New methods for estimating local electrical activation rate during atrial fibrillation

Electromechanical Cycle Length Mapping for atrial arrhythmia detection and cardioversion success assessment

BACKGROUND

Direct current cardioversion (DCCV) is an established treatment to acutely convert atrial fibrillation (AF) to normal sinus rhythm. Yet, more than 70% of patients revert to AF shortly thereafter. Electromechanical Cycle Length Mapping (ECLM) is a high framerate, spectral analysis technique shown to non-invasively characterize electromechanical activation in paced canines and re-entrant flutter patients. This study assesses ECLM feasibility to map and quantify atrial arrhythmic electromechanical activation rates and inform on 1-day and 1-month DCCV response.

METHODS

Forty-five subjects (30 AF; 15 healthy sinus rhythm (SR) controls) underwent transthoracic ECLM in four standard apical 2D echocardiographic views. AF patients were imaged within 1 h pre- and post-DCCV. 3D-rendered atrial ECLM cycle length (CL) maps and spatial CL histograms were generated. CL dispersion and percentage of arrhythmic CLs≤333ms across the entire atrial myocardium were computed transmurally. ECLM results were subsequently used as indicators of DCCV success.

RESULTS

ECLM successfully confirmed the electrical atrial activation rates in 100% of healthy subjects (R2=0.96). In AF, ECLM maps localized the irregular activation rates pre-DCCV and confirmed successful post-DCCV with immediate reduction or elimination. ECLM metrics successfully distinguished DCCV 1-day and 1-month responders from non-responders, while pre-DCCV ECLM values independently predicted AF recurrence within 1-month post-DCCV.

CONCLUSIONS

ECLM can characterize electromechanical activation rates in AF, quantify their extent, and identify and predict short- and long-term AF recurrence. ELCM constitutes thus a noninvasive arrhythmia imaging modality that can aid clinicians in simultaneous AF severity quantification, prediction of AF DCCV response, and personalized treatment planning.

High Dominant Frequencies and Fractionated Potentials Do Not Indicate Focal or Rotational Activation During AF

BACKGROUND

Dominant frequencies (DFs) or complex fractionated atrial electrograms (CFAEs), indicative of focal sources or rotational activation, are used to identify target sites for atrial fibrillation (AF) ablation in clinical studies, although the relationship among DF, CFAE, and activation patterns remains unclear.

OBJECTIVES

This study sought to investigate the relationship between patterns of activation underlying DF and CFAE sites during AF.

METHODS

Epicardial high-resolution mapping of the right and left atrium including Bachmann's bundle was performed in 71 participants. We identified the highest dominant frequency (DF_max) and highest degree of CFAE (CFAE_max) with the use of existing clinical criteria and classified patterns of activation as focal or rotational activation and smooth propagation, conduction block (CB), collision and remnant activity, and fibrillation potentials as single, double, or fractionated potentials containing, respectively, 1, 2, or 3 or more negative deflections. Relationships among activation patterns, DF_max, and potential types were investigated.

RESULTS

DF_max were primarily located at the left atrioventricular groove and did not harbor focal activation (proportion focal waves: 0% [IQR: 0%-2%]). Compared with non-DF_max sites, DF_max were characterized by more frequent smooth propagation (22% [IQR: 7%-48%] vs 17% [IQR: 11%-24%]; P = 0.001), less frequent conduction block (69% [IQR: 51%-81%] vs 74% [IQR: 69%-78%]; P = 0.006), a higher proportion of single potentials (72% [IQR: 55%-84%] vs 6%1 [IQR: 55%-65%]; P = 0.003), and a lower proportion of fractionated potentials (4% [IQR: 1%-11%] vs 12% [IQR: 9%-15%]; P = 0.004). CFAE_max were mainly found at the pulmonary veins area, and only 1% [IQR: 0%-2%] of all CFAE_max contained focal activation. Compared with non-CFAE_max sites, CFAE_max sites were characterized by less frequent smooth propagation (1% [IQR: 0%-1%] vs 17% [IQR: 12%-24%]; P < 0.001) and more frequent remnant activity (20% [IQR: 12%-29%] vs 8% [IQR: 5%-10%]; P < 0.001), and harbored predominantly fractionated potentials (52% [IQR: 43%-66%] vs 12% [IQR: 9%-14%]; P < 0.001).

CONCLUSIONS

Focal or rotational patterns of activation were not consistently detected at DF_max domains and CFAE_max sites. These findings do not support the concept of targeting DF_max or CFAE_max according to existing criteria for AF ablation.

Anti-arrhythmic strategies for atrial fibrillation: The role of computational modeling in discovery, development, and optimization

Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with increased risk of cerebrovascular stroke, and with several other pathologies, including heart failure. Current therapies for AF are targeted at reducing risk of stroke (anticoagulation) and tachycardia-induced cardiomyopathy (rate or rhythm control). Rate control, typically achieved by atrioventricular nodal blocking drugs, is often insufficient to alleviate symptoms. Rhythm control approaches include antiarrhythmic drugs, electrical cardioversion, and ablation strategies. Here, we offer several examples of how computational modeling can provide a quantitative framework for integrating multiscale data to: (a) gain insight into multiscale mechanisms of AF; (b) identify and test pharmacological and electrical therapy and interventions; and (c) support clinical decisions. We review how modeling approaches have evolved and contributed to the research pipeline and preclinical development and discuss future directions and challenges in the field.

Atrial Tachycardias After Atrial Fibrillation Ablation Manifest Different Waveform Characteristics: Implications for Characterizing Tachycardias

INTRODUCTION

Atrial fibrillation (AF) ablation patients often manifest atrial tachycardias (AT) with atypical ECG morphologies that preclude accurate localization and mechanism. Diagnostic maneuvers used to define ATs during electrophysiology studies can be limited by tachycardia termination or transformation. Additional methods of characterizing post-AF ablation ATs are required.

METHODS AND RESULTS

We evaluated the utility of noninvasive ECG signal analytics in postablation AF patients for the following features: (1) Localization of ATs (i.e., right vs. left atrium), and (2) Identification of common left AT mechanisms (i.e., focal vs. macroreentrant). Atrial waveforms from the surface ECG were used to analyze (1) spectral organization, including dominant amplitude (DA) and mean spectral profile (MP), and (2) temporospatial variability, using temporospatial correlation coefficients. We studied 94 ATs in 71 patients who had undergone prior pulmonary vein isolation for AF and returned for a second ablation: (1) right atrial cavotricuspid-isthmus dependent (CTI) ATs (n = 21); (2) left atrial macroreentrant ATs (n = 41) and focal ATs (n = 32). Right CTI ATs manifested higher DAs and lower MPs than left ATs, indicative of greater stability and less complexity in the frequency spectrum. Left macroreentrant ATs possessed higher temporospatial organization than left focal ATs.

CONCLUSIONS

Noninvasively recorded atrial waveform signal analyses show that right ATs possess more stable activation properties than left ATs, and left macroreentrant ATs manifest higher temporospatial organization than left focal ATs. Further prospective analyses evaluating the role these novel ECG-derived tools can play to help localize and identify mechanisms of common ATs in AF ablation patients are warranted.

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.

Real-time electrocardiogram P-QRS-T detection-delineation algorithm based on quality-supported analysis of characteristic templates

The main objective of this study is to introduce a simple, low-latency, and accurate algorithm for real-time detection of P-QRS-T waves in the electrocardiogram (ECG) signal. In the proposed method, real-time signal preprocessing, which includes high frequency noise filtering and baseline wander reduction, is performed by applying discrete wavelet transform (DWT). A method based on signal first-order derivative and adaptive threshold adjustment is employed for real-time detection of the QRS complex. Moreover, detection and delineation of P- and T-waves are achieved by correlation analysis conducted between signal and their templates. Besides, signal quality is investigated online, and if the quality of the analysis window is unacceptable, then the algorithm will guess (estimate) the locations of P- and T-waves. The operating characteristics of the proposed algorithm are evaluated by its implementation to an artificially generated ECG signal whose quality is adjustable from the best (Quality, 100%) to the worst (Quality, ≤40%) cases based on the random-walk noise theory. The algorithm was applied to the MIT-BIH arrhythmia database, QT database, and Physionet/CinC challenge 2011competition database. The obtained results, which were based on the QT database, showed sensitivity and positive predictivity of Se=99.63% and P+=99.83%, Se=99.83% and P+=99.98%, and Se=99.74% and P+=99.89% for the detection of P-, QRS-, and T-waves, respectively, and the obtained results, which were based on the MIT-BIH arrhythmia database, showed Se=99.81% and P+=99.70% for the detection of the QRS complex. Moreover, it will be shown that the results of the proposed method are reliable for a minimum signal quality value of 70%. According to numerical assessments, 8-ms after the occurrence of R-wave, its location will be identified by the computer code of the proposed algorithm. This parameter is 198-ms and 177-ms for P- and T-waves, respectively.

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.

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.

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.

Comparison of spectral estimators for characterizing fractionated atrial electrograms

Background

Complex fractionated atrial electrograms (CFAE) acquired during atrial fibrillation (AF) are commonly assessed using the discrete Fourier transform (DFT), but this can lead to inaccuracy. In this study, spectral estimators derived by averaging the autocorrelation function at lags were compared to the DFT.

Method

Bipolar CFAE of at least 16 s duration were obtained from pulmonary vein ostia and left atrial free wall sites (9 paroxysmal and 10 persistent AF patients). Power spectra were computed using the DFT and three other methods: 1. a novel spectral estimator based on signal averaging (NSE), 2. the NSE with harmonic removal (NSH), and 3. the autocorrelation function average at lags (AFA). Three spectral parameters were calculated: 1. the largest fundamental spectral peak, known as the dominant frequency (DF), 2. the DF amplitude (DA), and 3. the mean spectral profile (MP), which quantifies noise floor level. For each spectral estimator and parameter, the significance of the difference between paroxysmal and persistent AF was determined.

Results

For all estimators, mean DA and mean DF values were higher in persistent AF, while the mean MP value was higher in paroxysmal AF. The differences in means between paroxysmals and persistents were highly significant for 3/3 NSE and NSH measurements and for 2/3 DFT and AFA measurements (p<0.001). For all estimators, the standard deviation in DA and MP values were higher in persistent AF, while the standard deviation in DF value was higher in paroxysmal AF. Differences in standard deviations between paroxysmals and persistents were highly significant in 2/3 NSE and NSH measurements, in 1/3 AFA measurements, and in 0/3 DFT measurements.

Conclusions

Measurements made from all four spectral estimators were in agreement as to whether the means and standard deviations in three spectral parameters were greater in CFAEs acquired from paroxysmal or in persistent AF patients. Since the measurements were consistent, use of two or more of these estimators for power spectral analysis can be assistive to evaluate CFAE more objectively and accurately, which may lead to improved clinical outcome. Since the most significant differences overall were achieved using the NSE and NSH estimators, parameters measured from their spectra will likely be the most useful for detecting and discerning electrophysiologic differences in the AF substrate based upon frequency analysis of CFAE.

Transformation of videocapsule images to detect small bowel mucosal differences in celiac versus control patients

BACKGROUND

Videocapsule endoscopy can be useful to detect small intestinal pathology in celiac disease patients. However, presence of extraneous features including air bubbles and opaque fluids can complicate the analysis. A technique for quantitative analysis of videocapsule images is presented that is robust to presence of extraneous features.

METHOD

Videocapsule clips were acquired from five small intestinal locations in 12 celiacs with villous atrophy and 11 control patients. Clips were 200 frames in length, their resolution was 576 × 576 pixels and 256 grayscale levels, with 2/s frame rate. The dominant period (DP), defined as the tallest peak in the ensemble average power spectrum, was computed over each clip without removal of extraneous features. Ensemble average basis images were constructed, and measurements were made of their frame-to-frame variation in brightness and texture.

RESULTS

From pooled basis images, celiac images had greater texture than controls and exhibited more brightness variation (p<0.05 in mean and p<0.01 in standard deviation). In celiacs, correlation existed between greater textural alterations versus longer DP (r²=0.47), and between greater brightness variation and longer DP (r²=0.33). There was no significant correlation between quantitative features and DP in controls (r²<0.25).

CONCLUSIONS

Using this new method, celiac videoclips were quantitatively distinguishable from control videoclips without manual or computer-assisted detection, masking, and removal of extraneous image features. Furthermore, in celiac but not control basis images, larger textural and brightness alterations were correlated to longer DP. Greater textural and brightness alterations, and thus longer periodicities, are likely related to presence of villous atrophy.

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

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).

Improved frequency resolution for characterization of complex fractionated atrial electrograms

BACKGROUND

The dominant frequency of the Fourier power spectrum is useful to analyze complex fractionated atrial electrograms (CFAE), but spectral resolution is limited and uniform from DC to the Nyquist frequency. Herein the spectral resolution of a recently described and relatively new spectral estimation technique is compared to the Fourier radix-2 implementation.

METHODS

In 10 paroxysmal and 10 persistent atrial fibrillation patients, 216 CFAE were acquired from the pulmonary vein ostia and left atrial free wall (977 Hz sampling rate, 8192 sample points, 8.4 s duration). With these parameter values, in the physiologic range of 3-10 Hz, two frequency components can theoretically be resolved at 0.24 Hz using Fourier analysis and at 0.10 Hz on average using the new technique. For testing, two closely-spaced periodic components were synthesized from two different CFAE recordings, and combined with two other CFAE recordings magnified 2×, that served as interference signals. The ability to resolve synthesized frequency components in the range 3-4 Hz, 4-5 Hz, …, 9-10 Hz was determined for 15 trials each (105 total).

RESULTS

With the added interference, frequency resolution averaged 0.29 ± 0.22 Hz for Fourier versus 0.16 ± 0.10 Hz for the new method (p < 0.001). The misalignment error of spectral peaks versus actual values was ±0.023 Hz for Fourier and ±0.009 Hz for the new method (p < 0.001). One or both synthesized peaks were lost in the noise floor 13/105 times using Fourier versus 4/105 times using the new method.

CONCLUSIONS

Within the physiologically relevant frequency range for characterization of CFAE, the new method has approximately twice the spectral resolution of Fourier analysis, there is less error in estimating frequencies, and peaks appear more readily above the noise floor. Theoretically, when interference is not present, to resolve frequency components separated by 0.10 Hz using Fourier analysis would require an 18.2 s sequence duration, versus 8.4 s with the new method.

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.

Robust spectral analysis of videocapsule images acquired from celiac disease patients

Background

Dominant frequency (DF) analysis of videocapsule endoscopy images is a new method to detect small intestinal periodicities that may result from mechanical rhythms such as peristalsis. Longer periodicity is related to greater image texture at areas of villous atrophy in celiac disease. However, extraneous features and spatiotemporal phase shift may mask DF rhythms.

Method

The robustness of Fourier and ensemble averaging spectral analysis to compute DF was tested. Videocapsule images from the distal duodenum of 11 celiac patients (frame rate 2/s and pixel resolution 576 × 576) were analyzed. For patients 1, 2, ... 11, respectively, a total of 10, 11, ..., 20 sequential images were extracted from a randomly selected time epoch. Each image sequence was artificially repeated to 200 frames, simulating periodicities of 0.2, 0.18, ..., 0.1Hz, respectively. Random white noise at four different levels, spatiotemporal phase shift, and frames with air bubbles were added. Power spectra were constructed pixel-wise over 200 frames, and an average spectrum was computed from the 576 × 576 individual spectra. The largest spectral peak in the average spectrum was the estimated DF. Error was defined as the absolute difference between actual DF and estimated DF.

Results

For Fourier analysis, the mean absolute error between estimated and actual DF was 0.032 ± 0.052Hz. Error increased with greater degree of random noise imposed. In contrast, all ensemble average estimates precisely predicted the simulated DF.

Conclusions

The ensemble average DF estimate of videocapsule images with simulated periodicity is robust to noise and spatiotemporal phase shift as compared with Fourier analysis. Accurate estimation of DF eliminates the need to impose complex masking, extraction, and/or corrective preprocessing measures.

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.

Frequency analysis of atrial fibrillation surface and intracardiac electrograms during pulmonary vein isolation

AIMS

Frequency analysis of atrial electrograms from patients diagnosed with persistent atrial fibrillation (AF) appears to be crucial in its clinical diagnosis. This work explores the fibrillatory frequency properties of both surface and intracardiac electrograms before and after pulmonary vein isolation (PVI) using three time-frequency techniques.

METHODS AND RESULTS

Surface electrocardiograms (ECGs) of 21 patients diagnosed with persistent AF undergoing PVI were recorded. Three methods, Fourier, ensemble average, and wavelet analysis, were used to identify the dominant frequency (DF) in surface ECGs. Dominant frequency was also computed in electrograms recorded within the coronary sinus (CS). Dominant frequency measured within the CS was best estimated in surface lead V1 using both Fourier (relative error: 10.94 ± 10.37%, correlation: 0.58) and wavelet analysis (relative error: 10.97 ± 11.08%, correlation: 0.53). Ensemble average gave highest relative error (21.29 ± 18.07%) and lowest correlation (0.10). Dominant frequency decreased after right PVI. This decrease was significant (P< 0.05) in most of the patients (13, 14, and 7 out of 14 when Fourier, wavelets, and ensemble average was used; 14 in CS). Further isolation of the left pulmonary veins (PVs) yielded a significant (P< 0.05) decrease in only a few of them (3, 4, and 2 out of 14 when Fourier, wavelets, and ensemble average was used; 4 in CS).

CONCLUSION

Wavelet and Fourier analysis are good tools for estimating the atrial fibrillatory rate from surface ECG. A drop was observed in the DF value after isolation of the right PV. However, after left PVI this decrease was smaller.

Pathophysiological mechanisms of atrial fibrillation: a translational appraisal

Atrial fibrillation (AF) is an arrhythmia that can occur as the result of numerous different pathophysiological processes in the atria. Some aspects of the morphological and electrophysiological alterations promoting AF have been studied extensively in animal models. Atrial tachycardia or AF itself shortens atrial refractoriness and causes loss of atrial contractility. Aging, neurohumoral activation, and chronic atrial stretch due to structural heart disease activate a variety of signaling pathways leading to histological changes in the atria including myocyte hypertrophy, fibroblast proliferation, and complex alterations of the extracellular matrix including tissue fibrosis. These changes in electrical, contractile, and structural properties of the atria have been called "atrial remodeling." The resulting electrophysiological substrate is characterized by shortening of atrial refractoriness and reentrant wavelength or by local conduction heterogeneities caused by disruption of electrical interconnections between muscle bundles. Under these conditions, ectopic activity originating from the pulmonary veins or other sites is more likely to occur and to trigger longer episodes of AF. Many of these alterations also occur in patients with or at risk for AF, although the direct demonstration of these mechanisms is sometimes challenging. The diversity of etiological factors and electrophysiological mechanisms promoting AF in humans hampers the development of more effective therapy of AF. This review aims to give a translational overview on the biological basis of atrial remodeling and the proarrhythmic mechanisms involved in the fibrillation process. We pay attention to translation of pathophysiological insights gained from in vitro experiments and animal models to patients. Also, suggestions for future research objectives and therapeutical implications are discussed.

Frequency domain and time complex analyses manifest low correlation and temporal variability when calculating activation rates in atrial fibrillation patients

BACKGROUND

Atrial fibrillation (AF) activation rates have been calculated using both frequency domain and time complex analyses. Direct comparisons of these methods are limited. We report: (1) their correlation when measuring AF activation rates, (2) comparisons of recording durations required to minimize variability, and (3) differences in the temporal reproducibility.

METHODS

AF activation rates were calculated using domain frequency (DF) (via fast Fourier transform) and time complex (TC) (via beat-to-beat activation measurements) analyses. We compared: (1) AF frequencies derived from each method; (2) successively longer subinterval durations to their 16-second reference intervals, and (3) the correlation between consecutively collected 8-second segments and segments collected 10 minutes apart.

RESULTS

There was low intraclass correlation coefficient (ICC = 0.234) when comparing AF activation rates derived using DF versus TC analysis. There was no difference in the frequencies between any of the subintervals compared to their 16-second reference intervals, but variability of measurements was higher for intervals <8 seconds (P < 0.01). Correlations between successive segments and segments taken 10 minutes apart were 0.92 and 0.75 using DF analysis (P < 0.001), and 0.72 and 0.49 using TC analysis (P < 0.001).

CONCLUSIONS

There is low correlation between the DF and TC methods of analyzing AF activation rates. While AF rates do not differ between subintervals and 16-second reference electrograms, the variability of measurements is dependent upon the subinterval duration, and increases for durations less than 8 seconds. AF rates were prone to change over a 10-minute time period. These results point out existing clinical limitations of measuring atrial activation rates in AF patients.

Distinguishing patients with celiac disease by quantitative analysis of videocapsule endoscopy images

BACKGROUND

Although videocapsule endoscopy images are helpful in the evaluation of celiac disease, their interpretation is subjective. Quantitative disease markers could assist in determining the extent of villous atrophy and response to treatment.

METHOD

Capsule endoscopy images were acquired from celiac patients with small bowel pathology (N=11) and from control patients (N=10). Image resolution was 576x576 pixels in dimension, 256 grayscale levels, and had a 2 s(-1) frame rate. Pixel brightness and image texture were measured over 10x10 pixel subimages and then averaged for 56x56 subimages per frame. Measurements were obtained at five locations from proximal to distal small intestine in each patient. At each location, measurements were calculated using 200 consecutive image frames (100s). Mean frame-to-frame pixel brightness, image texture, and periodicity in brightness, an estimate of wall motion or intestinal motility, were computed and used for classification with a nonlinear discriminant function.

RESULTS

From pooled data, celiac images had greater texture than did images from control patients (p<0.001) and exhibited more frame-to-frame brightness variation as well (p=0.032). The dominant period of brightness was longer in celiacs (p=0.001), possibly indicating decreased motility. Using the markers for three-dimensional nonlinear classification of celiacs versus controls, sensitivity was 92.7% and specificity was 93.5%. The relationship between dominant period and small intestinal transit time was approximately linear for both celiacs and controls (r(2)=0.42 and r(2)=0.55, respectively).

CONCLUSIONS

Videocapsule images can be quantified to detect villous atrophy throughout the small intestine, and to distinguish individuals with celiac disease from individuals lacking mucosal atrophy.