Descriptors of wavefront propagation

Dynamic time warping applied to estimate atrial fibrillation temporal organization from the surface electrocardiogram

Atrial fibrillation (AF) is the most commonly diagnosed arrhythmia in clinical practice. However, the mechanisms responsible for its induction and maintenance still are not fully understood. To this respect, analysis of the electrical activity organization within the atria could play an important role in their proper interpretation. Although many algorithms to quantify AF organization from invasive electrograms can be found in the literature, a reduced number of indirect estimators from the standard ECG have been proposed to date. Furthermore, these surface methods can only yield a global AF organization assessment, blurring the possible information that each individual fibrillatory (f) wave may provide. To this respect, the present manuscript proposes a novel method for direct and short-time AF organization estimation from single-lead surface ECG recordings. Through the computation of morphological variations among f waves, the temporal arrhythmia organization is estimated. The f waves are individually extracted and delineated from the atrial activity signal, making use of a dynamic time warping approach. The proposed algorithm was tested on real AF surface recordings in order to discriminate atrial signals with different organization degrees, obtaining a diagnostic accuracy higher than 88%. In addition, its performance was validated by comparison with two temporal organization measures from invasive unipolar electrograms of both atria, providing statistically significant linear correlations between invasive and non-invasive estimates. As a consequence, new standpoints are opened through this work in the non-invasive analysis of AF, where the individualized study of each f wave could assess short-time AF organization, would improve the understanding of AF mechanisms and become useful for its clinical treatment.

An algorithm to measure beat-to-beat cycle lengths for assessment of atrial electrogram rate and regularity during atrial fibrillation

INTRODUCTION

Experimental models have demonstrated that atrial fibrillation (AF) may be due to one or more rapid drivers (source) producing AF. These drivers may be characterized by rapid and regular cycle lengths (CLs), producing fibrillatory conduction to the rest of the atria. The ability to reliably identify such drivers would be invaluable. The purpose of this study was to develop and validate a CL variability detection (CLVD) analysis capable of accurately determining beat-to-beat CLs of atrial electrograms (AEGs) during AF, and then to compare this analysis with dominant frequency (DF) analysis.

METHODS AND RESULTS

We analyzed 6 episodes of AF in 6 dogs (sterile pericarditis model) due either to a single, stable left atrial reentrant circuit, or unstable reentrant circuits causing fibrillatory conduction to the rest of the atria. During AF, AEGs were recorded simultaneously from 400 to 420 electrodes on both atria. CLs from over 20,000 AEGs were manually measured, and compared to CLs detected using both the CLVD and DF analyses. There was significant correlation between (1) CLs measured manually and the CLVD analysis (mean CL: correlation coefficient [CC]= 0.96, standard deviation [SD]: CC = 0.89); and (2) mean CL measured manually and the DF analysis (CC = 0.84). However, there was poor correlation between SD of CLs measured manually and the organization index (OI) by DF analysis (CC =-0.59).

CONCLUSION

The CLVD analysis was validated as being accurate for detecting both rate and degree of regularity of AEGs during AF, and more accurate than DF analysis.

Application of Wavelet Entropy to predict atrial fibrillation progression from the surface ECG

Atrial fibrillation (AF) is the most common supraventricular arrhythmia in clinical practice, thus, being the subject of intensive research both in medicine and engineering. Wavelet Entropy (WE) is a measure of the disorder degree of a specific phenomena in both time and frequency domains, allowing to reveal underlying dynamical processes out of sight for other methods. The present work introduces two different WE applications to the electrocardiogram (ECG) of patients in AF. The first application predicts the spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the electrical cardioversion (ECV) outcome in persistent AF patients. In both applications, WE was used with the objective of assessing the atrial fibrillatory (f) waves organization. Structural changes into the f waves reflect the atrial activity organization variation, and this fact can be used to predict AF progression. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity, and accuracy were 95.38%, 91.67%, and 93.60%, respectively. On the other hand, for ECV outcome prediction, 85.24% sensitivity, 81.82% specificity, and 84.05% accuracy were obtained. These results turn WE as the highest single predictor of spontaneous PAF termination and ECV outcome, thus being a promising tool to characterize non-invasive AF signals.

Central tendency measure and wavelet transform combined in the non-invasive analysis of atrial fibrillation recordings

Background

Atrial fibrillation (AF) is the most common supraventricular arrhythmia in the clinical practice, being the subject of intensive research.

Methods

The present work introduces two different Wavelet Transform (WT) applications to electrocardiogram (ECG) recordings of patients in AF. The first one predicts spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the prediction of electrical cardioversion (ECV) outcome in persistent AF patients. In both cases, the central tendency measure (CTM) from the first differences scatter plot was applied to the AF wavelet decomposition. In this way, the wavelet coefficients vector CTM associated to the AF frequency scale was used to assess how atrial fibrillatory (f) waves variability can be related to AF events.

Results

Structural changes into the f waves can be assessed by combining WT and CTM to reflect atrial activity organization variation. This fact can be used to predict organization-related events in AF. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity and accuracy were 100%, 91.67% and 96%, respectively. On the other hand, for ECV outcome prediction, 82.93% sensitivity, 90.91% specificity and 85.71% accuracy were obtained. Hence, CTM has reached the highest diagnostic ability as a single predictor published to date.

Conclusions

Results suggest that CTM can be considered as a promising tool to characterize non-invasive AF signals. In this sense, therapeutic interventions for the treatment of paroxysmal and persistent AF patients could be improved, thus, avoiding useless procedures and minimizing risks.

Non-invasive atrial fibrillation organization follow-up under successive attempts of electrical cardioversion

The development of non-invasive tools able to provide valuable information about the effectiveness of a shock in external electrical cardioversion (ECV) is clinically relevant to enhance these protocols in the treatment of atrial fibrillation (AF). The present contribution analyzes the ability of a non-linear regularity index, such as sample entropy (SampEn), to follow-up non-invasively AF organization under successive attempts of ECV and to predict the effectiveness of every single shock. To this respect, the atrial activity (AA) preceding each delivered shock was extracted by using a QRST cancellation method. Next, the main atrial wave (MAW), which can be considered as the fundamental waveform associated to the AA, was obtained by applying a selective filtering centered on the dominant atrial frequency (DAF). Finally, the MAW organization was estimated with SampEn and two thresholds (Th1 = 0.1223 and Th2 = 0.0832) were established to predict the ECV outcome. Results indicated that, prior to the first attempt, all the patients who needed only one shock to restore NSR were below Th1. In addition, most of them were above Th2 in case of AF relapsing during the first month. Regarding several shocks, all the patients who maintained NSR more than one month were below Th2 after the first shock. Moreover, all the patients who relapsed to AF during the first month were between Th1 and Th2 and, finally, all the patients with ineffective ECV were above Th1. After each unsuccessful shock, a SampEn relative decrease was observed for the patients who finally reverted to NSR, but the largest variation took place after the first attempt, thus indicating that this shock plays the most important role in the procedure. Indeed, by considering jointly the patients who needed only one shock and the patients who needed several shocks, 91.67% (22 out of 24) of ECVs resulting in NSR, 93.55% (29 out of 31) of ECVs relapsing to AF during the first month and 100% (10 out of 10) of ECVs in which NSR was not restored were correctly classified. As conclusion, the MAW organization analysis via SampEn can provide useful information that could improve the effectiveness of conventional external ECV protocols used in AF treatment.

Application of frequency and sample entropy to discriminate long-term recordings of paroxysmal and persistent atrial fibrillation

Atrial fibrillation (AF) is the most common arrhythmia in clinical practice. At an early stage of the disease, AF may terminate spontaneously and is then referred to as paroxysmal AF. On the other hand, when external intervention is required for the arrhythmia to terminate, it is referred to as persistent AF. In this work, a method to discriminate between paroxysmal and persistent AF in the long-term ECGs is presented. The dominant frequency as well as the organization of the atrial activity are employed to characterize AF. The dominant atrial frequency (DAF) is estimated using hidden Markov model based frequency tracking, and organization is estimated by the sample entropy of the main atrial wave (MAW) and the first two harmonics, respectively. Long-term variations in DAF and organization from 50 ECG recordings were evaluated, showing that episodes of paroxysmal AF were consistently associated with lower DAF and organization of the MAW and the harmonics, than was persistent AF. Discrimination of paroxysmal and persistent AF resulted in classification rates of 84.1±26.1%, thus suggesting that it possible to discriminate between paroxysmal and persistent AF in patients without previously known AF history.

Enhancement of atrial fibrillation electrical cardioversion procedures through the arrhythmia organization estimation from the ECG

The development of non-invasive tools able to provide valuable information about the effectiveness of a shock in external electrical cardioversion (ECV) is clinically relevant to enhance these protocols in the treatment of atrial fibrillation (AF). The present contribution analyzes the ability of a non-linear regularity index, such as sample entropy (SampEn), to follow-up noninvasively AF organization under successive attempts of ECV and to predict the effectiveness of every single shock. Results showed that, after each unsuccessful shock, a SampEn relative decrease was observed for the patients who finally reverted to normal sinus rhythm (NSR), but the largest variation took place after the first attempt, thus indicating that this shock plays the most important role in the procedure. Indeed, by considering jointly the patients who needed only one shock and the patients who needed several shocks, 91.67% (22 out of 24) of ECVs resulting in NSR, 93.55% (29 out of 31) of ECVs relapsing to AF during the first month and 100% (10 out of 10) of ECVs in which NSR was not restored were correctly classified. As conclusion, AF organization analysis via SampEn from the surface ECG can provide useful information that could improve the effectiveness of conventional external ECV protocols used in AF treatment.

Use of frequency analysis on the ECG for the prognosis of low energy cardioversion treatment of atrial fibrillation

Electric cardioversion is the most effective therapy for restoring sinus rhythm in patient with atrial fibrillation (AF), however, there is not a guiding criteria for advising on when and in whom it will be successful. The objective of this study was to employ frequency analysis on the surface electrocardiogram (ECG) to predict the outcome of low energy internal cardioversion in patients with AF. Thirty nine patients with AF, for elective DC cardioversion were included in this study. One catheter was positioned in the right atrial appendage and another in the coronary sinus. A voltage step-up protocol (50-300 V) was used for patient cardioversion. Prior to shock delivery, residual atrial activity signal (RAAS) was derived from 60 seconds of surface ECG from defibrillator pads, by bandpass filtering and ventricular activity (QRST) cancellation. Dominant atrial fibrillatory frequency (DAFF) was estimated from the RAAS power spectrum as the dominant frequency within the 3-12 Hz band. DAFF was calculated from whole 60 seconds segment (DAFF_L) and from the finals 10 seconds segment (DAFF_S) of the RAAS. Lower DAFF_L and DAFF_S were found in successfully cardioverted patients than in those nonsuccessful ones, with energy < or =3 and < or =6 joules. Therapy result (employing 3J or less) was predicted in 35/39 (89.7%) patients with DAFF_L=5.40Hz, and DAFF_L was > or =5.75Hz in a 100% of noncardioverted patients. In conclusion, frequency analysis of the RAAS could be useful for predicting success of low energy internal cardioversion of patients with atrial fibrillation.

Non-invasive organization variation assessment in the onset and termination of paroxysmal atrial fibrillation

Atrial Fibrillation (AF) is the most common supraventricular tachyarrhythmia. Recently, it has been suggested that AF is partially organized on its onset and termination, thus being more suitable for antiarrhythmia and to avoid unnecessary therapy. Although several invasive and non-invasive AF organization estimators have been proposed, the organization time course in the first and last minutes of AF has not been quantified yet. The aim of this work is to study non-invasively the organization variation within the first and last minutes of paroxysmal AF. The organization was evaluated making use of sample entropy, which can robustly estimate electrical atrial activity organization from surface ECG recordings. This work proves an organization decrease in the first minutes of AF onset and an increase within the last minute before spontaneous AF termination. These results are in agreement with the conclusions reported by other authors who made use of invasive recordings.