal

processing techniques and rigorous validation across lab-

oratories will be essential to advance the field. Underly-

ing the scope of the technical challenge are some of the

general limitations and constraints in the field of AFmapping, including the sparsity of mapped points within

the atria, practical constraints in terms of timing of

mapping, and the fact that the mechanisms responsible

for fibrillatory dynamics are yet to be fully elucidated.

The area will likely see significant advancements through

the development of flexible multielectrode catheters en-

abling recording of activation sequences at higher density

[143], robot-assisted systems to enable faster and moreautonomous map point acquisition, and the development

of detailed multimodal-imaging-based patient-specific

models. Together, these technological developments are

likely to lead to improvements in the volume and quality

of signal acquisition during AF, and place quantitative sig-

nal analysis at the center of innovation in AF ablation

techniques. h



Acknowledgment

M. Baumert holds a fellowship from the Australian

Research Council (DP110102049). P. Sanders holds aPractitioner Fellowship from the Australian National

Health and Medical Research Council. A. Ganesan holdsan Australian Early Career Health Practitioner Fellow-

ship from the Australian National Health and Medical

Research Council.

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ABOUT THE AUTHORS

Mathias Baumert (Senior Member, IEEE) re-

ceived the Ph.D. degree in biomedical engineer-

ing from the Ilmenau University of Technology,

Germany, in 2005.

Subsequently, he was awarded the Australian

Postdoctoral Fellowship and the Australian Re-

search Fellowship from the Australian Research

Council. He is currently an Associate Professor at

the School of Electrical & Electronic Engineering,

University of Adelaide, Adelaide, S.A., Australia.

His research interests include processing of electrophysiological sig-

nals, computerized electrocardiography and electroencephalography,

cardiac autonomic modulation, and sleep.

Prashanthan Sanders graduated from the Uni-

versity of Adelaide, Adelaide, S.A., Australia, with

honors and received the Ph.D. degree from the

University of Melbourne, Parkville, Vic., Australia.

He is a Professor at the University of Adelaide.

He is the Director of the Centre for Heart Rhythm

Disorders at the University of Adelaide, the Direc-

tor of Cardiac Electrophysiology and Pacing at the

Royal Adelaide Hospital, and the Group Leader for

Heart Rhythm Disorders at the South Australian

Health and Medical Research Institute. He trained as a Cardiologist at the

Royal Adelaide Hospital before subspecializing in cardiac electrophysiol-

ogy at the Royal Melbourne Hospital, Parkville, Vic., Australia. He has es-

tablished a formidable team of clinicians and researchers who span the

spectrum of research into heart rhythm disorders; ranging from computer

modeling of cardiac arrhythmias, cellular electrophysiology, small and

large animal models of disease to evaluate arrhythmia mechanisms, clini-

cal mechanistic, outcome and more recently population-based studies.

The group has an established clinical trials program and is involved in de-

velopment of novel technologies.

Anand Ganesan graduated in medicine from the

University of Sydney, Sydney, N.S.W., Australia

and received the Ph.D. degree in cardiac electro-

physiology from the Johns Hopkins University,

Baltimore, MD, USA.

He is a Senior Lecturer at the University of

Adelaide, Adelaide, S.A., Australia, and clinical

cardiac electrophysiologist currently based at

Flinders Medical Centre, Bedford Park, S.A.,

Australia. His research interest is in cardiac signal

processing in cardiac clinical electrophysiology, with a specific focus of

atrial fibrillation mapping.



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