Structure and function of the ventricular tachycardia isthmus.

Catheter ablation of postinfarction reentrant ventricular tachycardia (VT) has received renewed interest owing to the increased availability of high-resolution electroanatomic mapping systems that can describe the VT circuits in greater detail, and the emergence and need to target noninvasive external beam radioablation. These recent advancements provide optimism for improving the clinical outcome of VT ablation in patients with postinfarction and potentially other scar-related VTs. The combination of analyses gleaned from studies in swine and canine models of postinfarction reentrant VT, and in human studies, suggests the existence of common electroanatomic properties for reentrant VT circuits. Characterizing these properties may be useful for increasing the specificity of substrate mapping techniques and for noninvasive identification to guide ablation. Herein, we describe properties of reentrant VT circuits that may assist in elucidating the mechanisms of onset and maintenance, as well as a means to localize and delineate optimal catheter ablation targets.

Worldwide Survey of COVID-19-Associated Arrhythmias.

[Figure: see text].

Increased body mass index, age, and left atrial size are associated with altered intracardiac atrial electrograms in persistent atrial fibrillation patients.

BACKGROUND: There are limited studies evaluating whether atrial fibrillation (AF) patients with increased BMI, age, and left atrial (LA) size have altered intracardiac electrogram (EGM) morphology. METHODS: We analyzed left atrial intracardiac EGMs acquired during invasive electrophysiology study in 54 patients with AF. EGM correlations were assessed among AF risk factors including age, left atrial size, and BMI. RESULTS: BMI correlated positively with DF (r2 = 0.17, p = 0.009) and MP (r2 = 0.16, p = 0.01) with dominant frequency (DF) and mean spectral profile (MP) greater among obese individuals. Age was negatively associated with mean amplitude (r2 = 0.42, p < 0.001) and width (r2 = 0.32, p < 0.001); age was positively correlated with MP (r2 = 0.24, p < 0.001). LA size was negatively correlated with mean amplitude (r2 = 0.18, p = 0.03) and width (r2 = 0.23, p = 0.01); LA size was positively correlated with DF (r2 = 0.22, p = 0.01) and MP (r2 = 0.23, p = 0.01). Mean amplitude and width were decreased among subjects with a severely enlarged LA; DF and MP were increased in those with severely enlarged LA. The associations with BMI and LA size remained significant in multiple regression models that included age, male gender, time since AF diagnosis, and LVEF. CONCLUSIONS: EGM morphology of AF patients with increased BMI, older age, and an enlarged LA possessed decreased amplitude and decreased width and increased DF and MP. These findings suggest that atrial remodeling due to increased age, LA size, and BMI is associated with differences in local atrial activation, decreased refractoriness, and more heterogeneous activation. These novel findings point out clinical risk factors for atrial fibrillation that may affect electrogram characteristics.

Application of nonlinear methods to discriminate fractionated electrograms in paroxysmal versus persistent atrial fibrillation.

BACKGROUND AND OBJECTIVE: Complex fractionated atrial electrograms (CFAE) may contain information concerning the electrophysiological substrate of atrial fibrillation (AF); therefore they are of interest to guide catheter ablation treatment of AF. Electrogram signals are shaped by activation events, which are dynamical in nature. This makes it difficult to establish those signal properties that can provide insight into the ablation site location. Nonlinear measures may improve information. To test this hypothesis, we used nonlinear measures to analyze CFAE. METHODS: CFAE from several atrial sites, recorded for a duration of 16 s, were acquired from 10 patients with persistent and 9 patients with paroxysmal AF. These signals were appraised using non-overlapping windows of 1-, 2- and 4-s durations. The resulting data sets were analyzed with Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA). The data was also quantified via entropy measures. RESULTS: RQA exhibited unique plots for persistent versus paroxysmal AF. Similar patterns were observed to be repeated throughout the RPs. Trends were consistent for signal segments of 1 and 2 s as well as 4 s in duration. This was suggestive that the underlying signal generation process is also repetitive, and that repetitiveness can be detected even in 1-s sequences. The results also showed that most entropy metrics exhibited higher measurement values (closer to equilibrium) for persistent AF data. It was also found that Determinism (DET), Trapping Time (TT), and Modified Multiscale Entropy (MMSE), extracted from signals that were acquired from locations at the posterior atrial free wall, are highly discriminative of persistent versus paroxysmal AF data. CONCLUSIONS: Short data sequences are sufficient to provide information to discern persistent versus paroxysmal AF data with a significant difference, and can be useful to detect repeating patterns of atrial activation.

Electromechanical wave imaging (EWI) validation in all four cardiac chambers with 3D electroanatomic mapping in canines in vivo.

Characterization and mapping of arrhythmias is currently performed through invasive insertion and manipulation of cardiac catheters. Electromechanical wave imaging (EWI) is a non-invasive ultrasound-based imaging technique, which tracks the electromechanical activation that immediately follows electrical activation. Electrical and electromechanical activations were previously found to be linearly correlated in the left ventricle, but the relationship has not yet been investigated in the three other chambers of the heart. The objective of this study was to investigate the relationship between electrical and electromechanical activations and validate EWI in all four chambers of the heart with conventional 3D electroanatomical mapping. Six (n = 6) normal adult canines were used in this study. The electrical activation sequence was mapped in all four chambers of the heart, both endocardially and epicardially using the St Jude's EnSite 3D mapping system (St. Jude Medical, Secaucus, NJ). EWI acquisitions were performed in all four chambers during normal sinus rhythm, and during pacing in the left ventricle. Isochrones of the electromechanical activation were generated from standard echocardiographic imaging views. Electrical and electromechanical activation maps were co-registered and compared, and electrical and electromechanical activation times were plotted against each other and linear regression was performed for each pair of activation maps. Electromechanical and electrical activations were found to be directly correlated with slopes of the correlation ranging from 0.77 to 1.83, electromechanical delays between 9 and 58 ms and R 2 values from 0.71 to 0.92. The linear correlation between electrical and electromechanical activations and the agreement between the activation maps indicate that the electromechanical activation follows the pattern of propagation of the electrical activation. This suggests that EWI may be used as a novel non-invasive method to accurately characterize and localize sources of arrhythmias.

Validation of electromechanical wave imaging in a canine model during pacing and sinus rhythm.

BACKGROUND: Accurate determination of regional areas of arrhythmic triggers is of key interest to diagnose arrhythmias and optimize their treatment. Electromechanical wave imaging (EWI) is an ultrasound technique that can image the transient deformation in the myocardium after electrical activation and therefore has the potential to detect and characterize location of triggers of arrhythmias. OBJECTIVES: The objectives of this study were to investigate the relationship between the electromechanical and the electrical activation of the left ventricular (LV) endocardial surface during epicardial and endocardial pacing and during sinus rhythm as well as to map the distribution of electromechanical delays. METHODS: In this study, 6 canines were investigated. Two external electrodes were sutured onto the epicardial surface of the LV. A 64-electrode basket catheter was inserted through the apex of the LV. Ultrasound channel data were acquired at 2000 frames/s during epicardial and endocardial pacing and during sinus rhythm. Electromechanical and electrical activation maps were synchronously obtained from the ultrasound data and the basket catheter, respectively. RESULTS: The mean correlation coefficient between electromechanical and electrical activation was 0.81 for epicardial anterior pacing, 0.79 for epicardial lateral pacing, 0.69 for endocardial pacing, and 0.56 for sinus rhythm. CONCLUSION: The electromechanical activation sequence determined by EWI follows the electrical activation sequence and more specifically in the case of pacing. This finding is of key interest in the role that EWI can play in the detection of the anatomical source of arrhythmias and the planning of pacing therapies such as cardiovascular resynchronization therapy.

Differences in continuous spectra of fractionated electrograms in paroxysmal versus persistent atrial fibrillation.

INTRODUCTION: Quantitative measurements are helpful to discern fractionated electrograms in paroxysmal and persistent atrial fibrillation (AF), and may be useful to detect optimal ablation sites. However, electrical activation events can be transient, leading to erroneous estimates of electrogram properties. Measurement of continuous changes in electrogram frequency content may improve analysis. METHOD: Fractionated local electrograms from 10 paroxysmal and 10 persistent AF patients were acquired from outside the pulmonary vein ostia and left atrial free wall using the distal bipolar ablation catheter electrode, and analyzed over continuous 16 second intervals. A New Spectral Estimator (NSE) updated the frequency spectrum and spectral parameters once per millisecond. The tallest spectral peak (dominant frequency or DF) was determined. Statistical tests of variability were used to determine significant differences between paroxysmal and persistent AF. RESULTS: Changes in the value of the DF over 16 seconds were caused by transient drifts in the frequency of the dominant peak, or by changes in which peak had the highest amplitude. The continuous DF and the spectral profile parameters were more highly variable in paroxysmal as compared with persistent AF patients (p<0.001). There was found to be a gradient from high to low variability of DF in paroxysmal AF, from the left superior pulmonary vein antrum to the left atrial free wall. CONCLUSIONS: The results suggest that atrial electrical activation becomes more stable and focused at a narrow frequency range in persistent as compared to paroxysmal AF. The NSE implemented for continuous update of spectral parameters, enables a rapid characterization of fractionated electrograms with high time-frequency resolution and low computational cost.

Assessing the atrial electromechanical coupling during atrial focal tachycardia, flutter, and fibrillation using electromechanical wave imaging in humans.

Minimally-invasive treatments of cardiac arrhythmias such as radio-frequency ablation are gradually gaining importance in clinical practice but still lack a noninvasive imaging modality which provides insight into the source or focus of an arrhythmia. Cardiac deformations imaged at high temporal and spatial resolution can be used to elucidate the electrical activation sequence in normal and paced human subjects non-invasively and could potentially aid to better plan and monitor ablation-based arrhythmia treatments. In this study, a novel ultrasound-based method is presented that can be used to quantitatively characterize focal and reentrant arrhythmias. Spatio-temporal maps of the full-view of the atrial and ventricular mechanics were obtained in a single heartbeat, revealing with otherwise unobtainable detail the electromechanical patterns of atrial flutter, fibrillation, and tachycardia in humans. During focal arrhythmias such as premature ventricular complex and focal atrial tachycardia, the previously developed electromechanical wave imaging methodology is hereby shown capable of identifying the location of the focal zone and the subsequent propagation of cardiac activation. During reentrant arrhythmias such as atrial flutter and fibrillation, Fourier analysis of the strains revealed highly correlated mechanical and electrical cycle lengths and propagation patterns. High frame rate ultrasound imaging of the heart can be used non-invasively and in real time, to characterize the lesser-known mechanical aspects of atrial and ventricular arrhythmias, also potentially assisting treatment planning for intraoperative and longitudinal monitoring of arrhythmias.

Atrial electrogram discordance during baseline vs reinduced atrial fibrillation: Potential ramifications for ablation procedures.

BACKGROUND: There are scant data comparing the electrogram (EGM) signal characteristics of atrial fibrillation (AF) at baseline vs electrically induced states during ablation procedures. OBJECTIVE: The purpose of this study was to use novel intracardiac signal analysis techniques to gain insights into the effects of catheter ablation and AF reinduction on AF EGMs in patients with persistent AF. METHODS: We collected left atrial EGMs in patients undergoing first ablation for persistent AF at 3 time intervals: (1) AF at baseline; (2) AF after pulmonary vein isolation (PVI); and (3) AF after post-PVI cardioversion and subsequent reinduction. We analyzed 2 EGM spectral characteristics: (1) dominant frequency and (2) spectral complexity; and 2 EGM morphologic characteristics: (1) morphology variation and (2) pattern repetitiveness. RESULTS: There were no differences in AF dominant frequency, dominant amplitude, spectral complexity, or metrics of EGM morphology or repetitiveness at baseline vs after PVI. However, dominant frequency, dominant amplitude, and spectral complexity differed significantly after direct current cardioversion and reinduction of AF. CONCLUSION: The frequency, spectral complexity, and local EGM morphologies of AF do not significantly change over the course of a PVI procedure in patients with persistent AF. However, reinduction of AF after direct current cardioversion results in different dominant frequency and spectral complexity, consistent with a change in the characteristics of the perpetuating source(s) of the newly induced AF. These data suggest that AF properties can vary significantly between baseline and reinduced AF, with potential clinical ramifications for outcomes of catheter ablation procedures.

Software algorithm and hardware design for real-time implementation of new spectral estimator.

BACKGROUND: Real-time spectral analyzers can be difficult to implement for PC computer-based systems because of the potential for high computational cost, and algorithm complexity. In this work a new spectral estimator (NSE) is developed for real-time analysis, and compared with the discrete Fourier transform (DFT). METHOD: Clinical data in the form of 216 fractionated atrial electrogram sequences were used as inputs. The sample rate for acquisition was 977 Hz, or approximately 1 millisecond between digital samples. Real-time NSE power spectra were generated for 16,384 consecutive data points. The same data sequences were used for spectral calculation using a radix-2 implementation of the DFT. The NSE algorithm was also developed for implementation as a real-time spectral analyzer electronic circuit board. RESULTS: The average interval for a single real-time spectral calculation in software was 3.29 µs for NSE versus 504.5 µs for DFT. Thus for real-time spectral analysis, the NSE algorithm is approximately 150× faster than the DFT. Over a 1 millisecond sampling period, the NSE algorithm had the capability to spectrally analyze a maximum of 303 data channels, while the DFT algorithm could only analyze a single channel. Moreover, for the 8 second sequences, the NSE spectral resolution in the 3-12 Hz range was 0.037 Hz while the DFT spectral resolution was only 0.122 Hz. The NSE was also found to be implementable as a standalone spectral analyzer board using approximately 26 integrated circuits at a cost of approximately $500. The software files used for analysis are included as a supplement, please see the Additional files 1 and 2. CONCLUSIONS: The NSE real-time algorithm has low computational cost and complexity, and is implementable in both software and hardware for 1 millisecond updates of multichannel spectra. The algorithm may be helpful to guide radiofrequency catheter ablation in real time.

Model of bipolar electrogram fractionation and conduction block associated with activation wavefront direction at infarct border zone lateral isthmus boundaries.

BACKGROUND: Improved understanding of the mechanisms underlying infarct border zone electrogram fractionation may be helpful to identify arrhythmogenic regions in the postinfarction heart. We describe the generation of electrogram fractionation from changes in activation wavefront curvature in experimental canine infarction. METHODS AND RESULTS: A model was developed to estimate the extracellular signal shape that would be generated by wavefront propagation parallel to versus perpendicular to the lateral boundary (LB) of the reentrant ventricular tachycardia (VT) isthmus or diastolic pathway. LBs are defined as locations where functional block forms during VT, and elsewhere they have been shown to coincide with sharp thin-to-thick transitions in infarct border zone thickness. To test the model, bipolar electrograms were acquired from infarct border zone sites in 10 canine heart experiments 3 to 5 days after experimental infarction. Activation maps were constructed during sinus rhythm and during VT. The characteristics of model-generated versus actual electrograms were compared. Quantitatively expressed VT fractionation (7.6±1.2 deflections; 16.3±8.9-ms intervals) was similar to model-generated values with wavefront propagation perpendicular to the LB (9.4±2.4 deflections; 14.4±5.2-ms intervals). Fractionation during sinus rhythm (5.9±1.8 deflections; 9.2±4.4-ms intervals) was similar to model-generated fractionation with wavefront propagation parallel to the LB (6.7±3.1 deflections; 7.1±3.8-ms intervals). VT and sinus rhythm fractionation sites were adjacent to LBs ≈80% of the time. CONCLUSIONS: The results suggest that in a subacute canine infarct model, the LBs are a source of activation wavefront discontinuity and electrogram fractionation, with the degree of fractionation being dependent on activation rate and wavefront orientation with respect to the LB.

Temporal stability in the spectral representation of complex fractionated atrial electrograms.

BACKGROUND: Although local electrograms during atrial fibrillation (AF) are often spectrally analyzed over 8-second (8s) intervals, changes may be common over intervals as short as 2s. We sought to determine whether averaged 2s measurements of electrogram spectral parameters were similar to 8s measurements, and whether the 2s intervals could provide an estimate of the temporal stability of the signal frequency content in paroxysmal versus persistent AF. METHODS: Complex fractionated atrial electrograms (CFAEs) were acquired outside the pulmonary vein ostia and from free wall sites in nine paroxysmal and 10 longstanding persistent AF patients. Using a 2s sliding calculation window, a frequency spectrum was computed every 100 ms over an interval of 8.4 seconds (82 spectra in total). The dominant frequency (DF), the dominant amplitude (DA), and the mean spectral profile (MP) were measured. The 2s measurements were compared to single 8.4-second interval measurements. Coefficients of variation (COV) were computed from the 82 spectra for each CFAE recording to determine temporal variability of parameters. RESULTS: Over the sliding 2s computation intervals, as for fixed 8.4-second computation intervals, mean DA and DF were significantly higher in longstanding persistent AF while MP was significantly higher in paroxysmal AF (P ≤ 0.001). The COV was significantly higher for the DF parameter in paroxysmal AF (P < 0.001) and significantly higher for the MP parameter in persistent AF (P < 0.02). CONCLUSIONS: For both paroxysmal and persistent AF data, the 2s sliding window averages provide similar results to single 8.4-second intervals, and information regarding temporal stability was additionally obtained in the process.

The dominant morphology of fractionated atrial electrograms has greater temporal stability in persistent as compared with paroxysmal atrial fibrillation.

BACKGROUND: Measurements of both the dominant frequency (DF) and the time series morphology of complex fractionated atrial electrograms (CFAE) are useful to distinguish persistent from paroxysmal atrial fibrillation (AF). In this study, an algorithm was devised to extract morphologic components according to frequency, and its usefulness for distinguishing CFAE was shown. METHOD: CFAE of length 16s were obtained at two sites each from the four pulmonary vein ostia (PV), and from anterior and posterior left atrial free wall (FW), in nine paroxysmal and 10 longstanding persistent AF patients. The DF was computed for each of two 8s CFAE segments in each 16s recording. Each CFAE segment was then transformed into a set of basis vectors, which represent electrogram morphology at each frequency. The dominant morphology (DM) is defined as the ensemble average of sequential signal segments, with the segment length equal to the period at the DF. The DMs of the two 8s pairs were correlated. Normalized correlation coefficients were tabulated for all data, and separately for PV and FW. The means and coefficients of variation of the DM correlation coefficients were then plotted, and a linear discriminant function was used to classify persistent versus paroxysmal AF data. For comparison with DM results, CFE-mean and interval confidence level (ICL) were also calculated for persistent versus paroxysmal AF data. RESULTS: Mean correlation of the DM, 1st 8s versus 2nd 8s data, was 0.62+0.22 for persistent versus 0.50+0.19 for paroxysmal CFAE for all recording sites (p<0.001). At single anatomical locations, correlation was greater in persistents than paroxysmals at all sites, but achieved significance only at the left superior (p<0.001) and right superior (p<0.05) PV. Spatial variation in correlation coefficient was greater in paroxysmal than persistent AF (not significant). Using the means of DF correlation coefficients, 17/19 patients were classified correctly. The CFE-mean parameter averaged 89.01±20.99 ms in persistents versus 93.96±33.81 ms in paroxysmals (p<0.05), while ICL averaged 94.54±18.52 deflections/8s for persistents versus 90.70±19.28 deflections/8s for paroxysmals (p<0.05). CONCLUSIONS: In CFAE recordings, the DM parameter was found to have greater temporal morphologic variation in paroxysmal as compared with persistent AF data (p<0.001). In contrast, only moderate significance between paroxysmal versus persistent AF data was found when using the of CFE-mean and ICL parameters (p<0.05). The DM parameter may thus be useful as a new measure to discern both temporal and spatial variations in CFAE in paroxysmal versus persistent AF recordings.

Computational method for high resolution spectral analysis of fractionated atrial electrograms.

BACKGROUND: The discrete Fourier transform (DFT) is often used as a spectral estimator for analysis of complex fractionated atrial electrograms (CFAE) acquired during atrial fibrillation (AF). However, time resolution can be unsatisfactory, as the frequency resolution is proportional to rate/time interval. In this study we compared the DFT to a new spectral estimator with improved time-frequency resolution. METHOD: Recently, a novel spectral estimator (NSE) based upon signal averaging was derived and implemented computationally. The NSE is similar to the DFT in that both estimators model the autocorrelation function to form the power spectrum. However, as derived in this study, NSE frequency resolution is proportional to rate/period(2) and thus unlike the DFT, is not directly dependent on the window length. We hypothesized that the NSE would provide improved time resolution while maintaining satisfactory frequency resolution for computation of CFAE spectral parameters. Window lengths of 8s, 4s, 2s, 1s, and 0.5s were used for analysis. Two criteria gauged estimator performance. Firstly, a periodic electrogram pattern with phase jitter was embedded in interference. The error in detecting the frequency of the periodic pattern was determined. Secondly, significant differences in spectral parameters for paroxysmal versus persistent AF data, which have known dissimilarities, were determined using the DFT versus NSE methods. The parameters measured were the dominant amplitude, dominant frequency, and mean spectral profile. RESULTS: At all time resolutions, the error in detecting the frequency of the repeating electrogram pattern was less for NSE than for DFT (p<0.001). The DFT was accurate to 2s time resolution/0.5 Hz frequency resolution, while the NSE was accurate to 0.5s time resolution/0.05 Hz frequency resolution. At all time resolutions, significant differences in the dominant amplitude spectral parameter for paroxysmal versus persistent CFAE were greater using NSE than DFT (p<0.0001). For three of five time resolutions, the NSE had greater significant differences than DFT for discriminating the dominant frequency and mean spectral profile parameters between AF types. CONCLUSIONS: The results suggest that the NSE has improved performance versus DFT for measurement of CFAE spectral properties.

A clinical feasibility study of atrial and ventricular electromechanical wave imaging.

BACKGROUND: Cardiac resynchronization therapy (CRT) and atrial ablation procedures currently lack a noninvasive imaging modality for reliable treatment planning and monitoring. Electromechanical wave imaging (EWI) is an ultrasound-based method that has previously been shown to be capable of noninvasively and transmurally mapping the activation sequence of the heart in animal studies by estimating and imaging the electromechanical wave, that is, the transient strains occurring in response to the electrical activation, at both high temporal and spatial resolutions. OBJECTIVE: To demonstrate the feasibility of transthoracic EWI for mapping the activation sequence during different cardiac rhythms in humans. METHODS: EWI was perfor`med in patients undergoing CRT and a left bundle branch block (LBBB) during sinus rhythm, left ventricular pacing, and right ventricular pacing, as well as in patients with atrial flutter (AFL) before intervention, EWI findings from patients with AFL were subsequently correlated with results from invasive intracardiac electrical mapping studies during intervention. In addition, the feasibility of single-heartbeat EWI at 2000 frames/s is demonstrated in humans for the first time in a patient with both AFL and right bundle branch block (RBBB). RESULTS: The electromechanical activation maps demonstrated the capability of EWI to localize the pacing sites and characterize the bundle branch block activation sequence transmurally in patients with CRT. In patients with AFL, the EWI propagation patterns obtained with EWI were in excellent agreement with those obtained from invasive intracardiac mapping studies. CONCLUSIONS: Our findings demonstrate the potential capability of EWI to aid in the assessment and follow-up of patients undergoing CRT pacing therapy and atrial ablation, with preliminary validation in vivo.

Spectral profiles of complex fractionated atrial electrograms are different in longstanding and acute onset atrial fibrillation atrial electrogram spectra.

UNLABELLED: Spectral Profiles of CFAE. BACKGROUND: Spectral analysis of complex fractionated atrial electrograms (CFAE) may be useful for gaining insight into mechanisms underlying paroxysmal and longstanding atrial fibrillation (AF). The commonly used dominant frequency (DF) measurement has limitations. METHOD: CFAE recordings were acquired from outside the 4 pulmonary vein ostia and at 2 left atrial free wall sites in 10 paroxysmal and 10 persistent AF patients. Two consecutive 8s-series were analyzed from recordings >16s in duration. Power spectra were computed for each 8s-series in the range 3-12 Hz and normalized. The mean and standard deviation of normalized power spectra (MPS and SPS, respectively) were compared for paroxysmal versus persistent CFAE. Also, the DF and its peak amplitude (ADF) were compared for pulmonary vein sites only. Power spectra were computed using ensemble average and Fourier methods. RESULTS: No significant changes occurred in any parameter from the first to second recording sequence. For both sequences, MPS and SPS were significantly greater, and DF and ADF were significantly less, in paroxysmals versus persistents. The MPS and ADF measurements from ensemble spectra produced the most significant differences in paroxysmals versus persistents (P < 0.0001). DF differences were less significant, which can be attributed to the relatively high variability of DF in paroxysmals. The MPS was correlated to the duration of uninterrupted persistent AF prior to electrophysiologic study (P = 0.01), and to left atrial volume for all AF (P < 0.05). CONCLUSIONS: The MPS and ADF measurements introduced in this study are probably superior to DF for discerning power spectral differences in paroxysmal versus longstanding CFAE. (J Cardiovasc Electrophysiol, Vol. 23, pp. 971-979, September 2012).

Identification of recurring patterns in fractionated atrial electrograms using new transform coefficients.

BACKGROUND: Identification of recurrent patterns in complex fractionated atrial electrograms (CFAE) has been used to differentiate paroxysmal from persistent atrial fibrillation (AF). Detection of the atrial CFAE patterns might therefore be assistive in guiding radiofrequency catheter ablation to drivers of the arrhythmia. In this study a technique for robust detection and classification of recurrent CFAE patterns is described. METHOD: CFAE were obtained from the four pulmonary vein ostia, and from the anterior and posterior left atrium, in 10 patients with paroxysmal AF and 10 patients with longstanding persistent AF (216 recordings in total). Sequences 8.4 s in length were analyzed (8,192 sample points, 977 Hz sampling). Among the 216 sequences, two recurrent patterns A and B were substituted for 4 and 5 of the sequences, respectively. To this data, random interference, and random interference + noise were separately added. Basis vectors were constructed using a new transform that is derived from ensemble averaging. Patterns A and B were then detected and classified using a threshold level of Euclidean distance between spectral signatures as constructed with transform coefficients. RESULTS: In the presence of interference, sensitivity to detect and distinguish two patterns A and B was 96.2%, while specificity to exclude nonpatterns was 98.0%. In the presence of interference + noise, sensitivity was 89.1% while specificity was 97.0%. CONCLUSIONS: Transform coefficients computed from ensemble averages can be used to succinctly quantify synchronized patterns present in AF data. The technique is useful to automatically detect recurrent patterns in CFAE that are embedded in interference without user bias. This quantitation can be implemented in real-time to map the AF substrate prior to and during catheter ablation.

A new transform for the analysis of complex fractionated atrial electrograms.

BACKGROUND: Representation of independent biophysical sources using Fourier analysis can be inefficient because the basis is sinusoidal and general. When complex fractionated atrial electrograms (CFAE) are acquired during atrial fibrillation (AF), the electrogram morphology depends on the mix of distinct nonsinusoidal generators. Identification of these generators using efficient methods of representation and comparison would be useful for targeting catheter ablation sites to prevent arrhythmia reinduction. METHOD: A data-driven basis and transform is described which utilizes the ensemble average of signal segments to identify and distinguish CFAE morphologic components and frequencies. Calculation of the dominant frequency (DF) of actual CFAE, and identification of simulated independent generator frequencies and morphologies embedded in CFAE, is done using a total of 216 recordings from 10 paroxysmal and 10 persistent AF patients. The transform is tested versus Fourier analysis to detect spectral components in the presence of phase noise and interference. Correspondence is shown between ensemble basis vectors of highest power and corresponding synthetic drivers embedded in CFAE. RESULTS: The ensemble basis is orthogonal, and efficient for representation of CFAE components as compared with Fourier analysis (p ≤ 0.002). When three synthetic drivers with additive phase noise and interference were decomposed, the top three peaks in the ensemble power spectrum corresponded to the driver frequencies more closely as compared with top Fourier power spectrum peaks (p ≤ 0.005). The synthesized drivers with phase noise and interference were extractable from their corresponding ensemble basis with a mean error of less than 10%. CONCLUSIONS: The new transform is able to efficiently identify CFAE features using DF calculation and by discerning morphologic differences. Unlike the Fourier transform method, it does not distort CFAE signals prior to analysis, and is relatively robust to jitter in periodic events. Thus the ensemble method can provide a useful alternative for quantitative characterization of CFAE during clinical study.

Differences in repeating patterns of complex fractionated left atrial electrograms in longstanding persistent atrial fibrillation as compared with paroxysmal atrial fibrillation.

BACKGROUND: Complex fractionated atrial electrograms (CFAE) are morphologically more uniform in persistent longstanding as compared with paroxysmal atrial fibrillation (AF). It was hypothesized that this may result from a greater degree of repetitiveness in CFAE patterns at disparate left atrial (LA) sites in longstanding AF. METHODS AND RESULTS: CFAEs were obtained from recording sites outside the 4 pulmonary vein (PV) ostia and at a posterior and an anterior LA site during paroxysmal and longstanding persistent AF (10 patients each, 120 sequences total). To quantify repetitiveness in CFAE, the dominant frequency was measured from ensemble spectra using 8.4-second sequences, and repetitiveness was calculated by 2 novel techniques: linear prediction and Fourier reconstruction methods. Lower prediction and reconstruction errors were considered indicative of increasing repetitiveness and decreasing randomness. In patients with paroxysmal AF, CFAE pattern repetitiveness was significantly lower (randomness higher) at antral sites outside PV ostia as compared with LA free wall sites (P < 0.001). In longstanding AF, repetitiveness increased outside the PV ostia, especially outside the left superior PV ostium, and diminished at the LA free wall sites. The result was that in persistent AF, there were no significant site-specific differences in CFAE repetitiveness at the selected LA locations used in this study. Average dominant frequency magnitude was 5.32 ± 0.29 Hz in paroxysmal AF and higher in longstanding AF, at 6.27 ± 0.13 Hz (P < 0.001), with the frequency of local activation approaching a common upper bound for all sites. CONCLUSIONS: In paroxysmal AF, CFAE repetitiveness is low and randomness high outside the PVs, particularly the left superior PV. As evolution to persistent longstanding AF occurs, CFAE repetitiveness becomes more uniformly distributed at disparate sites, possibly signifying an increasing number of drivers from remote PVs.