Quantitative-Electrogram-Based Methods for Guiding Catheter ...

INVITED PAPER

Quantitative-Electrogram-Based Methods for Guiding Catheter Ablation in Atrial Fibrillation How to identify points for catheter ablation to stop atrial fibrillation? This paper summarizes challenges and recent advances in signal processing guided ablation. By Mathias 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 disorganized electrical activation of the atria. In the past decade, catheter ablation, i.e., focally burning targeted areas of cardiac muscle, has emerged as a potentially curative therapy for AF. Accompanying this development there has been an increasing interest in quantitative intraprocedural signal analysis to guide the selection of ablation targets. In this review, we provide an overview of quantitative signal processing approaches for mapping and ablation of AF. KEYWORDS

|

Atrial

fibrillation

(AF);

catheter

ablation;

electrogram

I. BACKGROUND Atrial fibrillation (AF) is the most common cardiac arrhythmia in humans, with an estimated lifetime prevalence 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 electrical activation of the atria that results in rapid and irregular contractions of the ventricles. AF typically manifests clinically as palpitations and chest discomfort. 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, The University of Adelaide, Adelaide, S.A. 5000, Australia (e-mail: mathias. [email protected]). P. Sanders and A. Ganesan are with the Centre for Heart Rhythm Disorders, South Australian Health and Medical Research Institute, University of Adelaide and Royal Adelaide Hospital, Adelaide, S.A. 5000, Australia (e-mail: [email protected]). Digital Object Identifier: 10.1109/JPROC.2015.2505318

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 amplified by health-related costs associated with hospitalization. In the United States alone, over 400000 patients are hospitalized for AF annually, with total costs exceeding $US 3.5 billion [7]. Pharmacological control of AF is frequently ineffective and/or poorly tolerated and may be associated with significant side effects [1]. In the past decade, 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]. Accompanying the development of AF ablation there has been a concomitantly increasing interest in quantitative intraprocedural signal analysis approaches to guide the selection of 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.

I I. ATRIAL FIBRILLATION: PHYSI OLOGY, MECHANI SM S, AND TREATMENT A. Cardiac Anatomy and Electrophysiology The human heart comprises four chambers, consisting 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 contraction, the atria have an important role in coordinating flow of blood through the heart in different phases of the

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

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

cardiac cycle. During ventricular contraction, the atria act 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 ventricles is regulated by specialized areas of electrically excitable 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 normal physiologic rhythm (i.e., sinus rhythm) electrical impulses 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 propagation to flow from the atria to ventricles, as the atria and ventricles are otherwise electrically isolated from each other by the fibrous rings of the atrio–ventricular valves. Electrical activation passes through the atrio–ventricular node to the ventricles utilizing a specialized conduction tissue 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 expulsion of blood from the heart. During AF, this sequence of cardiac electrical activation and subsequent coordinated ventricular contraction is disrupted and the atrial activation frequency increases dramatically from 1–2 Hz (i.e., 60–120 beats/min during normal sinus rhythm) to 6–10 Hz. The timing of activation in different parts of the chamber becomes spatially asynchronous, resulting in irregular and uncoordinated atrial contractions 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 highfrequency 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 some patients 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 Fibrillation Despite over a century of research there remains an ongoing debate regarding the fundamental mechanisms underlying atrial fibrillatory dynamics [9]. At present, a consensus has emerged, proposing that AF is the consequence of an incompletely understood interplay between triggering mechanisms and substrate [10], [11]. Based on clinical and experimental findings, the predominant triggering mechanism for AF is believed to be an ectopic electrical discharge arising from the pulmonary veins [8],

[12]–[14]. Atrial fibrillation commonly arises in the context of a substrate of electrophysiological and structural remodeling 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 expression of ion channels in cardiac myocytes and the development 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 maintained by the precession of a discrete number of electrical waves. It arises as a consequence of functional inhomogeneity in the local electrical excitability of atrial tissue [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 or small 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 computational simulations (Fig. 1) [24]. According to this theory, the disorganization and spatio–temporal irregularity of electrical activity seen in AF arises as a consequence of the interaction of these waves with functional or anatomical boundaries [25], [26]. Computational models and simulations have significantly informed mechanistic understanding of the fibrillatory 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 for modeling 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 arrangements. Computational models of AF have been valuable in elucidating cellular mechanisms involved in the pharmacological control of heart rate, for devising pacing algorithms for AF termination and optimizing catheter ablation strategies [24].

C. Clinical Ablation Procedures Catheter ablation procedures are performed in specialized 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


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.

catheters used in ablation procedures are typically 1.5 m long and have platinum-alloy electrodes at the tip, designed for percutaneous introduction into the body, most commonly via the femoral vein at the upper leg. During catheter ablation procedures, physicians manually maneuver catheters within the heart to specific locations under the guidance of X-ray fluoroscopy. In recent years, however, a variety of electro-anatomic mapping technologies have evolved to enable 3-D anatomical reconstruction 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 simultaneously record electrical signals from intracardiac catheters, 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 interpret cardiac activity and identify ablation targets. 418


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 ectopic impulses from this anatomical location from triggering AF episodes (Fig. 2) [8], [29]. Significant evidence has accumulated, however, suggesting that long-term outcomes in patients with longer lasting, persistent 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 intraprocedurally acquired EGM, meaningful information is extracted to assists cardiac electrophysiologists in identifying 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. 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.

catheters that are introduced into the heart usually record signals from a limited number of electrodes. Therefore, mapping of activation patterns in AF is typically performed sequentially instead of simultaneously mapping the entire atrial surface. Second, the catheters allow recording of signals only from the endocardial surface and 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 timing. Fourth, the spatial accessibility of mapping procedures is limited by the fact that the catheters are maneuvered within enclosed cardiac chambers. Finally, practical and logistical constraints restrict clinical mapping to discrete regions of the atrium to relatively short periods of time.

II I. SIGNAL ACQUISITION AND PRE PROCE SS ING A. Intracardiac Electrogram Acquisition Critical to the development of quantitative intraprocedural EGM analysis is an understanding of the acquisition 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 depolarization of the cardiac muscle due to differences in the axial voltage gradient at the border zone between activated and inactivated cells. Cardiac EGM recordings are typically obtained by differential amplifiers with a high input impedance and a good common mode rejection. By convention, in the unipolar recording configuration, the recording electrode on the tip of the intracardiac catheter and in physical contact with the cardiac muscle is connected with the anodal input of the amplifier. The cathodal input is connected to an indeterminate electrode that registers minimal cardiac signal in theory. This recording configuration 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 tip electrode. 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 directly underneath the electrode. A limitation of the unipolar 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 activity. In the case of atrial EGM recordings, the primary issue is overlap with electrical activity caused by ventricular depolarization. To a large extent, these issues have hindered the utilization of unipolar EGMs for clinical mapping during AF, although an extensive literature has developed around the use of unipolar recording in research settings [18], [32], [33]. In clinical AF mapping, most EGMs are recorded in the bipolar mode. Bipolar EGMs are created by subtracting two unipolar EGMs recorded at proximate sites, typically 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 recordings, the timing of local electrical activation is less well defined [34].

B. Electrogram Morphology in Atrial Fibrillation The EGM morphology during sinus rhythm and AF is an important consideration in the development of quantitative analytical approaches. In sinus rhythm, unipolar endocardial EGMs display predominantly negative deflections and a relatively uniform morphologic appearance. 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 morphologies during AF [35] (Fig. 4). Type 1 AF is Vol. 104, No. 2, February 2016 | Proceedings of the IEEE

419


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).

characterized by discrete atrial complexes, of variable morphological appearance, but with a discrete isoelectric 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 irregular atrial EGMs that fail to exhibit either discrete complexes or isoelectric intervals. An important concept in the morphological descriptions of EGMs during AF is that of fractionation. Although 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 prolonged duration compared to “organized” EGMs. A number of different physiological processes are thought to contribute to EGM fractionation observed in human AF. 420


These include slow conduction through areas of scarring, curved or turning wavefronts and so-called anisotropic conduction in areas of tissue where conduction is more rapid longitudinally along muscle fibers, than perpendicularly between adjacent muscle strands [36]. Fractionation can also occur for reasons other than abnormal tissue properties, e.g., in regions with anatomically overlaying areas of tissue, It can be introduced artificially by inappropriate signal high-pass filtering or external electromagnetic interference [36]. Separating the features pertinent to the maintenance of AF that are relevant as ablation targets is an ongoing challenge in the field.

C. Electrogram Filtering As with other bioelectrical signals, EGMs are contaminated with noise, originating from various internal and


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.

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 shifts and 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 influence of ventricular far-field artefacts [37]. Many EGM analysis techniques require a strong simplification of the atrial signal, primarily aiming at the extraction of the envelope of the signal. Therefore, Botteron and Smith have proposed to rectify the bandpass 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 ELECTROGRAM ANALYSIS 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 approaches 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), is based 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 bipolar EGM excursion from baseline, the time of the maximum slope in either direction, or the time the sloping segment containing the maximum slope in either direction crossed the baseline (fastest zero crossing) [40]. Examples 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 computed by averaging the EGM outside a 120-ms subwindow centered at the extreme value in the analysis window. Extrema associated with notches narrower than 4 ms are excluded. The LAT is determined from the waveform morphology by defining primary and secondary phases. The primary phase is obtained by finding the peak excursion from the baseline and searching backward for the start of the phase and forward for the end. The temporal limits Vol. 104, No. 2, February 2016 | Proceedings of the IEEE

421


of phases are defined by a change in slope sign, baseline crossing, 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 middle phase. If more than three phases are found, the LAT is assigned 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 barycenter of LAWs in bipolar EGMs [42]. After ventricular artefact rejection and application of the signal filtering procedure proposed by Botteron and Smith [38] to the atrial signal sðnÞ, resulting in sw ðnÞ, 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 calculated as the time that divides the local area of the modulus of the signal in two equal parts. A noncausal moving average filter with 90 Pcoefficients is applied P45 to the modulus of sðnÞ: sf ðnÞ ¼ 44 i¼0 jsðn iÞj i¼1 jsðn þ iÞj. The activation time is set on the positive zero crossing of sf ðtÞ that is closer to the local peak of sw ðnÞ. Ng et al. recently devised an iterative method for measuring LAT and AFCL [43]. Following preprocessing similar to the one proposed by Botteron and Smith, the peak with the highest magnitude is detected as the first LAT. After excluding all neighboring peaks within a 50-ms blanking 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 either 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 the set and the procedure repeated until there are no more intervals 9 1:5 median CL with peaks between them.

Typical time frames for frequency domain analysis range between 2.5 and 8 s. While shorter time frames offer increased 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 deflections in bipolar EGM [47] and is considered a surrogate for the local activation rate [23] (Fig. 5). A so-called regularity 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 furthermore, 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 physiological activation rates [54]. An alternative approach for

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 applying the discrete Fourier transform to decompose N samples of sw ðnÞ into k frequency components [45], [46]

Sw ðkÞ ¼

N X

2jkn N

sw ðnÞe

:

n¼0

422


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 averaging of EGMs for different window lengths and subsequent 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 domain analysis for estimating DF [53] and a poor correlation of DF with the local cycle length measured in the time domain has been reported [56]. In view of AF ablation mapping, 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 centrifugal gradients surrounding sites of high DF [57], others have reported significant instability of DF during prolonged recordings [58], [59] and have failed to observe a centrifugal 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

1 Wð; aÞ ¼ pffiffiffi a

Z1 1

t xðtÞ dt a

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, unipolar EGMs have been analyzed, using the first derivative of the Gaussian function as the mother wavelet, resembling 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 extracted with an algorithm that comprises template matching, using a library of automatically generated templates, and a thresholding procedure [60]. The continuous 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 coefficients, 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. proposed 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 subsequently normalized to their standard norm: xî ¼ xi =jxi j. Morphological dissimilarity between pairs of standardized LAWs are computed as the angle of associated vectors: dð^ xi ; xî Þ ¼ cos1 ð^ xi x^j Þ. The similarity index is then defined as

ð"Þ ¼

N X N X 2 ð dð^ xi ; xî ÞÞ NðN 1Þ i¼1 j¼iþ1

where is the Heaviside function [ðxÞ ¼ 0 for x G 0 and ðxÞ ¼ 1 for x 0], " is the threshold value, empirically 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 track changes 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 similarity have been constructed and distinct regions of high similarity that anchored to anatomy were demonstrated in patients with paroxysmal AF, while maps of low similarity were observed in maps of patients with chronic AF [65]. Fusion of computed tomography images with similarity index and AFCL maps have allowed identifying anatomical 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 operator maps have been recently published [66]. Recently, similarity analysis based on the cross correlation 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 described above. Periodic EGM recurrence was present at all recording sites and highly repetitive LAW morphologies were found to be important for maintaining AF. 2) Entropy: Shannon entropy, the classical measure of P information theory, is defined as SE ¼ M i¼1 pðiÞ log2 pðiÞ, where M is the number of discrete values the variable can assume and pðiÞ is the probability of assuming the ith value. From a finite set of N observations Shannon entropy can be estimated as

b ¼ SE

M X i¼1

p^ðiÞ log2 p^ðiÞ þ

M1 2N

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

423


where p^ðiÞ is the maximum-likelihood estimate and M is b of the number of bins with nonzero probability. The SE 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 synchronization index has been defined as

A regularity index defined by means of conditional entropy (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 M N X X p sJL1 p CEðLÞ ¼ j¼1

Sy ¼ 1

b SE : log2 N

Higher Sy values of time delays have been related to higher complexity of wave propagation patterns in computer simulations, and to higher EGM complexity in patients with paroxysmal AF [68]. The SE has been used to measure AF complexity directly in bipolar EGMs thereby circumventing the need for delineating LATs and related issues [69]. Pivots of rotational atrial activations have been shown to coincide with high SE values [70]. Based on the results of computer simulations, multielectrode array recordings of isolated rat atria, epicardial plaque recordings of hypertensive sheep, and high density mapping of AF patients, SE mapping has been proposed to assist in AF rotor identification [69]. In 2-D spiral wave simulations obtained with different cell models, SE has been consistently highest at the pivot across all investigated models and independent of electrode spacing, electrode orientation, 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 patterns similar to each other will remain similar in the next incremental comparison and is defined as

ApEnðSN;m;r Þ ¼ ln

Cm ðrÞ Cmþ1 ðrÞ

where m is the pattern length, r is the criterion of similarity, and Cm ðrÞ the prevalence of repetitive patterns of length m in the sequence SN . High ApEn has been correlated with EGM fractionation in a database of AF recordings [72], [73]. In a 3-D electro-anatomical computer model 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 entropy (a modified version of ApEn) obtained from unipolar right epicardial EGM has been shown to correlate with DF and could be estimated from surface ECG [74]. 424


i¼1

! si sJL1

! log p

si sJL1

where sJL1 represents the Jth pattern of length L 1, pðsJL1 Þ is its probability, and pðsi =sJL1 Þ is the conditional probability of the sample si given the pattern sJL1 [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 discriminate 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 principal component analysis [78], where multichannel EGM were decomposed into their orthogonal constituents, distribution 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 nonlinear dynamics 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 delays, where the phase space is represented as a vector SðnÞ ¼ ½sðnÞ; sðnLÞ; sðn2LÞ; . . . ; sðnð 1ÞLÞT and the embedding dimension and reconstruction delay L have to be empirically determined. The noisy, nonstationary nature of EGMs and unknown dimensionality generally thwart faithful reconstruction of the dynamics of the underlying system. Recurrence quantification analysis (RQA), which is based on recurrence plots for visualizing system dynamics, has been utilized to measure the complexity of EGM signals during AF [82]. The phase space was reconstructed rather pragmatically. A more 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 complex 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 mapping systems [85]–[87]. However, even with automated software, commercially available algorithms require significant end-user inputs for thresholds, and parameter definitions, which has limited replicability of CFAE measurements. 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, obtained from 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 have suggested temporal stability of the majority of CFAE locations between sequential maps [88]–[90], but basket catheter recordings with a stable position have demonstrated 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 stability of CFAE sites was examined in a recent systematic review, which demonstrated relative stability of 81% between 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 extensively 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 regarding the optimal definition of CFAE [112] and the clinical application of CFAE-based ablation is currently limited to selected laboratories.

the cross-correlation functions between LAWs at different recording sites [38]. The maximum cross-correlation between brief EGM segments, preprocessed according to Botteron and Smith, has been shown to decrease with increasing 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 increased complexity of AF [76]. To deal with nonstationarity and spatio–temporal instability, time-frequency analysis of the coherence function of two EGM has been proposed, using a multitaper method for spectrum estimation [113]. 2) Multivariate Autoregressive Models: Autoregressive (AR) models are frequently used to describe stochastic processes. Simultaneous recordings of multiple EGMs sðnÞ ¼ ½s1 ðnÞ; . . . ; sN ðnÞT , acquired from N different catheter poles can be modeled as a multivariate autoregressive process of order m

sðnÞ ¼

m X

Ak sðn kÞ þ wðnÞ

k¼1

where each Ak is an N N matrix comprising the AR coefficients aij ðkÞ, i; j ¼ 1; . . . ; N and wðnÞ ¼ ½w1 ðnÞ; . . . ; wN ðnÞT is a multivariate white noise P process characterized by the diagonal covariance matrix w , in which each diagonal element 2ii defines the variance of wi ðnÞ [114]. From this multivariate AR model, the partial directed coherence between pairs of EMG channels can be derived 1 ii Aij ðf Þ ffi i;j ðf Þ ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi PN 1 2 k¼1 2 Akj ðf Þ kk

C. Multivariate Electrogram Analysis While 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 multielectrode recordings aim at assessing the spatio–temporal relationship of atrial activity.

where Aij represents the ði; jÞ-entry of the matrix Aðf Þ, being the Fourier transform of the coefficients Ak . The partial directed coherence indicates the coupling from sj ðnÞ to si ðnÞ at frequency f , viewed in relation to the direct coupling strength of sj ðnÞ to all other signals sk ðnÞ; 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 between recording sites as constraints in the model parameter estimation has been shown to improve the identification of propagation patterns in comparison to conventional least square estimation [115].

1) Cross-Correlation and Nonparametric Coherence Estimation: Early attempts to measure spatial organization of atrial activation in the time domain involved computing

3) Hilbert Phase Mapping: Obtaining the instantaneous phase of atrial signals in multichannel EGM recording is useful for mapping the spatial propagation of LAWs Vol. 104, No. 2, February 2016 | Proceedings of the IEEE

425


across the atria. The instantaneous phase ’ðnÞ of a signal sðnÞ can be obtained from the analytic signal s ðnÞ, given by s ðnÞ ¼ sr ðnÞ þ jsi ðnÞ where the imaginary part si ðnÞ is the Hilbert transform of the measured signal sr ðnÞ: ’ðnÞ ¼ arctanðsi ðnÞ=sðnÞÞ. The Hilbert 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 be of single mode, the complicated nature of EGM waveforms does not lend itself to Hilbert phase estimation. Empirical mode decomposition (Hilbert–Huang transform) is a datadriven approach that has been used decompose the EGM signal into intrinsic mode functions, which can be individually subjected to Hilbert transform. A modified version of empirical mode decomposition for phase-space reconstruction has been developed, where the intrinsic mode function relevant to AF is obtained based on the knowledge of the DF [116]. Phase mapping techniques have provided the first depiction of spiral wave rotors as drivers of cardiac fibrillation [117], [118]. Spiral waves have been demonstrated in optically mapped ventricular fibrillation and atrial fibrillation in experimental model systems [23], [117], [118]. Recently, reconstruction of the EGM signal, comprising 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 to decline with increasing spatial distance [120]. Hilbert-transform-based phase mapping has been subsequently adapted for the mapping of ventricular fibrillation, 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 fibrillation was shown to include spatio–temporally unstable rotors as the drivers of the fibrillatory process. 4) Other Techniques: Linear prediction between pairwise EGM has been used to measure the degree of randomness 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 426


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.

simultaneously recorded EGMs, including a conditionalentropy-based approach [76], and a multivariate expansion of the waveform similarity index [124]. Other techniques involve the reconstruction of wavefronts based on the distance of LATs [125].

V. QUANTITATI VE ELECTROGRAM ANALYSIS IN CLINICAL RE SEARCH 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 arrhythmia mechanism in individual patients. Contemporary quantitative 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 Ablation Dominant frequency analysis has been adapted and utilized prospectively in a number of studies as a quantitative tool to guide AF ablation. In a study utilizing realtime 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 reduction 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


11% of persistent AF patients. Verma and coworkers prospectively 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 who underwent PVI alone [128]. In paroxysmal AF patients, the role of DF ablation is an area of ongoing clinical investigation.

B. Clinical Results of CFAE Ablation Wide 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 termination in 95% (115/121) of patients and 91% of the patients were free of atrial arrhythmia and symptoms one year after the ablation procedure [84]. However, replication of these promising results has been challenging, with a number of studies failing to demonstrate substantial incremental benefit with CFAE ablation [129]–[132]. Two systematic reviews collating results of randomized controlled trials utilizing adjunctive CFAE have demonstrated positive results in nonparoxysmal AF [133], [134]. A meta-analysis of seven trials with 622 patients comparing 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 including paroxysmal AF patients, no benefit was seen in terms of increasing sinus rhythm maintenance [133]. In trials reporting nonparoxysmal AF outcomes, there was a significant benefit with adjunctive CFAE ablation [133]. The net benefit was not uniform, however, with at least two trials showing no benefit of adjunctive CFAE ablation in nonparoxysmal AF [131], [135]. The role of CFAE ablation was most recently examined in the STAR-AF 2 trial, which randomized 589 persistent AF patients to PVI, PVI plus CFAE ablation, or PVI plus linear ablation in the atrium [136]. In contrast to 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 Mapping A 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 reconstruction 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 patients undergoing FIRM-based ablation to 36 patients undergoing conventional PVI, the acute endpoint of AF slowing or termination was achieved in 86% of FIRMguided 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 conventional 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 additional validation of the FIRM technique is required [142].

VI . CONCLUSION AND FUTURE OUT L OOK There is substantial evidence that AF wavefronts propagate 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. Although significant developments have occurred, translation 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 laboratories will be essential to advance the field. Underlying the scope of the technical challenge are some of the general limitations and constraints in the field of AF mapping, 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 enabling recording of activation sequences at higher density [143], robot-assisted systems to enable faster and more autonomous 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 signal analysis at the center of innovation in AF ablation techniques. h Vol. 104, No. 2, February 2016 | Proceedings of the IEEE

427


Acknowledgment M. Baumert holds a fellowship from the Australian Research Council (DP110102049). P. Sanders holds a Practitioner Fellowship from the Australian National REFERENCES [1] C. T. January et al., “2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society,” J. Amer. Coll. Cardiol., vol. 64, pp. e1–e76, Dec. 2, 2014. [2] D. M. Lloyd-Jones et al., “Lifetime risk for development of atrial fibrillation: The Framingham Heart Study,” Circulation, vol. 110, pp. 1042–1046, Aug. 31, 2004. [3] S. S. Chugh et al., “Worldwide epidemiology of atrial fibrillation: A Global Burden of Disease 2010 Study,” Circulation, vol. 129, pp. 837–847, Feb. 25, 2014. [4] T. J. Wang et al., “Temporal relations of atrial fibrillation and congestive heart failure and their joint influence on mortality: The Framingham Heart Study,” Circulation, vol. 107, pp. 2920–2925, Jun. 17, 2003. [5] P. A. Wolf, R. D. Abbott, and W. B. Kannel, “Atrial fibrillation as an independent risk factor for stroke: The Framingham Study,” Stroke, vol. 22, pp. 983–988, Aug. 1991. [6] A. Ott et al., “Atrial fibrillation and dementia in a population-based study. The Rotterdam Study,” Stroke, vol. 28, pp. 316–321, Feb. 1997. [7] N. J. Patel et al., “Contemporary trends of hospitalization for atrial fibrillation in the United States, 2000 through 2010: Implications for healthcare planning,” Circulation, vol. 129, pp. 2371–2379, Jun. 10, 2014. [8] M. Haissaguerre et al., “Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins,” New England J. Med., vol. 339, pp. 659–666, Sep. 3, 1998. [9] U. Schotten, S. Verheule, P. Kirchhof, and A. Goette, “Pathophysiological mechanisms of atrial fibrillation: A translational appraisal,” Physiol. Rev., vol. 91, pp. 265–325, Jan. 2011. [10] S. Nattel, B. Burstein, and D. Dobrev, “Atrial remodeling and atrial fibrillation: Mechanisms and implications,” Circulation: Arrhythmia Electrophysiol., vol. 1, pp. 62–73, Apr. 2008. [11] S. Nattel and M. Harada, “Atrial remodeling and atrial fibrillation: Recent advances and translational perspectives,” J. Amer. Coll. Cardiol., Mar. 5, 2014. [12] M. Haissaguerre et al., “Catheter ablation of chronic atrial fibrillation targeting the reinitiating triggers,” J. Cardiovasc. Electrophysiol., vol. 11, pp. 2–10, Jan. 2000. [13] M. Haissaguerre et al., “Electrophysiological breakthroughs from the left atrium to the pulmonary veins,” Circulation, vol. 102, pp. 2463–2465, Nov. 14, 2000. [14] M. Haissaguerre et al., “Mapping-guided ablation of pulmonary veins to cure atrial fibrillation,” Amer. J. Cardiol., vol. 86, pp. 9K–19K, Nov. 2, 2000. [15] M. C. Wijffels, C. J. Kirchhof, R. Dorland, J. Power, and M. A. Allessie, “Electrical remodeling due to atrial fibrillation in

428

[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]

[25]

[26]

[27]

Health and Medical Research Council. A. Ganesan holds an Australian Early Career Health Practitioner Fellowship from the Australian National Health and Medical Research Council.

chronically instrumented conscious goats: Roles of neurohumoral changes, ischemia, atrial stretch, high rate of electrical activation,” Circulation, vol. 96, pp. 3710–3720, Nov. 18, 1997. G. K. Moe, W. C. Rheinboldt, and J. A. Abildskov, “A computer model of atrial fibrillation,” Amer. Heart J., vol. 67, pp. 200–220, Feb. 1964. F. Ravelli et al., “Anatomic localization of rapid repetitive sources in persistent atrial fibrillation: Fusion of biatrial CT images with wave similarity/cycle length maps,” J. Amer. Coll. Cardiol., Cardiovasc. Imag., vol. 5, pp. 1211–1220, Dec. 2012. K. T. Konings et al., “High-density mapping of electrically induced atrial fibrillation in humans,” Circulation, vol. 89, pp. 1665–1680, Apr. 1994. J. Eckstein et al., “Time course and mechanisms of endo-epicardial electrical dissociation during atrial fibrillation in the goat,” Cardiovasc. Res., vol. 89, pp. 816–824, Mar. 1, 2011. J. Eckstein et al., “Transmural conduction is the predominant mechanism of breakthrough during atrial fibrillation: Evidence from simultaneous endo-epicardial high-density activation mapping,” Circulation: Arrhythmia Electrophysiol., vol. 6, pp. 334–341, Apr. 2013. J. Jalife, “Deja vu in the theories of atrial fibrillation dynamics,” Cardiovasc. Res., vol. 89, pp. 766–775, Mar. 1, 2011. A. C. Skanes, R. Mandapati, O. Berenfeld, J. M. Davidenko, and J. Jalife, “Spatiotemporal periodicity during atrial fibrillation in the isolated sheep heart,” Circulation, vol. 98, pp. 1236–1248, Sep. 22, 1998. R. Mandapati, A. Skanes, J. Chen, O. Berenfeld, and J. Jalife, “Stable microreentrant sources as a mechanism of atrial fibrillation in the isolated sheep heart,” Circulation, vol. 101, pp. 194–199, Jan. 18, 2000. N. A. Trayanova, “Mathematical approaches to understanding and imaging atrial fibrillation: Significance for mechanisms and management,” Circ. Res., vol. 114, pp. 1516–1531, Apr. 25, 2014. O. Berenfeld, A. V. Zaitsev, S. F. Mironov, A. M. Pertsov, and J. Jalife, “Frequencydependent breakdown of wave propagation into fibrillatory conduction across the pectinate muscle network in the isolated sheep right atrium,” Circ. Res., vol. 90, pp. 1173–1180, Jun. 14, 2002. J. Kalifa et al., “Mechanisms of wave fractionation at boundaries of highfrequency excitation in the posterior left atrium of the isolated sheep heart during atrial fibrillation,” Circulation, vol. 113, pp. 626–633, Feb. 7, 2006. L. Gepstein, G. Hayam, and S. A. Ben-Haim, “A novel method for nonfluoroscopic catheter-based electroanatomical mapping of the heart. In vitro and in vivo accuracy results,” Circulation, vol. 95, pp. 1611–1622, Mar. 18, 1997.


[28] A. Kadish, J. Hauck, B. Pederson, G. Beatty, and C. Gornick, “Mapping of atrial activation with a noncontact, multielectrode catheter in dogs,” Circulation, vol. 99, pp. 1906–1913, Apr. 13, 1999. [29] H. Calkins et al., “2012 HRS/EHRA/ECAS expert consensus statement on catheter and surgical ablation of atrial fibrillation,” Heart Rhythm, vol. 9, pp. 632–696, Apr. 2012. [30] R. Weerasooriya et al., “Catheter ablation for atrial fibrillation: Are results maintained at 5 years of follow-up?” J. Amer. Coll. Cardiol., vol. 57, pp. 160–166, Jan. 11, 2011. [31] A. N. Ganesan et al., “Long-term outcomes of catheter ablation of atrial fibrillation: A systematic review and meta-analysis,” J. Amer. Heart Assoc., vol. 2, Apr. 2013, Art. ID e004549. [32] M. A. Allessie et al., “Electropathological substrate of long-standing persistent atrial fibrillation in patients with structural heart disease: Longitudinal dissociation,” Circulation: Arrhythmia Electrophysiol., vol. 3, pp. 606–615, Dec. 1, 2010. [33] K. T. Konings, J. L. Smeets, O. C. Penn, H. J. Wellens, and M. A. Allessie, “Configuration of unipolar atrial electrograms during electrically induced atrial fibrillation in humans,” Circulation, vol. 95, pp. 1231–1241, Mar. 4, 1997. [34] M. Shenasa, G. Hindricks, M. Borggrefe, and G. Breithardt, Cardiac Mapping. New York, NY, USA: Wiley, 2009. [35] J. L. Wells, Jr. et al., “Characterization of atrial fibrillation in man: Studies following open heart surgery,” Pacing Clin. Electrophysiol., vol. 1, pp. 426–438, Oct. 1978. [36] J. M. de Bakker and F. H. Wittkampf, “The pathophysiologic basis of fractionated and complex electrograms and the impact of recording techniques on their detection and interpretation,” Circulation: Arrhythmia Electrophysiol., vol. 3, pp. 204–213, 2010. [37] S. Shkurovich, A. V. Sahakian, and S. Swiryn, “Detection of atrial activity from high-voltage leads of implantable ventricular defibrillators using a cancellation technique,” IEEE Trans. Biomed. Eng., vol. 45, pp. 229–234, Feb. 1998. [38] G. W. Botteron and J. M. Smith, “A technique for measurement of the extent of spatial organization of atrial activation during atrial fibrillation in the intact human heart,” IEEE Trans. Biomed. Eng., vol. 42, pp. 579–586, Jun. 1995. [39] B. J. Scherlac, P. Samet, and R. H. Helfant, “His bundle electrogram. A critical appraisal of its uses and limitations,” Circulation, vol. 46, pp. 601–613, Sep. 1972. [40] C. F. Pieper, R. Blue, and A. Pacifico, “Simultaneously collected monopolar and discrete bipolar electrograms: Comparison of activation time detection algorithms,” Pacing Clin. Electrophysiol., vol. 16, pp. 426–433, Mar. 1993. [41] C. F. Pieper, R. Blue, and A. Pacifico, “Simultaneously collected monopolar and discrete bipolar electrograms: Comparison


[42]

[43]

[44]

[45]

[46]

[47]

[48]

[49]

[50]

[51]

[52]

[53]

[54]

of activation time detection algorithms,” Pacing Clin. Electrophysiol., vol. 16, pp. 426–433, Mar. 1993. L. Faes, G. Nollo, R. Antolini, F. Gaita, and F. Ravelli, “A method for quantifying atrial fibrillation organization based on wave-morphology similarity,” IEEE Trans. Biomed. Eng., vol. 49, pp. 1504–1513, Dec. 2002. J. Ng, V. Sehgal, J. K. Ng, D. Gordon, and J. J. Goldberger, “Iterative method to detect atrial activations and measure cycle length from electrograms during atrial fibrillation,” IEEE Trans. Biomed. Eng., vol. 61, pp. 273–278, Feb. 2014. O. Berenfeld et al., “Spatially distributed dominant excitation frequencies reveal hidden organization in atrial fibrillation in the Langendorff-perfused sheep heart,” J. Cardiovasc. Electrophysiol., vol. 11, pp. 869–879, Aug. 2000. T. H. Everett, L. C. Kok, R. H. Vaughn, J. R. Moorman, and D. E. Haines, “Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy,” IEEE Trans. Biomed. Eng., vol. 48, pp. 969–978, Sep. 2001. J. Ng and J. J. Goldberger, “Understanding and interpreting dominant frequency analysis of AF electrograms,” J. Cardiovasc. Electrophysiol., vol. 18, pp. 680–685, Jun. 2007. G. Fischer et al., “On computing dominant frequency from bipolar intracardiac electrograms,” IEEE Trans. Biomed. Eng., vol. 54, pp. 165–169, Jan. 2007. T. H. Everett, J. R. Moorman, L. C. Kok, J. G. Akar, and D. E. Haines, “Assessment of global atrial fibrillation organization to optimize timing of atrial defibrillation,” Circulation, vol. 103, pp. 2857–2861, Jun. 12, 2001. F. Atienza et al., “Real-time dominant frequency mapping and ablation of dominant frequency sites in atrial fibrillation with left-to-right frequency gradients predicts long-term maintenance of sinus rhythm,” Heart Rhythm, vol. 6, pp. 33–40, Jan. 2009. M. K. Stiles et al., “High-density mapping of atrial fibrillation in humans: Relationship between high-frequency activation and electrogram fractionation,” J. Cardiovasc. Electrophysiol., vol. 19, pp. 1245–1253, Dec. 2008. A. Verma et al., “Relationship between complex fractionated electrograms (CFE) and dominant frequency (DF) sites and prospective assessment of adding DF-guided ablation to pulmonary vein isolation in persistent atrial fibrillation (AF),” J. Cardiovasc. Electrophysiol., vol. 22, pp. 1309–1316, Dec. 2011. J. Ng, A. H. Kadish, and J. J. Goldberger, “Technical considerations for dominant frequency analysis,” J. Cardiovasc. Electrophysiol., vol. 18, pp. 757–764, Jul. 2007. J. Ng, A. H. Kadish, and J. J. Goldberger, “Effect of electrogram characteristics on the relationship of dominant frequency to atrial activation rate in atrial fibrillation,” Heart Rhythm, vol. 3, pp. 1295–1305, Nov. 2006. P. Sanders et al., “Spectral analysis identifies sites of high-frequency activity maintaining atrial fibrillation in humans,” Circulation, vol. 112, pp. 789–797, Aug. 9, 2005.

[55] E. J. Ciaccio, A. B. Biviano, and H. Garan, “Optimization of novel spectral estimator for fractionated electrogram analysis is helpful to discern atrial fibrillation type,” Comput. Methods Programs Biomed., vol. 117, pp. 343–350, Nov. 2014. [56] A. Elvan et al., “Dominant frequency of atrial fibrillation correlates poorly with atrial fibrillation cycle length,” Circulation: Arrhythmia Electrophysiol., vol. 2, pp. 634–644, Dec. 2009. [57] D. E. Krummen et al., “Correlation of electrical rotors and focal sources with sites of centrifugal stepdown in dominant frequency in human atrial fibrillation,” Heart Rhythm, vol. 8, p. S176, 2011. [58] N. Habel et al., “The temporal variability of dominant frequency and complex fractionated atrial electrograms constrains the validity of sequential mapping in human atrial fibrillation,” Heart Rhythm, vol. 7, pp. 586–593, May 2010. [59] J. W. Jarman et al., “Spatiotemporal behavior of high dominant frequency during paroxysmal and persistent atrial fibrillation in the human left atrium,” Circulation: Arrhythmia Electrophysiol., vol. 5, pp. 650–658, Aug. 1, 2012. [60] R. P. Houben, N. M. de Groot, and M. A. Allessie, “Analysis of fractionated atrial fibrillation electrograms by wavelet decomposition,” IEEE Trans. Biomed. Eng., vol. 57, pp. 1388–1398, Jun. 2010. [61] J. Zhao et al., “Novel methods for characterization of paroxysmal atrial fibrillation in human left atria,” Open Biomed. Eng. J., vol. 7, pp. 29–40, 2013. [62] V. Kremen et al., “A new approach to automated assessment of fractionation of endocardial electrograms during atrial fibrillation,” Physiol. Meas., vol. 29, pp. 1371–1381, Dec. 2008. [63] F. Ravelli and M. Mase, “Computational mapping in atrial fibrillation: How the integration of signal-derived maps may guide the localization of critical sources,” Europace, vol. 16, pp. 714–723, May 2014. [64] F. Ravelli, M. Mase, M. Del Greco, L. Faes, and M. Disertori, “Deterioration of organization in the first minutes of atrial fibrillation: A beat-to-beat analysis of cycle length and wave similarity,” J. Cardiovasc. Electrophysiol., vol. 18, pp. 60–65, Jan. 2007. [65] F. Ravelli et al., “Wave similarity mapping shows the spatiotemporal distribution of fibrillatory wave complexity in the human right atrium during paroxysmal and chronic atrial fibrillation,” J. Cardiovasc. Electrophysiol., vol. 16, pp. 1071–1076, Oct. 2005. [66] F. Ravelli, M. Mase, A. Cristoforetti, M. Marini, and M. Disertori, “The logical operator map identifies novel candidate markers for critical sites in patients with atrial fibrillation,” Progr. Biophys. Mol. Biol., vol. 115, pp. 186–197, Aug. 2014. [67] J. Ng et al., “Electrogram morphology recurrence patterns during atrial fibrillation,” Heart Rhythm, Aug. 5, 2014. [68] M. Mase, L. Faes, R. Antolini, M. Scaglione, and F. Ravelli, “Quantification of synchronization during atrial fibrillation by Shannon entropy: Validation in patients and computer model of atrial arrhythmias,” Physiol. Meas., vol. 26, pp. 911–923, Dec. 2005. [69] A. N. Ganesan et al., “Bipolar electrogram shannon entropy at sites of rotational activation: Implications for ablation of

[70]

[71]

[72]

[73]

[74]

[75]

[76]

[77]

[78]

[79]

[80]

[81]

[82]

[83]

[84]

atrial fibrillation,” Circulation: Arrhythmia Electrophysiol., vol. 6, pp. 48–57, Feb. 2013. A. N. Ganesan et al., “Origin and characteristics of high Shannon entropy at the pivot of locally stable rotors: Insights from computational simulation,” PLoS One, vol. 9, 2014, Art. ID e110662. K. Balasundaram et al., “Tracking rotors with minimal electrodes: Modulation index-based strategy,” Circulation: Arrhythmia Electrophysiol., vol. 8, pp. 447–455, Apr. 2015. A. Orozco-Duque, J. P. Ugarte, C. Tobon, J. Saiz, and J. Bustamante, “Approximate entropy can localize rotors, but not ectopic foci during chronic atrial fibrillation: A simulation study,” in Proc. Comput. Cardiol. Conf., 2013, pp. 903–906. J. P. Ugarte et al., “Dynamic approximate entropy electroanatomic maps detect rotors in a simulated atrial fibrillation model,” PLoS One, vol. 9, 2014, Art. ID e114577. R. Alcaraz, F. Hornero, and J. J. Rieta, “Assessment of non-invasive time and frequency atrial fibrillation organization markers with unipolar atrial electrograms,” Physiol. Meas., vol. 32, pp. 99–114, Jan. 2011. A. Porta et al., “Measuring regularity by means of a corrected conditional entropy in sympathetic outflow,” Biol. Cybern., vol. 78, pp. 71–78, Jan. 1998. L. T. Mainardi et al., “Linear and non-linear analysis of atrial signals and local activation period series during atrial-fibrillation episodes,” Med. Biol. Eng. Comput., vol. 39, pp. 249–254, Mar. 2001. P. Bonizzi et al., “Systematic comparison of non-invasive measures for the assessment of atrial fibrillation complexity: A step forward towards standardization of atrial fibrillation electrogram analysis,” Europace, vol. 17, pp. 318–325, Feb. 2015. L. Faes et al., “Principal component analysis and cluster analysis for measuring the local organisation of human atrial fibrillation,” Med. Biol. Eng. Comput., vol. 39, pp. 656–663, Nov. 2001. B. P. Hoekstra, C. G. Diks, M. A. Allessie, and J. DeGoede, “Nonlinear analysis of epicardial atrial electrograms of electrically induced atrial fibrillation in man,” J. Cardiovasc. Electrophysiol., vol. 6, pp. 419–440, Jun. 1995. V. D. A. Corino, R. Sassi, L. T. Mainardi, and S. Cerutti, “Signal processing methods for information enhancement in atrial fibrillation: Spectral analysis and non-linear parameters,” Biomed. Signal Process. Control, vol. 1, pp. 271–281, Oct. 2006. F. Takens, Detecting Strange Attractors in Turbulence. New York, NY, USA: Springer-Verlag, 1981. F. Censi et al., “Recurrent patterns of atrial depolarization during atrial fibrillation assessed by recurrence plot quantification,” Ann. Biomed. Eng., vol. 28, pp. 61–70, Jan. 2000. N. Navoret, S. Jacquir, G. Laurent, and S. Binczak, “Detection of complex fractionated atrial electrograms using recurrence quantification analysis,” IEEE Trans. Biomed. Eng., vol. 60, pp. 1975–1982, Jul. 2013. K. Nademanee et al., “A new approach for catheter ablation of atrial fibrillation: Mapping of the electrophysiologic substrate,” J. Amer. Coll. Cardiol., vol. 43, pp. 2044–2053, Jun. 2, 2004.


429


[85] A. Verma et al., “A prospective, multicenter evaluation of ablating complex fractionated electrograms (CFEs) during atrial fibrillation (AF) identified by an automated mapping algorithm: Acute effects on AF and efficacy as an adjuvant strategy,” Heart Rhythm, vol. 5, pp. 198–205, Feb. 2008. [86] D. Scherr et al., “Automated detection and characterization of complex fractionated atrial electrograms in human left atrium during atrial fibrillation,” Heart Rhythm, vol. 4, pp. 1013–1020, Aug. 2007. [87] M. Porter et al., “Prospective study of atrial fibrillation termination during ablation guided by automated detection of fractionated electrograms,” J. Cardiovasc. Electrophysiol., vol. 19, pp. 613–620, Jun. 2008. [88] J. F. Roux et al., “Complex fractionated electrogram distribution and temporal stability in patients undergoing atrial fibrillation ablation,” J. Cardiovasc. Electrophysiol., vol. 19, pp. 815–820, Aug. 2008. [89] A. Verma et al., “Spatial and temporal stability of complex fractionated electrograms in patients with persistent atrial fibrillation over longer time periods: Relationship to local electrogram cycle length,” Heart Rhythm, vol. 5, pp. 1127–1133, Aug. 2008. [90] D. Scherr et al., “Long- and short-term temporal stability of complex fractionated atrial electrograms in human left atrium during atrial fibrillation,” J. Cardiovasc. Electrophysiol., vol. 20, pp. 13–21, Jan. 2009. [91] D. H. Lau et al., “Stability of complex fractionated atrial electrograms: A systematic review,” J. Cardiovasc. Electrophysiol., vol. 23, pp. 980–987, Sep. 2012. [92] M. K. Stiles et al., “The effect of electrogram duration on quantification of complex fractionated atrial electrograms and dominant frequency,” J. Cardiovasc. Electrophysiol., vol. 19, pp. 252–258, Mar. 2008. [93] F. Atienza et al., “Mechanisms of fractionated electrograms formation in the posterior left atrium during paroxysmal atrial fibrillation in humans,” J. Amer. Coll. Cardiol., vol. 57, pp. 1081–1092, Mar. 1, 2011. [94] E. J. Ciaccio, A. B. Biviano, and H. Garan, “Computational method for high resolution spectral analysis of fractionated atrial electrograms,” Comput. Biol. Med., vol. 43, pp. 1573–1582, Oct. 2013. [95] E. J. Ciaccio, A. B. Biviano, A. Gambhir, J. T. Jacobson, and H. Garan, “Temporal stability in the spectral representation of complex fractionated atrial electrograms,” Pacing Clin. Electrophysiol., vol. 37, pp. 79–89, Jan. 2014. [96] E. J. Ciaccio et al., “Differences in repeating patterns of complex fractionated left atrial electrograms in longstanding persistent atrial fibrillation as compared with paroxysmal atrial fibrillation,” Circulation: Arrhythmia Electrophysiol., vol. 4, pp. 470–477, Aug. 2011. [97] E. J. Ciaccio, A. B. Biviano, W. Whang, A. Gambhir, and H. Garan, “Different characteristics of complex fractionated atrial electrograms in acute paroxysmal versus long-standing persistent atrial fibrillation,” Heart Rhythm, vol. 7, pp. 1207–1215, Sep. 2010.

430

[98] Y. J. Lin et al., “Spatiotemporal organization of the left atrial substrate after circumferential pulmonary vein isolation of atrial fibrillation,” Circulation: Arrhythmia Electrophysiol., vol. 2, pp. 233–241, Jun. 2009. [99] Y. J. Lin et al., “Novel assessment of temporal variation in fractionated electrograms using histogram analysis of local fractionation interval in patients with persistent atrial fibrillation,” Circulation: Arrhythmia Electrophysiol., vol. 5, pp. 949–956, Oct. 2012. [100] Y. J. Lin et al., “Consistency of complex fractionated atrial electrograms during atrial fibrillation,” Heart Rhythm, vol. 5, pp. 406–412, Mar. 2008. [101] S. L. Chang et al., “Electrophysiological characteristics of complex fractionated electrograms and high frequency activity in atrial fibrillation,” Int. J. Cardiol., Feb. 25, 2013. [102] J. Ng et al., “Measuring the complexity of atrial fibrillation electrograms,” J. Cardiovasc. Electrophysiol., vol. 21, pp. 649–655, Jun. 1, 2010. [103] N. Navoret, B. Xu, S. Jacquir, and S. Binczak, “Comparison of complex fractionated atrial electrograms at cellular scale using numerical and experimental models,” in Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2010, pp. 3249–3252. [104] K. Umapathy et al., “Electrogram fractionation in murine HL-1 atrial monolayer model,” Heart Rhythm, vol. 5, pp. 1029–1035, Jul. 2008. [105] R. J. Hunter et al., “Characterization of fractionated atrial electrograms critical for maintenance of atrial fibrillation: A randomized, controlled trial of ablation strategies (the CFAE AF trial),” Circulation: Arrhythmia Electrophysiol., vol. 4, pp. 622–629, Oct. 2011. [106] K. R. Grzeda, S. F. Noujaim, O. Berenfeld, and J. Jalife, “Complex fractionated atrial electrograms: Properties of time-domain versus frequency-domain methods,” Heart Rhythm, vol. 6, pp. 1475–1482, Oct. 2009. [107] D. Katritsis et al., “Autonomic modulation of complex fractionated atrial electrograms in patients with paroxysmal atrial fibrillation,” J. Interventional Cardiac Electrophysiol., vol. 31, pp. 217–223, Sep. 2011. [108] D. Katritsis, E. Giazitzoglou, D. Sougiannis, E. Voridis, and S. S. Po, “Complex fractionated atrial electrograms at anatomic sites of ganglionated plexi in atrial fibrillation,” Europace, vol. 11, pp. 308–315, Mar. 2009. [109] A. S. Jadidi et al., “Functional nature of electrogram fractionation demonstrated by left atrial high-density mapping,” Circulation: Arrhythmia Electrophysiol., vol. 5, pp. 32–42, Feb. 2012. [110] K. Yoshida et al., “Complex electrograms within the coronary sinus: Time- and frequency-domain characteristics, effects of antral pulmonary vein isolation, relationship to clinical outcome in patients with paroxysmal and persistent atrial fibrillation,” J. Cardiovasc. Electrophysiol., vol. 19, pp. 1017–1023, Oct. 2008. [111] G. Lee et al., “Relationship among complex signals, short cycle length activity, dominant frequency in patients with long-lasting persistent AF: A high-density epicardial mapping study in humans,” Heart Rhythm, vol. 8, pp. 1714–1719, Nov. 2011.


[112] D. H. Lau et al., “Indices of bipolar complex fractionated atrial electrograms correlate poorly with each other and atrial fibrillation substrate complexity,” Heart Rhythm, vol. 12, pp. 1415–1423, Jul. 2015. [113] E. G. Lovett and K. M. Ropella, “Time-frequency coherence analysis of atrial fibrillation termination during procainamide administration,” Ann. Biomed. Eng., vol. 25, pp. 975–984, Nov./Dec. 1997. [114] U. Richter et al., “A novel approach to propagation pattern analysis in intracardiac atrial fibrillation signals,” Ann. Biomed. Eng., vol. 39, pp. 310–323, Jan. 2011. [115] U. Richter, L. Faes, F. Ravelli, and L. Sornmo, “Propagation pattern analysis during atrial fibrillation based on sparse modeling,” IEEE Trans. Biomed. Eng., vol. 59, pp. 1319–1328, May 2012. [116] M. A. Bray and J. P. Wikswo, “Considerations in phase plane analysis for nonstationary reentrant cardiac behavior,” Phys. Rev. E, Stat. Nonlinear Soft Matter Phys., vol. 65, May 2002, Art. ID 051902. [117] R. A. Gray, A. M. Pertsov, and J. Jalife, “Spatial and temporal organization during cardiac fibrillation,” Nature, vol. 392, pp. 75–78, Mar. 5, 1998. [118] A. N. Iyer and R. A. Gray, “An experimentalist’s approach to accurate localization of phase singularities during reentry,” Ann. Biomed. Eng., vol. 29, pp. 47–59, Jan. 2001. [119] P. Kuklik et al., “Reconstruction of instantaneous phase of unipolar atrial contact electrogram using a concept of sinusoidal recomposition and Hilbert transform,” IEEE Trans. Biomed. Eng., vol. 62, pp. 296–302, Jan. 2015. [120] P. Kuklik, E. Bidar, A. Gharaviri, J. Maessen, and U. Schotten, “Application of phase coherence in assessment of spatial alignment of electrodes during simultaneous endocardial-epicardial direct contact mapping of atrial fibrillation,” Europace, vol. 16, pp. iv135–iv140, Nov. 2014. [121] M. P. Nash et al., “Evidence for multiple mechanisms in human ventricular fibrillation,” Circulation, vol. 114, pp. 536–542, Aug. 8, 2006. [122] S. Masse, E. Downar, V. Chauhan, E. Sevaptsidis, and K. Nanthakumar, “Ventricular fibrillation in myopathic human hearts: Mechanistic insights from in vivo global endocardial and epicardial mapping,” Amer. J. Physiol. Heart Circ. Physiol., vol. 292, pp. H2589–H2597, Jun. 2007. [123] H. J. Sih, D. P. Zipes, E. J. Berbari, and J. E. Olgin, “A high-temporal resolution algorithm for quantifying organization during atrial fibrillation,” IEEE Trans. Biomed. Eng., vol. 46, pp. 440–450, Apr. 1999. [124] L. Faes and F. Ravelli, “A morphology-based approach to the evaluation of atrial fibrillation organization,” IEEE Eng. Med. Biol. Mag., vol. 26, pp. 59–67, Jul./Aug. 2007. [125] U. Richter, A. Bollmann, D. Husser, and M. Stridh, “Right atrial organization and wavefront analysis in atrial fibrillation,” Med. Biol. Eng. Comput., vol. 47, pp. 1237–1246, Dec. 2009. [126] K. Lemola et al., “Effects of two different catheter ablation techniques on spectral characteristics of atrial fibrillation,” J. Amer. Coll. Cardiol., vol. 48, pp. 340–348, Jul. 18, 2006.


[127] Y. Takahashi et al., “Characterization of electrograms associated with termination of chronic atrial fibrillation by catheter ablation,” J. Amer. Coll. Cardiol., vol. 51, pp. 1003–1010, Mar. 11, 2008. [128] F. Atienza et al., “Comparison of radiofrequency catheter ablation of drivers and circumferential pulmonary vein isolation in atrial fibrillation: A noninferiority randomized multicenter RADAR-AF trial,” J. Amer. Coll. Cardiol., vol. 64, pp. 2455–2467, Dec. 16, 2014. [129] H. Oral et al., “Radiofrequency catheter ablation of chronic atrial fibrillation guided by complex electrograms,” Circulation, vol. 115, pp. 2606–2612, May 22, 2007. [130] H. Oral et al., “Randomized evaluation of right atrial ablation after left atrial ablation of complex fractionated atrial electrograms for long-lasting persistent atrial fibrillation,” Circulation: Arrhythmia Electrophysiol., vol. 1, pp. 6–13, Apr. 2008. [131] H. Oral et al., “A randomized assessment of the incremental role of ablation of complex fractionated atrial electrograms after antral pulmonary vein isolation for long-lasting persistent atrial fibrillation,” J. Amer. Coll. Cardiol., vol. 53, pp. 782–789, Mar. 3, 2009. [132] A. Verma et al., “Selective complex fractionated atrial electrograms targeting for atrial fibrillation study (SELECT AF): A multicenter, randomized trial,” Circulation: Arrhythmia Electrophysiol., vol. 7, pp. 55–62, Feb. 1, 2014. [133] W. J. Li et al., “Additional ablation of complex fractionated atrial electrograms after pulmonary vein isolation in patients with atrial fibrillation: A meta-analysis,” Circulation: Arrhythmia Electrophysiol., vol. 4, pp. 143–148, Apr. 1, 2011.

[134] R. M. Hayward et al., “Pulmonary vein isolation with complex fractionated atrial electrogram ablation for paroxysmal and nonparoxysmal atrial fibrillation: A meta-analysis,” Heart Rhythm, vol. 8, pp. 994–1000, Jul. 2011. [135] A. Verma et al., “Efficacy of adjuvant anterior left atrial ablation during intracardiac echocardiography-guided pulmonary vein antrum isolation for atrial fibrillation,” J. Cardiovasc. Electrophysiol., vol. 18, pp. 151–156, Feb. 2007. [136] A. Verma et al., “Approaches to catheter ablation for persistent atrial fibrillation,” New England J. Med., vol. 372, pp. 1812–1822, May 7, 2015. [137] S. M. Narayan et al., “Treatment of atrial fibrillation by the ablation of localized sources: Confirm (conventional ablation for atrial fibrillation with or without focal impulse and rotor modulation) trial,” J. Amer. Coll. Cardiol., vol. 60, pp. 628– 636, Aug. 14, 2012. [138] S. M. Narayan, D. E. Krummen, M. W. Enyeart, and W. J. Rappel, “Computational mapping identifies localized mechanisms for ablation of atrial fibrillation,” PloS One, vol. 7, 2012, Art. ID e46034. [139] S. M. Narayan, D. E. Krummen, P. Clopton, K. Shivkumar, and J. M. Miller, “Direct or coincidental elimination of stable rotors or focal sources may explain successful atrial fibrillation ablation: On-treatment analysis of the confirm (conventional ablation for AF with or without focal impulse and rotor modulation) trial,” J. Amer. Coll. Cardiol., vol. 62, pp. 138–147, Apr. 3, 2013. [140] J. M. Miller et al., “Initial independent outcomes from focal impulse and rotor

[141]

[142]

[143]

[144]

[145]

[146]

modulation ablation for atrial fibrillation: Multicenter FIRM registry,” J. Cardiovasc. Electrophysiol., vol. 25, pp. 921–929, Sep. 2014. S. M. Narayan et al., “Ablation of rotor and focal sources reduces late recurrence of atrial fibrillation compared with trigger ablation alone: Extended follow-up of the CONFIRM trial (conventional ablation for atrial fibrillation with or without focal impulse and rotor modulation),” J. Amer. Coll. Cardiol., vol. 63, pp. 1761–1768, May 6, 2014. P. Benharash et al., “Quantitative analysis of localized sources identified by focal impulse and roter modulation mapping in atrial fibrillation,” Circulation: Arrhythmia Electrophysiol., Apr. 14, 2015. L. Xu et al., “3D multifunctional integumentary membranes for spatiotemporal cardiac measurements and stimulation across the entire epicardium,” Nature Commun., vol. 5, p. 3329, 2014. S. V. Pandit et al., “Ionic determinants of functional reentry in a 2-D model of human atrial cells during simulated chronic atrial fibrillation,” Biophys. J., vol. 88, pp. 3806–3821, Jun. 2005. A. G. Brooks et al., “Image integration using NavX Fusion: Initial experience and validation,” Heart Rhythm, vol. 5, pp. 526–535, Apr. 2008. W. G. Stevenson and K. Soejima, “Recording techniques for clinical electrophysiology,” J. Cardiovasc. Electrophysiol., vol. 16, pp. 1017–1022, Sep. 2005.

ABOUT THE AUTHORS Mathias Baumert (Senior Member, IEEE) received the Ph.D. degree in biomedical engineering from the Ilmenau University of Technology, Germany, in 2005. Subsequently, he was awarded the Australian Postdoctoral Fellowship and the Australian Research 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 signals, computerized electrocardiography and electroencephalography, cardiac autonomic modulation, and sleep. Prashanthan Sanders graduated from the University 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 Director 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 electrophysiology at the Royal Melbourne Hospital, Parkville, Vic., Australia. He has established 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, clinical mechanistic, outcome and more recently population-based studies. The group has an established clinical trials program and is involved in development 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 electrophysiology 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.


431

Quantitative-Electrogram-Based Methods for Guiding Catheter ...

Quantitative-Electrogram-Based Methods for Guiding Catheter ...

Suggest Documents

Feasibility of Using a Sheathless Guiding Catheter for ...

An Extremely Effective and Safe Approach of Guiding Catheter ...

A guiding catheter to facilitate accurate stent length determination

Guiding the Adoption of Software Development Methods

a guiding framework for developing design methods

Guiding carbon farming using interdisciplinary mixed methods ...www.researchgate.net › publication › fulltext › Guiding-c

NCAL's Guiding Principles for Leadership

Catheter Ablation for Atrial Fibrillation

Catheter Ablation for Atrial Fibrillation

tourism guiding tourism guiding qualification - Wessa

Impact of a single universal guiding catheter on door-to-balloon time in ...

Guiding Principles

Catheter ablation

Guiding principles

GUIDING STUDENTS

Guiding Principles of Specimen Preservation for Confocal ...

Developing population models for guiding reintroductions ... - CiteSeerX

Fit-For-Purpose Land Administration Guiding Principles

Guiding phosphorus stewardship for multiple ecosystem services

Evaluating biomarkers for guiding treatment decisions - IFCC

Guiding Principles for Congregational Mission Planning - ELCA

Double Guiding Catheters for Complex Percutaneous ...

NEW GUIDING PRINCIPLES FOR LIBRARIANS AND LIBRARIES

Guiding Principles for International Youth Development - InterAction