Atrial Fibrillation Signals Research Articles

An Automatic System for the Analysis and Classification of Human Atrial Fibrillation Patterns from Intracardiac Electrograms

This paper presents an automatic system for the analysis and classification of atrial fibrillation (AF) patterns from bipolar intracardiac signals. The system is made up of: 1) a feature-extraction module that defines and extracts a set of measures potentially useful for characterizing AF types on the basis of their degree of organization; 2) a feature-selection module (based on the Jeffries-Matusita distance and a branch and bound search algorithm) identifying the best subset of features for discriminating different AF types; and 3) a support vector machine technique-based classification module that automatically discriminates the AF types according to the Wells' criteria. The automatic system was applied on 100 intracardiac AF signal strips and on a selection of 11 representative features, demonstrating: a) the possibility to properly identify the most significant features for the discrimination of AF types; b) higher accuracy (97.7% using the seven most informative features) than the traditional maximum likelihood classifier; and c) effectiveness in AF classification also with few training samples (accuracy = 88.3% with only five training signals). Finally, the system identifies a combination of indices characterizing changes of morphology of atrial activation waves and perturbation of the isoelectric line as the most effective in separating the AF types.

Frequency Tracking of Atrial Fibrillation Using Hidden Markov Models

A hidden Markov model (HMM) is employed to improve noise robustness when tracking the dominant frequency of atrial fibrillation (AF) in the electrocardiogram (ECG). Following QRST cancellation, a sequence of observed frequency states is obtained from the residual ECG, using the short-time Fourier transform. Based on the observed state sequence, the Viterbi algorithm retrieves the optimal state sequence by exploiting the state transition matrix, incorporating knowledge on AF characteristics, and the observation matrix, incorporating knowledge of the frequency estimation method and signal-to-noise ratio (SNR). The tracking method is evaluated with simulated AF signals to which noise, obtained from ECG recordings, has been added at different SNRs. The results show that the use of HMM improves performance considerably by reducing the rms error associated with frequency tracking: at 4-dB SNR, the rms error drops from 0.2 to 0.04 Hz.

Technical Considerations for Dominant Frequency Analysis

Dominant frequency (DF) analysis of atrial electrograms has been used to characterize atrial fibrillation (AF). The aim of this study was to explore technical issues that may affect the estimation of local activation rate during AF using DF analysis. Epicardial atrial electrograms recorded during AF from 10 dogs were used to evaluate the effects of unipolar versus bipolar recordings, bipolar electrode spacing, postrecording processing, far field ventricular depolarizations, ventricular template subtraction, and signal duration on DF analysis. Simulated electrograms were used to evaluate the effect of far field ventricular depolarizations and signal-to-noise ratio. DFs were compared with activation rates obtained by manual marking and the reproducibility of the DFs was evaluated. Bipolar electrograms were found to be preferable to unipolar electrograms. Preprocessing was a necessary step for bipolar signals, but also aided analysis of unipolar recordings. Ventricular far field depolarizations significantly affected DFs. Ventricular template subtraction helped DF analysis in signals with both minimal and significant ventricular components. A recording duration above 2 seconds was required for reliable DF measurements. Signal-to-noise ratios below 13 dB could also affect DF, particularly for signals with significant amplitude and frequency variation. Various factors affect DF analysis. Proper interpretation of DF analysis requires careful evaluation of the AF signals and robust processing techniques.

Effects of atrial fibrillation substrate and spatiotemporal organization on atrial defibrillation thresholds

We previously showed that canine models of atrial fibrillation (AF) have different substrates (either structural or electrical) that lead to differences in AF characteristics. The purpose of this study was to determine whether the differences in AF characteristics also would lead to differences in atrial defibrillation thresholds (ADFTs). Dogs were divided into five groups: control; MR-mitral regurgitation for 5 weeks; CHF-congestive heart failure for 4 weeks; RAP-rapid atrial pacing for 6 weeks; and METH-acetyl-beta-methylcholine acutely administered. A cross-sectional area of the left atrium was calculated, and AF was induced with rapid atrial pacing. Biphasic shocks with a pulse width of 3/3 ms were delivered through specially constructed shocking catheters with a surface area of 3.7 cm(2) that were placed in the right and left atria. An up-down-up protocol was used to determine the 50% ADFT threshold (ADFT(50)). A wide-bipole AF signal was digitally filtered, and a fast Fourier transform was calculated over a 2-second window every 1 second. The dominant frequency was determined, and the organization index was calculated as the ratio of the area under the dominant peak and its harmonics to the total area of the spectrum. For left atrial size, the CHF and MR groups had a significantly larger atria than did control. ADFT(50) for control, MR, CHF, RAP, and METH groups were 160 +/- 30 V, 120 +/- 50 V, 132 +/- 20 V, 668 +/- 205 V, and 593 +/- 128 V, respectively (analysis of variance, P <.0001). Dominant frequencies were significantly higher and organization indexes significantly lower in the RAP and METH models compared with the other models. RAP and METH canine models had a significantly higher ADFT(50) compared with the other AF models. The increase in ADFT(50) in these models corresponded with higher global dominant frequencies and lower measured organization indexes.

Time-frequency characterization of atrial fibrillation from surface ECG based on Hilbert-Huang transform

In this paper, based on Hilbert-Huang transform (HHT), we develop a new non-invasive time-frequency analysis method to characterize the dynamic behaviour of atrial fibrillation (AF) from surface ECG. We first extract f waves from single-lead ECG records of AF patients using PCA analysis. To capture the non-stationary behaviours of AF signals at different time scales, we use HHT to find the Hilbert spectrum and instantaneous frequency (IF) distribution of residual signals from principal component analysis. Two important feature variables, namely mean IF (mIF) and index of frequency stability over time (IS), are derived from the IF distribution, and in combination will be able to effectively discriminate two different AF types: self-terminating and non-terminating termination. The proposed AF signal decomposition and analysis method will help us efficiently differentiate individual AF patients, advance our understanding of AF mechanisms, and provide useful guidelines for improving administration of AF patients, especially paroxysmal AF.

Analysis of electrocardiograms during atrial fibrillation

The research discussed in this article is motivated by the search for an optimal classification of the different types of atrial fibrillation (AF) on the basis of recorded atrial signals. This would facilitate the selection of an optimal therapy. This article focuses on the biophysical models of the genesis of ECG waveforms during AF. The model of the electric activity of the atria was found to have sufficient realism to be used to describe the electric sources during AF. The inclusion of the volume conduction model resulted in electrocardiographic signals that are in all aspects similar to those observed clinically. The model is currently applied to solve various problems related to optimal signal preprocessing and extraction of features in AF signals for the classification of AF abnormalities. The biophysical model of the atrial activity is an essential element in this research, since it is capable of describing the electric source specifications derived from different hypotheses relating to the etiology of AF

Atrial fibrillation signal organization predicts sinus rhythm maintenance in patients undergoing cardioversion of atrial fibrillation

Electrical remodelling is believed to influence the outcome following cardioversion of patients with persistent atrial fibrillation (AF). However, the results in clinical studies are conflicting. We assessed the hypothesis that non-invasively obtained atrial fibrillatory organization can be used as a predictor of sinus rhythm (SR) maintenance. Fifty-four patients (37 men, age 67+/-11) with persistent AF (median duration 3 months, 1 day to 18 months), without anti-arrhythmic drug treatment, referred for cardioversion were studied. Assessment of the atrial harmonic decay was made by time-frequency analysis of the ECG. At 1-month follow-up, 30 patients had relapsed into AF. The mean harmonic decay at inclusion of those relapsing into AF was 1.5+/-0.3 compared with 1.1+/-0.3 among those maintaining SR (P=0.0004). Using a cut-off value of harmonic decay <or=1.5 to determine suitability for cardioversion would have resulted in a clinically useful discriminator (sensitivity=92%, specificity=47%, PPV=59%, and NPV=88%). This study shows that patients relapsing rapidly to AF have a higher harmonic decay than those maintaining SR. The degree of AF signal organization (harmonic decay) was a superior discriminator to other patient parameters. Further studies are needed to confirm these results and to determine the electrophysiological correlate of harmonic decay.

Atrial activity extraction from atrial fibrillation episodes based on maximum likelihood source separation

A novel non-invasive approach for the atrial wave estimation in atrial fibrillation (AF) episodes is presented. The method is based on the maximum likelihood (ML) solution of independent component analysis (ICA). The densities of the bioelectric independent sources corresponding to the ventricular and atrial activities are previously analyzed, and the prior knowledge extracted from them is considered in order to develop an appropriate separation model. As a consequence, the sources can be recovered through the optimization of the ML criterion. The algorithm is validated using a significant database of synthesized and real AF electrocardiograms (ECGs). A simulation model for the generation of realistic AF signals with known AA is also defined. The results show good performance in terms of SNR and correlation indices when estimated AA is compared to known AA. Regarding the real AF ECGs, the AA sources could always be estimated. Successful AA extraction was validated using spectral parameters. The main frequency of the atrial wave ranged from 4.9 to 7.4 Hz, and the spectral concentration was 49.8% on average.

FREQUENCY COHERENCE MAPPING OF CANINE ATRIAL FIBRILLATION: IMPLICATION FOR ANTI-ARRHYTHMIC DRUG-INDUCED TERMINATION

The coherence mapping was used in frequency analysis of atrial fibrillation (AF). Unipolar electrograms were recorded at AF and during infusion of antiarrhythmic drug, using a 120-electrode array (electrode spacing is 3 mm) placed on the right atrial free wall of canine. We used magnitude-squared coherence in frequency (MSCF) maps to show the frequency distribution during AF. The spectra were limited to 2-15 Hz for AF signals. We chose each site as a reference and established its MSC spectra with other sites in the array. Dominant frequency (DF) was defined by maximum power of spectrum from reference. MSC spectrum was calculated for every point in the array relative to the reference site. If the MSC value of DF is greater than or equal to 0.5, the frequency at this site is set to DF, otherwise it is set to zero. The majority of these values was assigned as the DF of that position. The spatial distribution of DFs was presented as a map, MSCF map. The dominant area (DA) was chosen from the isochronal maps. We developed the MSCF maps for four episodes: (1) normal sinus rhythm: (2) AF without drug infusion: (3) just before AF termination by drug infusion: (4) sinus rhythm following termination. The results show that MSCF map in DA just before AF termination has a uniform pattern. The signals in DA have lower frequencies and longer cycle lengths compared with those in other areas (P &lt; 0.05). In conclusion, this mapping study of the epicardial surface during induced atrial fibrillation in canine right atrial wall has demonstrated that a dominant area near SA node may have an important role for drug-induced AF termination.

Assessment of global atrial fibrillation organization to optimize timing of atrial defibrillation.

We hypothesized that frequency domain analysis of a wide bipolar interatrial electrogram describes the global organization of atrial fibrillation (AF) and should vary over time. By timing shocks to periods of high organization of AF, cardioversion efficacy should improve. A total of 15 dogs (weight, 28.2+/-3.4 kg) were rapidly paced for 48 to 72 hours to induce AF. Coil electrodes with a surface area of 1.80 cm(2) were then placed in the left and right atria to form a wide bipole. Wide bipolar electrograms were digitally filtered, and a fast Fourier transform was performed over a sliding 2-s window every 0.5 s. The organization index (OI) was calculated as the ratio of the area of the dominant peak and its harmonics to the total area of the magnitude spectrum. The atrial defibrillation threshold (ADFT(50)) was determined using a 3-ms/3-ms biphasic shock and an up-down-up protocol. Additional shocks with higher and lower energies were delivered in a random sequence to develop a distribution curve. The OI varied over time, with a mean of 0.42+/-0.03, a maximum of 0.65+/-0.07, and a minimum of 0.20+/-0.06. The OI changed rapidly, with durations of high organization (OI>0.5) ranging from 1 to 5 s. The ADFT(50) for QRS complex-synchronized shocks was 183+/-56 V, versus 142+/-49 V for shocks synchronized to an OI>0.5 (P<0.001). The distribution curve shifted leftward when shocks were synchronized to an OI>0.5. AF signals show a high degree of variability. Shock efficacy is increased when shocks are delivered during periods of high AF organization as determined by the OI method.

Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy.

We hypothesized that frequency domain analysis of an interatrial atrial fibrillation (AF) electrogram would show a correlation of the variance of the signal and the amplitude of harmonic peaks with the periodicity and morphology (organization) of the AF signal and defibrillation efficacy. We sought to develop an algorithm that would provide a high-resolution measurement of the changes in the spatiotemporal organization of AF. AF was initiated with burst atrial pacing in ten dogs. The atrial defibrillation threshold (ADFT50) was determined, and defibrillation was repeated at the ADFT50. Bipolar electrograms from the shocking electrodes were acquired immediately preshock, digitally filtered, and a FFT was performed. The organization index (OI) was calculated as the ratio of the area under the first four harmonic peaks to the total area of the spectrum. For a 4-s window, the mean OI was 0.505 +/- 0.087 for successful shocks, versus 0.352 +/- 0.068 for unsuccessful shocks (p < 0.001). Receiver operator characteristic (ROC) curve analysis was used to determine the optimal sampling window for predicting successful shocks. The area of the ROC curve was 0.8 for a 1-s window, and improved to 0.9 for a 4-s window. We conclude that the spectrum of an AF signal contains information relating to its organization, and can be used in predicting a successful defibrillation.

Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation

A new method for QRST cancellation is presented for the analysis of atrial fibrillation in the surface electrocardiogram (ECG). The method is based on a spatiotemporal signal model which accounts for dynamic changes in QRS morphology caused, e.g., by variations in the electrical axis of the heart. Using simulated atrial fibrillation signals added to normal ECGs, the results show that the spatiotemporal method performs considerably better than does straightforward average beat subtraction (ABS). In comparison to the ABS method, the average QRST-related error was reduced to 58 percent. The results obtained from ECGs with atrial fibrillation agreed very well with those from simulated fibrillation signals.

Decreasing the number of leads required for an implantable atrial defibrillator: Use of a new 2-lead system

Background The purpose of this study was to evaluate the use of a new 2-lead system for detection of atrial fibrillation (AF) and atrial defibrillation. Methods In 16 patients undergoing elective cardioversion of AF, a 2-lead system was compared with the conventional 3-lead system in a randomized trial. The new 2-lead system consisted of a catheter with a distal bipolar right ventricular electrode pair and a proximal right atrial shock electrode coil and a separate decapolar defibrillation catheter in the coronary sinus. For the 3-lead system, an additional decapolar catheter was placed in the right atrium. AF and sinus rhythm signal amplitude detection and atrial defibrillation threshold (ADFT) were compared in each patient with both systems. Results Successful defibrillation was obtained in all patients. ADFT for the 2-lead system was significantly higher compared with the 3-lead system (370 ± 112 vs 316 ± 100 V, P <.05; 9.3 ± 5.2 vs 6.8 ± 4.2 J, P <.05). In contrast, there was an increase in impedance for the 3-lead system (77 ± 16 Ω vs 68 ± 13 ω; P <.05). AF had a lower signal amplitude compared with sinus rhythm for both systems (P <.05), and the 2-lead system had a lower signal amplitude compared with the 3-lead system for both rhythms (P <.05). Conclusion The use of a 2-lead system with this configuration is not superior to the 3-lead system regarding AF signal amplitude detection and ADFT. Further study is needed with implantable-quality leads in place of the temporary catheters used in this study. (Am Heart J 2000;140:e11.)

P30 Microembolic signals in chronic atrial fibrillation: lack of correlation with cerebrovascular ischemic events and anticoagulation

P30 Microembolic signals in chronic atrial fibrillation: lack of correlation with cerebrovascular ischemic events and anticoagulation

A prospective study of asymptomatic embolic signals in atrial fibrillation M. Cullinane, R. Wainwright, H. Markus. King's College School of Medicine and Dentistry, London, UK

A prospective study of asymptomatic embolic signals in atrial fibrillation M. Cullinane, R. Wainwright, H. Markus. King's College School of Medicine and Dentistry, London, UK

A prospective study of asymptomatic embolic signals in atrial fibrillation: M. Cullinane, R. Wainwright, H. Markus. King's College School of Medicine and Dentistry, London, UK

A prospective study of asymptomatic embolic signals in atrial fibrillation: M. Cullinane, R. Wainwright, H. Markus. King's College School of Medicine and Dentistry, London, UK