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Atrial fibrillation (AF) is a common supraventricular arrhythmia with episodes that, in the first stages of the disease, may terminate spontaneously. This fact is referred as paroxysmal atrial fibrillation. The analysis of its termination or maintenance could avoid unnecessary therapy and contribute to take the appropriate decisions on its management. The aim of this work is to study if an AF episode terminates spontaneously or not by analyzing the increase of atrial activity (AA) organization prior to AF termination. The organization varies as a consequence of the decrease in the number of reentries into the atrial tissue. The analysis was carried out noninvasively through the use of surface electrocardiogram (ECG) recordings. Sample entropy was selected as non-linear organization index. It was observed that noise and ventricular residues degrade AA organization estimation performance, therefore the use of selective filtering to get the main atrial wave (MAW) was necessary. Using the MAW organization analysis, that is the signal produced by the main reentry wandering the atrial tissue, 46 out of 50 of the terminating and non-terminating analyzed AF episodes were correctly classified (92%). The obtained outcomes allow to conclude that the dominant atrial frequency, and therefore, the main atrial reentry, contains the most relevant information about spontaneous AF termination.

Detection of atrial activity (AA) is quite important in the study and monitoring of supraventricular arrhythmias. This study shows the possibility of AA extraction from atrial fibrillation (AF) episodes in Holter registers using only two leads with a new technique, the Wavelet Domain in Blind Source Separation (WDBSS). Our principal aim is to join a processing stage with Blind Source Separation (BSS) with methodologies based on wavelet transform. A first stage with Discrete Wavelet Transform (DWT) increases the spectral information, decomposing the considered signal in a set of coefficients with different temporal and spectral features. A second stage with BSS uses this information to extract the AA. The obtained improvements are the increase of spectral concentration (in the band of 5-8 Hz) and the lack of residual complexes. In WDBSS, the use of several leads from the ECG is needless, which could be applied for to the detection of different arrhythmias in Holter registers, where the number of leads is reduced, like the paroxysmal atrial fibrillation.

Methods: The materials correspond to 14 synthetic recordings and 40 real recordings from the Clinical University Hospital, Valencia, Spain. The proposed method (frequency domain algorithm, or FDA) maximizes the power content in the range of frequencies of 3 to 10 Hz for the first uncorrelated recordings to assure the decoupling between the atrial and ventricular activities. The central frequency of this range of frequencies corresponds to an initial estimation of the atrial frequency from the T-Q intervals of lead V1. Fig. 1 shows the corresponding block diagram. The performance measures used to assess the quality are correlation (ρ), spectral concentration (SC), and kurtosis. In the case of real recordings, we also estimate the peak frequency of the extracted atrial activity. To compare the results, Fast ICA and PCA were also applied to the same recordings. Results: We have analyzed the performance of the 3 algorithms with synthetic and real recordings. The results are shown in the Table 1 (statistical analysis in the last line). The new method outperforms the classical blind source separation methods applied to the estimation of atrial activity, Fast ICA and PCA. We have also analyzed the behavior of the methods under realistic noise conditions. In this case, in terms of correlation, SC, and kurtosis, FDA is specially robust compared with other algorithms. In the last experiment, we have checked that FDA does not require an accurate prior estimation of the initial atrial rate to extract the atrial activity successfully. Conclusion: The proposed method presents the following advantages: simplicity and efficiency in computational time, because it is a fixed-point algorithm and a source extraction method; that is, it does not require the computation of the whole set of sources and the postprocessing identification of the atrial component among them. It has been applied successfully to nonnoisy and noisy simulated ECG and to real ECG, obtaining better results than other algorithms in terms of the proposed quality parameters. These results show that (i) the use of higher order statistics is not necessary to solve the problem (Fast ICA) and (ii) the use of decorrelation can be enhanced (PCA). Finally, the algorithm is robust to deviations in the initial estimation of the central frequency.

Nonlinear analysis has emerged as a novel methodology for the processing of physiological time series over the past few decades because biological systems and processes are inherently complex, nonlinear, and non-stationary. In this sense, cardiac cells and the time course of atrial electrophysiological properties have shown a far-from-linear behavior during atrial fibrillation (AF). This work reviews the main approaches of nonlinear analysis (chaos theory, information content quantification, irregularity measures, and geometric features) in the study of AF. The application of these indices both to surface electrocardiographic recordings and invasive atrial electrograms reveals useful clinical information in a complementary way to traditional linear techniques, such as spectral analysis. Indeed, atrial activity signal analysis through nonlinear indices provides more accurate estimates of the temporal, spatial, and spatio-temporal organization of AF. Given that AF organization has been associated with the number of simultaneous wavelets wandering throughout the atrial tissue during arrhythmia, its robust estimation can help improve treatments and allow more appropriate decisions on AF management.

Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice. One frequent and very effective alternative to revert AF back to a normal sinus rhythm (NSR) is external electrical cardioversion (ECV). However, conventional ECV protocols do not consider effects of electrical shocks on the atrial activity (AA) and its evolution during the procedure. The present study analyzes the feasibility of using the fibrillatory wave amplitude (fWA) and the dominant atrial frequency (DAF), extracted from the AA, to noninvasively detect AF recurrence under successive attempts of ECV and to predict the effectiveness of each shock in the ECV protocols. Results show that the effects of the first ineffective shock are only notably found in the fWA. The percentage of patients who needed several attempts who were correctly classified into patients who relapsed to AF, maintained NSR, and had an ineffective procedure was 64.3% for the fWA analyzed before the first shock, and increased to 89.3% for the same analysis after the first shock. Additionally, by jointly considering the patients who needed only one and several shocks, the discriminant ability increased when the values after the first ineffective attempt were used for the prediction. For the fWA, the diagnostic accuracy increased from 79.4% to 85.7%, for the DAF, from 65.1% to 71.4%, and for their combination, from 82.5% to 87.3%. The effect of the first unsuccessful shock is crucial for determining whether NSR will be restored through successive cardioversion attempts and predicting AF recurrence in patients undergoing ECV protocols. fWA and DAF can thus provide valuable information to improve the effectiveness of conventional ECV protocols, in which the effect of unsuccessful shocks is not taken into account at present.

This study shows the possibility of atrial activity (AA) extraction from atrial fibrillation (AF) episodes in Holter registers using only two leads (V1 and V5) with a new technique, the Wavelet Blind Separation (WBS). The WBS increases the observed mixtures of the original signal from the decomposition of each lead into six transformed signals using a wavelet transform. These mixtures provide enough useful information to a Blind Source Separation implemented system and the extraction can be achieved. To evaluate the suggested algorithm, artificial AF signals have been synthesized adding fibrillation activity to normal sinus rhythm. Results indicate that the WBS technique can be an important tool to perform the atrial extraction in short duration registers with a reduced number of leads like paroxysmal atrial fibrillation, which have to be usually detected from Holter systems.

[EN] Atrial Fibrillation (AF) is the most commonly sustained cardiac arrhythmia and the major cause of cardiovascular morbidity and mortality. Because of its wide availability and initial effectiveness, electrical cardioversion (ECV) is the primary method used for reverting this arrhythmia to normal sinus rhythm (NSR). However, this procedure presents some collateral effects, and barely 80% of the patients prevail in NSR after 1 month. Thus, being able to predict the outcome of ECV before its application is of great interest in clinical practice, so cardiac complications in patients with high probability of early AF recurrence could be prevented. For that purpose, this work characterizes atrial activity (AA) in patients with persistent AF, before ECV, by means of nonlinear multiscale dynamics, particularly composite multiscale entropy (CMSE), and compares its performance with other recently used parameters, such as, dominant atrial frequency, AA¿s amplitude and sample entropy. The results show that characterizing AA by means of CMSE predicts ECV outcome with an accuracy above 90%, whereas the remaining parameters only forecast correctly about 70% of the analyzed patients. As a conclusion, using complexity techniques at different time scales for AA characterization increases the probability of correctly predicting AF recurrence., This work has been funded by projects: DPI2007-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501000411 from Junta de Castilla la Mancha and AICO/2019/036 from Generalitat Valenciana.

Atrial tachyarrhythmias are a very common cardiovascular disease in clinical practice with an incidence that doubles with each advancing decade. A key issue to understand their pathophysiological mechanisms is the analysis and interpretation of atrial electrograms (AEG). To properly study these signals, ventricular artifacts have to be removed from the AEG. In this work, a new application of independent component analysis (ICA) to the AEG is presented where ventricular artifacts are removed from atrial recordings making use of only one reference lead. Therefore the technique is suitable when multi-lead recordings are unavailable as in atrial implantable cardioverter-defibrilators. The methodology has been compared with traditional techniques on a database of 20 patients. Performance was evaluated through atrial waveform similarity (S) and ventricular activity reduction (V D R) as a function of atrial rhythm regularity on a beat-by-beat basis. When the atrial tachyarrhythmia is quite regular, results show that ICA preserves the atrial waveform better than the other methods (median S = 99:64%) whereas maintaining ventricular reduction (median VDR = 6:32dB).

Atrial fibrillation (AF) is a common supraventricular arrhythmia with episodes that, in the first stages of the disease, may terminate spontaneously. This fact is referred as paroxysmal atrial fibrillation. The analysis of its termination or maintenance could avoid unnecessary therapy and contribute to take the appropriate decisions on its management. The aim of this work is to study if an AF episode terminates spontaneously or not by analyzing the increase of atrial activity (AA) organization prior to AF termination. The organization varies as a consequence of the decrease in the number of reentries into the atrial tissue. The analysis was carried out noninvasively through the use of surface electrocardiogram (ECG) recordings. Sample entropy was selected as non-linear organization index. It was observed that noise and ventricular residues degrade AA organization estimation performance, therefore the use of selective filtering to get the main atrial wave (MAW) was necessary. Using the MAW organization analysis, that is the signal produced by the main reentry wandering the atrial tissue, 46 out of 50 of the terminating and non-terminating analyzed AF episodes were correctly classified (92%). The obtained outcomes allow to conclude that the dominant atrial frequency, and therefore, the main atrial reentry, contains the most relevant information about spontaneous AF termination.

The present work introduces a new ECG delineator, based on the Phasor Transform, which is characterized by its robustness, low computational cost and mathematical simplicity. The method converts each instantaneous ECG sample into a phasor, thus being able to deal very precisely with P and T waves, which are of notably lower amplitude than the QRS complex. Initially, the method relies on the detection of R peaks and, next, onset and offset of the QRS complex are identified. Finally, taking the QRS as a reference, P and T waves are detected and delineated. The new delineator has been validated with the QT database, providing average values of sensitivity higher than 98.60% for the detection of all the significant ECG waves and fiducial points. Additionally, the average maximum time delineation error was lower than 6 ms and its standard deviation was in agreement with the accepted tolerances for expert physicians in the onset and offset identification for QRS, P and T waves. As a consequence, this new algorithm is able to achieve similar performance to the one provided by other well known delineation algorithms, but with notably lower computational cost.

Sample Entropy (SampEn) is a nonlinear regularity index that requires the a priori selection of three parameters: the length of the sequences to be compared, m, the patterns similarity tolerance, r, and the number of samples under analysis, N. Appropriate values for m, r and N have been recommended in some cases, such as heart rate, hormonal data, etc., but no guidelines exist for the selection of that values. Hence, an optimal parameters study should be required for the application of SampEn to not previously analyzed biomedical signals. In this work, a thorough analysis on the optimal SampEn parameter values within two different scenarios of AF organization estimation, such as the prediction of paroxysmal AF termination and the electrical cardioversion outcome in persistent AF, is presented. Results indicated that, (i) the proportion between N and the sampling rate (ƒ(s)) should be higher than one second and ƒ(s) ≥ 256 Hz, (ii) overlapping between adjacent N-length windows does not improve organization estimation and (iii) values of m and r maximizing classification should be considered within a range wider than the proposed in the literature for heart rate analysis, i. e. m = 1 and m = 2 and r between 0.1 and 0.25 times the standard deviation of the data.

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., This work was supported by the projects TEC2010-20633 from the Spanish Ministry of Science and Innovation and PPII11-0194-8121 and PII1C09-0036-3237 from Junta de Comunidades de Castilla-La Mancha.