Search

[EN] Despite its limited long-term success rate, catheter ablation (CA) is still the first-line treatment for patients suffering from persistent atrial fibrillation (AF). Preoperative prediction on the procedure¿s outcome is gaining clinical interest to provide optimized patient selection, limit repeated procedures, hospitalization rates, and treatment costs. To this respect, dominant frequency (DF) and amplitude of fibrillatory waves (f-waves) reflected on the electrocardiogram (ECG) recording have provided promising results. Hence, this work explores the ability of a novel set of frequency and amplitude features of the f-waves to improve CA outcome prediction. More precisely, in addition to DF and normalized f-wave amplitude (NFWA), three indices such as spectral entropy (SE), spectral flatness measure (SFM), and amplitude spectrum area (AMSA) were also analyzed. The results obtained for 204 6 s-length ECG intervals extracted from 51 persistent AF patients showed that all indices found statistically significant differences between the patients who maintained sinus rhythm and those who relapsed to AF after a follow-up of 9 months. However, they only obtained limited a discriminant ability ranging between 55 and 62%, which was overcome by 15¿23% when NFWA, SE and AMSA were combined. Consequently, this combination of frequency and amplitude features of the f-waves seems to provide new insights about the atrial substrate remodeling, which could be helpful in improving preoperative CA outcome prediction. Nonetheless, further studies are required in the future, This research has been supported by grants DPI2007-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501000411 from Junta de Comunidades de Castilla-la Mancha and AICO/2019/036 from Generalitat Valenciana.

[EN] Pulmonary vein isolation is nowadays the cornerstone for the treatment of paroxysmal atrial fibrillation (AF). However, its success rate is not as high as clinically desirable, and repeated procedures are often required. In this context, the development of noninvasive, pre-ablation predictors of the procedure failure is essential to enable tailored approaches. For that purpose several P-wave features have been analyzed from short electrocardiogram (ECG) recordings, but their time variability has not been studied yet. Hence, this work explores the ability of time variability of two common P-wave parameters, such as duration and amplitude, to anticipate cryoablation failure. Before ablation, a standard 12-lead ECG was recorded continuously for 5 minutes from 45 paroxysmal AF patients. All P-waves in lead II were delineated with an automatic algorithm. Duration and amplitude of all waves were computed, and then mean, standard deviation (std), and coefficient of variation (CV) were obtained for both parameters and correlated with ablation outcome after a follow-up of 9 months. The CV for both P-waves features reported the best separation between the patients who maintained sinus rhythm and those who relapsed to AF. Moreover, improvements about 10% in diagnostic ability were obtained by the CV with regards to the mean, reaching values about 70%. These results suggest that time variability of the P-wave can provide new insights about atrial conduction heterogeneity, which could be helpful in improving the selection strategy of optimal candidates for cryoablation., This research has been supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-la Mancha and AICO/2019/036 from Generalitat Valenciana.

[EN] In the management of atrial fibrillation (AF), electrical cardioversion (ECV) is a common treatment. Although its initial success rate is high, many patients present AF recurrence after some weeks or months. Hence, being able to identify patients at low chance of mid-term sinus rhythm maintenance is important for a rationale therapeutic strategy. To this end, several parameters assessing fibrillatory (f-) waves have been introduced, however, with limited predictive ability. Moreover, the cardiovascular system exhibits nonlinear dynamics at different time-scales that these indices do not account for. Hence, the present work evaluates the ability of the multiscale entropy (MSE) analysis of the f-waves to improve preoperative forecasts of ECV outcome. Both traditional MSE and a refined version (RMSE) were applied to the main f waves component obtained for standard lead V1. As a reference, previously proposed predictors were also computed. Results revealed that RMSE was able to anticipate AF recurrence after 1 month of ECV with an accuracy around 78%. Moreover, a Naive Bayes model combining previous parameters and RMSE indices reported a discriminant ability 10% higher than single metrics. It could then be concluded that analysis of nonlinear dynamics at large time-scales can enhance ECV outcome predictions., This research was funded by projects: DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501000411 from Junta de Castilla la Mancha and AICO/2019/036 from Generalitat Valenciana.

[EN] It has been shown that substrate modification due to catheter ablation (CA), the first-line treatment of paroxysmal atrial fibrillation (PAF), provokes alterations in P-wave features of the surface electrocardiogram. The absence of knowledge on whether invasive recordings acquired during CA can track similar modifications, creates nevertheless a significant void in the thorough understanding of CA effect on PAF. By analyzing 5-minute bipolar coronary sinus (CS) recordings from 26 PAF patients in sinus rhythm (SR) undergoing CA, this study aims to shed light on whether CA affects local activation waves (LAWs) features recorded in the CS, which is a widely used reference point to assess AF substrate. Recordings were obtained before and after CA. The analysis consisted of LAWs detection and processing of features such as duration, amplitude, positive and negative area, morphology variability (MV), dispersion and number of deflections and inflections (NODI). These features were defined similarly as with previous studies made with surface P-waves. Statistical analysis contained median calculation and comparison with Mann-Whitney U-test. Although some variations in line with previously reported results on P-wave analysis have been observed, none of them was statistically significant (p>0.05). Duration, amplitude, positive and negative area marked a minor decrease (- 1.4% to - 14.3%). MV and dispersion showed a minor to moderate increase + 64.8%). NODI showed no alteration. CA in PAF does not lead to significant variations on LAWs features of bipolar CS recordings. The alterations reported by previous studies on the P-waves may be the product of substrate modification of other atrial regions or the superposition of less significant alterations occurring at each atrial site., Research supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2019/036 from GVA.

[EN] Aimed at characterizing the atrial substrate in atrial fibrillation (AF), a wide number of indexes has been investigated on complex fractionated atrial electrograms (CFAEs). However, many times the assessment of CFAEs signal quality as well as intra-recording and intra-patient stability of CFAEs is missing. The present work introduces the application of nonlinear metrics to assess intra-recording and intra-patient stability on a total of 204 AF bipolar CFAEs, of 16 seconds in length from patients with paroxysmal ( n=15) and persistent ( n=19) AF. To estimate differences between intra-recording and intra-patient CFAEs, the coefficient of variation (CV) has been used on Sample Entropy (SE) and on the Determinism (DET) of Recurrence Quantification Analysis (RQA) of the data. In addition, the variations introduced by discarding artifacted and noisy CFAE segments were also analyzed. Intra-recording analysis indicated a significant variation of CV(%) in any segment length both for DET and SE when discarding segments, with larger decreases for longer segments. Large variations of CV(%) for DET and SE at any segment length were observed in the intra-patient analysis but, in this case, discarding segments did not improve results. The Kruskal-Wallis test reported statistically significant differences for DET and SE among channels, regardless of the discarding process. Therefore, the observed high variability of CFAEs has proved that averaging data in one channel or among different channels may lead to an unfair oversimplification of CFAE-based atrial substrate characterization., Research supported by grants DPI2017¿83952¿C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2019/036 from GVA.

[EN] The European Society of Cardiology guidelines recommend electrical cardioversion (ECV) as a rhythm control strategy in persistent atrial fibrillation (AF). Although being able to initially restore sinus rhythm in most patients, mid- and long-term AF recurrence rates are high. In this context, anticipation of ECV outcome is interesting to rationalize the management of AF patients. To this end, several parameters have been recently proposed for atrial activity (AA) characterization, such as fibrillatory wave amplitude (FWA), dominant frequency (DF) and sample entropy (SEn). These indices have revealed promising results, but have been mainly computed from lead V1, thus discarding spatial information from the remaining leads. Hence, this work explores whether a multidimensional extension of these parameters can improve ECV outcome prediction. Results showed that multidimensional parameters provided more balanced values of sensitivity and specificity than unidimensional ones. While FWA and DF showed similar discriminant ability among both approaches, multivariate SEn improved the discriminant ability of its univariate version by 5%, thus predicting 80% of the ECV procedures correctly. Consequently, whereas multivariate extension of linear parameters did not reveal new predictive information, multidimensional entropy analysis was able to quantify novel AA dynamics, which have been helpful in improving ECV outcome prediction., This research was funded by projects DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from "Junta de Castilla la Mancha" and AICO/2019/036 from "Generalitat Valenciana".

[EN] Although catheter ablation (CA) is still the first-line treatment for persistent atrial fibrillation (AF) patients, its limited long-term success rate has motivated clinical interest in preoperative prediction on the procedure¿s outcome to provide optimized patient selection, limit repeated procedures, hospitalization rates, and treatment costs. To this respect, dominant frequency (DF) and amplitude of fibrillatory waves (f-waves) reflected on the ECG have provided promising results. Hence this work explores the ability of a novel set of frequency and amplitud f-waves features, such as spectral entropy (SE), spectral flatness measure (SFM), and amplitud spectrum area (AMSA), along with DF and normalized f-wave amplitude (NFWA), to improve CA outcome prediction. Despite all single indices reported statistically significant differences between patients who relapsed to AF and those who maintained sinus rhythm after a follow-up of 9 months for 204 6 s-length ECG intervals extracted from 51 persistent AF patients, they obtained a limited discriminant ability ranging between 55 and 62%, which was overcome by 15¿23% when NFWA, SE and AMSA were combined. Consequently, this combination of frequency and amplitude features of the fwaves seems to provide new insights about the atrial substrate remodeling, which could be helpful in improving preoperative CA outcome prediction., This research has been supported by grants DPI201783952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-la Mancha and AICO/2019/036 from Generalitat Valenciana. Moreover, Pilar Escribano holds a graduate research scholarship from University of Castilla-La Mancha

[EN] Electrical cardioversion (ECV) is a well-established strategy for atrial fibrillation (AF) management. Despite its high initial effectiveness, a high relapsing rate is also found. Hence, identification of patients at high risk of early AF recurrence is crucial for a rationale therapeutic strategy. For that purpose, a set of indices characterizing fibrillatory (f-) waves have been proposed, but they have not considered nonlinear dynamics present at different timescales within the cardiovascular system. This work thus explores whether a multiscale entropy (MSE) analysis of the f-waves can improve preoperative predictions of ECV outcome. Thus, two MSE approaches were considered, i.e., traditional MSE and a refined version (RMSE). Both algorithms were applied to the main f-waves component extracted from lead V1 and entropy values were computed for the first 20 time-scales. As a reference, dominant frequency (DF) and f-wave amplitude (FWA) were also computed. A total of 70 patients were analyzed, and all parameters but FWA showed statistically significant differences between those relapsing to AF and maintaining sinus rhythm during a follow-up of 4 weeks. RMSE reported the best results for the scale 19, improving predictive ability up to an 8% with respect to DAF and FWA. Consequently, investigation of nonlinear dynamics at large time-scales can provide useful insights able to improve predictions of ECV failure, This research was funded by the projects DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from "Junta de Castilla La Mancha" and AICO/2019/036 from "Generalitat Valenciana".

[EN] Electrical cardioversion (ECV) is an effective and low-cost rhythm control strategy for persistent atrial fibrillation (AF). Because of its limited mid- and long-term success rates, prediction of early failure could avoid patients with reduced chance to maintain sinus rhythm (SR). To this end and due to its proximity to the right atrium, several indices characterizing atrial activity have been proposed based on lead V1. However, information from other leads has been discarded to date. Hence, this work studies how effective some common indices computed over the whole set of 12 standard ECG leads are in predicting ECV outcome. Precisely, amplitude, dominant frequency, and sample entropy were computed from the fibrillatory (f-) waves extracted for each one of 12 standard leads acquired before ECV from 58 patients in persistent AF. The classification between the patients who relapsed to AF and maintained sinus rhythm after a follow-up of 4 weeks achieved by these parameters was better from limb lead II than from V1, thus reporting improvements about 6 and 12%. As a consequence, characterization of f-waves from the more accessible limb lead II has proven to be the best choice to improve AF ECV outcome prediction from the ECG., This research was funded by the projects DPI2017-83952C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from "Junta de Castilla La Mancha" and AICO/2019/036 from "Generalitat Valenciana".

[EN] A variety of indexes has been applied to complex fractionated atrial electrograms (CFAEs) of atrial fibrillation (AF) aimed at characterizing the atrial substrate. However, often the reported results miss the assessment of intra-recording and intra-patient stability of the analyzed data, as well as CFAEs signal quality. This work introduces a study in which Determinism (DET) of Recurrence Quantification Analysis (RQA) and Sample Entropy (SE) have been applied to assess intra-recording and intrapatient stability of 1, 2 and 4 s-length segments CFAEs recorded from patients with paroxysmal and persistent AF using the coefficient of variation (CV). Furthermore, the analyses verified changes introduced by discarding artifacted and noisy CFAE segments. The intra-recording analysis pointed out that discarding segments provoked a significant variation of CV(%) in any segment length both for DET and SE, with deeper decreases for longer segments. Intra-patient stability provided large variations in CV(%) for DET and even bigger for SE at any segment length. In this case discarding segments was useless and CV provided limited variations. Kruskal-Wallis test revealed significant differences in DET and SE values among channels, independently from the discarding process., Research supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2019/036 from GVA.

[EN] Analysis of coronary sinus (CS) electrograms (EGMs) is vastly used for the assessment of the atrial fibrillation (AF) substrate. As a catheter consists of five dipoles (distal, mid-distal, medial, mid-proximal, proximal), results may vary upon the employed channel: myocardial contraction and bad contact are unavoidable factors affecting the recording. This work aims to specify the most reliable channels in catching AF dynamics, using 44 multichannel bipolar CS recordings in sinus rhythm (SR) of paroxysmal AF with 1-5 minutes duration. Local activation waves (LAWs) were detected and main features obtained: duration, amplitude, area and correlation between dominant morphologies of each channel. Analysis was performed with Kruskal-Wallis test for multichannel comparison and Mann-Whitney U-test for pairs of channels and comparison between one and the remaining channels, using Bonferroni correction. Median values were calculated. Distal channel presented the highest alteration in LAWs features, being the least correlated channel (82.84 - 88.31%) with the lowest amplitude and area (p(max) < 0.01). Contrastly, medial and mid-proximal channels showed the most robust LAW characteristics, with very high correlation (94.53%) and high area and amplitude values (p(max) < 0.02 and p(max) < 0.07, respectively) and their analysis is recommended for AF substrate characterization during SR, Research supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2019/036 from GVA.

Atrial fibrillation (AF) is nowadays the most common cardiac arrhythmia, being associated with an increase in cardiovascular mortality and morbidity. When AF lasts for more than seven days, it is classified as persistent AF and external interventions are required for its termination. A well-established alternative for that purpose is electrical cardioversion (ECV). While ECV is able to initially restore sinus rhythm (SR) in more than 90% of patients, rates of AF recurrence as high as 20&ndash, 30% have been found after only a few weeks of follow-up. Hence, new methods for evaluating the proarrhythmic condition of a patient before the intervention can serve as efficient predictors about the high risk of early failure of ECV, thus facilitating optimal management of AF patients. Among the wide variety of predictors that have been proposed to date, those based on estimating organization of the fibrillatory (f-) waves from the surface electrocardiogram (ECG) have reported very promising results. However, the existing methods are based on traditional entropy measures, which only assess a single time scale and often are unable to fully characterize the dynamics generated by highly complex systems, such as the heart during AF. The present work then explores whether a multi-scale entropy (MSE) analysis of the f-waves may provide early prediction of AF recurrence after ECV. In addition to the common MSE, two improved versions have also been analyzed, composite MSE (CMSE) and refined MSE (RMSE). When analyzing 70 patients under ECV, of which 31 maintained SR and 39 relapsed to AF after a four week follow-up, the three methods provided similar performance. However, RMSE reported a slightly better discriminant ability of 86%, thus improving the other multi-scale-based outcomes by 3&ndash, 9% and other previously proposed predictors of ECV by 15&ndash, 30%. This outcome suggests that investigation of dynamics at large time scales yields novel insights about the underlying complex processes generating f-waves, which could provide individual proarrhythmic condition estimation, thus improving preoperative predictions of ECV early failure.

[EN] European Society of Cardiology guidelines recommend electrical cardioversion (ECV) as a rhythm control strategy in persistent atrial fibrillation (AF). Although ECV initially restores sinus rhythm (SR) in almost every patient, mid- and long-term AF recurrence rates are high, so that additional research is needed to anticipate ECV outcome and rationalize the management of AF patients. Although indices characterizing fibrillatory (f -) waves from surface lead V1, such as dominant frequency (DF), amplitude (FWA), and entropy, have reported good results, they discard the spatial information from the remaining leads. Hence, this work explores whether a multidimensional characterization approach of these parameters can improve ECV outcome prediction. The obtained results have shown that multidimensional FWA reported more balanced values of sensitivity and specificity, although the discriminant ability was similar in both cases. For DF, a similar outcome was also obtained. In contrast, multivariate entropy overcome discriminant ability of its univariate version by 5%, rightly anticipating result in more than 80% of ECV cases. Therefore, multidimensional entropy analysis seems to be able to quantify novel dynamics in the f-waves, which lead to a better ECV outcome prediction, This research was funded by the projects DPI2017-83952C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from "Junta de Castilla La Mancha" and AICO/2019/036 from "Generalitat Valenciana"

[EN] Nowadays, the first-line therapy for paroxysmal atrial fibrillation (PAF) is pulmonary vein isolation through catheter ablation. However, the success rate of this procedure is still not as high as desirable. Thus, preoperative prediction of early AF recurrence after ablation is a challenge to select optimal candidates for the intervention. To this end, some promising predictors based on the P-wave in short ECG signals have been proposed in the last years. However, evolution of the P-wave along the time has still not been analyzed. Hence, the present work studies how time variability of two features of the P-wave predicts midterm cryoablation failure. For 45 PAF patients, a standard 12-lead ECG signal was obtained for 5 minutes before ablation. An automatic algorithm was then used to delineate all P-waves in lead II, and duration and amplitude were computed. The resulting time series were characterized by their mean, standard deviation and coefficient of variation (CV). Correlating these measures with ablation outcome, the CV for both parameters obtained the best discrimination between patients. In fact, compared with the mean value, the CV for both features obtained accuracies 10% greater, thus achieving values of 70%. These outcomes entail that time variability of the P-wave can reveal new information about the proarrhythmic condition of the patients, thus improving predictions of ablation failure.

[EN] To revert atrial fibrillation (AF) in patients requiring concomitant open-heart surgery, surgical ablation (SA) is one of the most effective procedures. However, this intervention is as- sociated with major side effects. Considering this limitation together with the benefits of anticipating antiarrhythmic treatment decisions, preoperative prediction of long-term outcome of SA is an interesting challenge. In the present work a novel algorithm to anticipate SA outcome after a follow-up of 12 months is introduced by just analyzing one lead from a preoperative electrocardiogram (ECG). Precisely, fibrillatory waves reflected on lead V1 are extracted using a QRST cancellation method. The obtained signal is divided into 1 s-length segments, which are decomposed into 8 time scales by making use a Wavelet transform. Finally, relative wavelet energy for each segment and scale is computed and the standard deviation of the time series for the 7th scale is obtained as a measure of organization of the fibrillatory waves. This index obtained statistically significant differences between the patients who maintained sinus rhythm and relapsed to AF during the follow-up. Moreover, it also yielded a discriminant ability about 75%, thus improving between 13 and 29% the one showed by other well-established parameters., This research has been supported by grants DPI2017-83952- C3 from MINECO/AEI/FEDER EU, SBPLY/17/ 180501/000411 from Junta de Comunidades de Castilla-la Mancha, and AICO/2019/036 from Generalitat Valenciana. Moreover, Pilar Escribano holds a graduate research scholarship from University of Castilla-La Mancha.

[EN] Atrial ¿brillation (AF) is currently the most common cardiac arrhythmia, with catheter ablation (CA) of the pulmonary veins (PV) being its ¿rst line therapy. Ablation of complex fractionated atrial electrograms (CFAEs) outside the PVs has demonstrated improved long-term results, but their identi¿cation requires a reliable electrogram (EGM) fractionation estimator. This study proposes a technique aimed to assist CA procedures under real-time settings. The method has been tested on three groups of recordings: Group 1 consisted of 24 highly representative EGMs, eight of each belonging to a different AF Type. Group 2 contained the entire dataset of 119 EGMs, whereas Group 3 contained 20 pseudo-real EGMs of the special Type IV AF. Coarse-grained correlation dimension (CGCD) was computed at epochs of 1 s duration, obtaining a classi¿cation accuracy of 100% in Group 1 and 84.0¿85.7% in Group 2, using 10-fold cross-validation. The receiver operating characteristics (ROC) analysis for highly fractionated EGMs, showed 100% speci¿city and sensitivity in Group 1 and 87.5% speci¿city and 93.6% sensitivity in Group 2. In addition, 100% of the pseudo-real EGMs were correctly identi¿ed as Type IV AF. This method can consistently express the fractionation level of AF EGMs and provides better performance than previous works. Its ability to compute fractionation in short-time can agilely detect sudden changes of AF Types and could be used for mapping the atrial substrate, thus assisting CA procedures under real-time settings for atrial substrate modi¿cation., This research has been supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from JCCM and AICO/2019/036 from GVA.

[EN] Surgical ablation (SA) is the most effective procedure to terminate atrial fibrillation (AF) in patients requiring concomitant open-heart surgery. However, considering the great stress provoked in the patient¿s heart, along with the benefits of anticipating antiarrhythmic therapeutical decisions, preoperative prediction of long-term failure of the procedure is an interesting clinical challenge. Hence, the present work introduces a novel algorithm to anticipate SA outcome after one year of follow-up by just analyzing the surface ECG. The method firstly extracts fibrillatory waves reflected on standard lead V1 using an adaptive QRST cancellation approach. The resulting signal is then segmented into 1 s-length intervals and wavelet energy is computed for all of them. Finally, the coefficient of variation of the time series obtained for the 7th scale is computed. Analyzing 20 second-length preoperative ECG excerpts from 53 persistent AF patients undergoing concomitant openheart surgery, only the proposed method reported statistically significant differences between the patients who relapsed to AF and those who maintained sinus rhythm during the follow-up. The algorithm also provided values of sensitivity, specificity, and accuracy between 10 and 20% better than the well-established dominant atrial frequency and fibrillatory waves amplitude, thus suggesting to be a promising predictor of AF recurrence after SA., This research has been supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-la Mancha and AICO/2019/036 from Generalitat Valenciana. Moreover, Pilar Escribano holds a graduate research scholarship from University of Castilla-La Mancha.

[EN] Atrial fibrillation (AF) is the most common arrhythmia in clinical practice with an increasing prevalence of about 15% in the elderly. Despite other alternatives, catheter ablation is currently considered as the first-line therapy for the treatment of AF. This strategy relies on cardiac electrophysiology systems, which use intracardiac electrograms (EGM) as the basis to determine the cardiac structures contributing to sustain the arrhythmia. However, the noise-free acquisition of these recordings is impossible and they are often contaminated by different perturbations. Although suppression of nuisance signals without affecting the original EGM pattern is essential for any other later analysis, not much attention has been paid to this issue, being frequently considered as trivial. The present work introduces the first thorough study on the significant fallout that regular filtering, aimed at reducing acquisition noise, provokes on EGM pattern morphology. This approach has been compared with more refined denoising strategies. Performance has been assessed both in time and frequency by well established parameters for EGM characterization. The study comprised synthesized and real EGMs with unipolar and bipolar recordings. Results reported that regular filtering altered substantially atrial waveform morphology and was unable to remove moderate amounts of noise, thus turning time and spectral characterization of the EGM notably inaccurate. Methods based on Wavelet transform provided the highest ability to preserve EGM morphology with improvements between 20 and beyond 40%, to minimize dominant atrial frequency estimation error with up to 25% reduction, as well as to reduce huge levels of noise with up to 10 dB better reduction. Consequently, these algorithms are recommended as a replacement of regular filtering to avoid significant alterations in the EGMs. This could lead to more accurate and truthful analyses of atrial activity dynamics aimed at understanding and locating the sources of AF., This work was supported by the projects TEC201452250-R from the Spanish Ministry of Economy and Competitiveness and PPII-2014-026-P from Junta de Comunidades de Castilla-La Mancha.

[EN] Optimal defibrillation timing guided by ventricular fibrillation (VF) waveform analysis would contribute to improved survival of out-of-hospital cardiac arrest (OHCA) patients by minimizing myocardial damage caused by futile defibrillation shocks and minimizing interruptions to cardiopulmonary resuscitation. Recently, fuzzy entropy (FuzzyEn) tailored to jointly measure VF amplitude and regularity has been shown to be an efficient defibrillation success predictor. In this study, 734 shocks from 296 OHCA patients (50 survivors) were analyzed, and the embedding dimension (m) and matching tolerance (r) for FuzzyEn and sample entropy (SampEn) were adjusted to predict defibrillation success and patient survival. Entropies were significantly larger in successful shocks and in survivors, and when compared to the available methods, FuzzyEn presented the best prediction results, marginally outperforming SampEn. The sensitivity and specificity of FuzzyEn were 83.3% and 76.7% when predicting defibrillation success, and 83.7% and 73.5% for patient survival. Sensitivities and specificities were two points above those of the best available methods, and the prediction accuracy was kept even for VF intervals as short as 2s. These results suggest that FuzzyEn and SampEn may be promising tools for optimizing the defibrillation time and predicting patient survival in OHCA patients presenting VF., This work received financial support from Spanish Ministerio de Economia y Competitividad and jointly with the Fondo Europeo de Desarrollo Regional (FEDER), projects TEC2015-64678-R and DPI2017-83952-C3; from UPV/EHU through the grant PIF15/190 and through project GIU17/031; from the Basque Government through grant PRE-2016-1-0012; and from Junta de Comunidades de Castilla-La Mancha through SBPLY/17/180501/000411.

The analysis of P-wave variability from the electrocardiogram (ECG) has been suggested as an early predictor of the onset of paroxysmal atrial fibrillation (PAP). Hence, a preventive treatment could be used to avoid the loss of normal sinus rhythm, thus minimising health risks and improving the patient's quality of life. In these previous studies the variability of different temporal and morphological P-wave features has been only analysed in a linear fashion. However, the electrophysiological alteration occurring in the atria before the onset of PAF has to be considered as an inherently complex, chaotic and non-stationary process. This work analyses the presence of non-linear dynamics in the P-wave progression before the onset of PAF through the application of the central tendency measure (CTM), which is a non-linear metric summarising the degree of variability in a time series. Two hour-length ECG intervals just before the arrhythmia onset belonging to 46 different PAF patients were analysed. In agreement with the invasively observed inhomogeneous atrial conduction preceding the onset of PAF, CTM for all the considered P-wave features showed higher variability when the arrhythmia was closer to its onset. A diagnostic accuracy around 80% to discern between ECG segments far from PAF and close to PAP was obtained with the CTM of the metrics considered. This result was similar to previous P-wave variability methods based on linear approaches. However, the combination of linear and non-linear methods with a decision tree improved considerably their discriminant ability up to 90%, thus suggesting that both dynamics could coexist at the same time in the fragmented depolarisation of the atria preceding the arrhythmia. (C) 2015 IPEM. Published by Elsevier Ltd. All rights reserved., This work was supported by the projects TEC2014-52250-R from the Spanish Ministry of Economy Competitiveness and PPII-2014-026-P from Junta de Comunidades de Castilla-La Mancha.

Atrial fibrillation (AF) is the most common cardiac arrhythmia, as well as a growing healthcare burden worldwide. It is often asymptomatic and usually starts with very brief episodes, thus making its early detection an interesting challenge. For that purpose, the present work introduces a novel method exploiting the variability presented both by ventricular and atrial activities reflected on the surface electrocardiogram (ECG). Thus, time series from the RR intervals and the fibrillatory waves morphology contained by the TQ intervals are first generated and, then, their regularity is estimated making use of the Coefficient of Sample Entropy (COSEn). The collected information is finally combined through a multi-class support vector machine (SVM) approach to discern among short episodes of AF, normal sinus rhythm (NSR) and other rhythms (OR). The algorithm has been validated in the context of the Phy-sioNet Computing in Cardiology Challenge 2017, thus reporting a global F 1 measure of 0.73 for the training set and 0.71 for the testing group. Nonetheless, to evaluate the method in a common scenario for previous works, the widely used MIT-BIH AF database has also been considered. A notably higher F 1 score of 0.87 has been provided in this case. The significantly different balance between the number of AF, NSR and OR recordings in both databases could justify the obtained outcomes.

Thanks to its high rates of success, catheter ablation (CA) is nowadays considered as the first-line therapy to treat atrial fibrillation (AF). However, a procedural consensus beyond pulmonary veins isolation (PVI) still lacks for patients with persistent AF. Nonetheless, the main goal of every CA procedure is to ablate the least amount of tissue able to render patients free from AF at long-term. Hence, the identification of an optimal CA endpoint is an interesting clinical challenge. For that purpose, the present work explores the ability of some spectral features obtained from the atrial activity (AA) reflected on the surface ECG. More precisely, the variations experienced during the procedure by the dominant atrial frequency (DAF), its 3 dB bandwidth and the median atrial frequency (MAF) are analyzed from lead V1 in 15 patients undergoing CA. The obtained results show a strong agreement between the DAF and MAF, which are able to anticipate with an accuracy between 70 and 80% the patients' rhythm after a follow-up of 3 months. As a consequence, monitoring AA spectral features could be helpful in quantifying the atrial substrate alteration provoked by CA procedures.

Atrial fibrillation (AF) is the most common arrhythmia in clinical practice, affecting roughly 1.5–2% of the general population and around 15% of the elderly. Despite its high prevalence, mechanisms triggering and maintaining the arrhythmia are not fully known, thus making current treatments often ineffective. Nonetheless, in last years intensive research in signal processing applied both to surface electrocardiogram (ECG) and intracardiac electrogram (EGM) recordings has resulted in an improved knowledge of this cardiac disorder. Indeed, by taking advantage of the fact that the heart during AF behaves in an inherently complex, heterogeneous and non-stationary way, relevant advances has been reached by nonlinear analysis of that recordings. The main goal of this chapter is hence to review the most significant achievements in some important AF facets, such as identification of events associated to its natural progression, accurate quantification of atrial substrate electrophysiological features and customization of current therapies.

Background The study of atrial conduction defects associated with the onset of paroxysmal atrial fibrillation (PAF) can be addressed by analyzing the P wave from the surface electrocardiogram (ECG). Traditionally, signal-averaged ECGs have been mostly used for this purpose. However, this alternative hinders the possibility to quantify every single P wave, its variability over time, as well as to obtain complimentary and evolving information about the arrhythmia. This work analyzes the time progression of several time and frequency P wave features as potential indicators of atrial conduction variability several hours preceding the onset of PAF. Methods The longest sinus rhythm interval from 24-hour Holter recordings of 46 PAF patients was selected. Next, the 2 hours before the onset of PAF were extracted and divided into two 1-hour periods. Every single P wave was automatically delineated and characterized by 16 time and frequency metrics, such as its duration, absolute energy in several frequency bands and high-to-low-frequency energy ratios. Finally, the P wave variability over each 1-hour period was estimated from the 16 features making use of a least-squares linear fitting. As a reference, the same parameters were also estimated from a set of 1-hour ECG segments randomly chosen from a control group of 53 healthy subjects age-, gender-, and heart rate-matched. Results All the analyzed metrics provided an increasing P wave variability trend as the onset of PAF approximated, being P wave duration and P wave high-frequency energy the most significant individual metrics. The linear fitting slope α associated with P wave duration was (2.48 ± 1.98)×10-2 for healthy subjects, (23.8 ± 14.1)×10-2for ECG segments far from PAF and for (81.8 ± 48.7)×10-2ECG segments close to PAF p = 6.96×10-22. Similarly, the P wave high-frequency energy linear fitting slope was (2.42 ± 4.97)×10-9, (54.2 ± 107.1)×10-9 and (274.2 ± 566.1)×10-9, respectively (p = 2.85×10-20). A univariate discriminant analysis provided that both P wave duration and P wave high-frequency energy could discern among the three ECG sets with diagnostic ability around 80%, which was improved up to 88% by combining these metrics in a multivariate discriminant analysis. Conclusion Alterations in atrial conduction can be successfully quantified several hours before the onset of PAF by estimating variability over time of several time and frequency P wave features.

This paper introduces a new algorithm to quantify the P-wave morphology time course with the aim of anticipating as much as possible the onset of paroxysmal atrial fibrillation (PAF). The method is based on modeling each P-wave with a single Gaussian function and analyzing the extracted parameters variability over time. The selected Gaussian approaches are associated with the amplitude, peak timing, and width of the P-wave. In order to validate the algorithm, electrocardiogram segments 2h preceding the onset of PAF episodes from 46 different patients were assessed. According to the expected intermittently disturbed atrial conduction before the onset of PAF, all the analyzed Gaussian metrics showed an increasing variability trend as the PAF onset approximated. Moreover, the Gaussian P-wave width reported a diagnostic accuracy around 80% to discern between healthy subjects, patients far from PAF, and patients less than 1h close to a PAF episode. This discriminant power was similar to those provided by the most classical time-domain approach, i.e., the P-wave duration. However, this newly proposed parameter presents the advantage of being less sensitive to a precise delineation of the P-wave boundaries. Furthermore, the linear combination of both metrics improved the diagnostic accuracy up to 86.69%. In conclusion, morphological P-wave characterization provides additional information to the metrics based on P-wave timing., This work was supported by the projects TEC2010-20633 from the Spanish Ministry of Science and Innovation and PPII11-0194-8121 from Junta de Comunidades de Castilla La Mancha.

Atrial fibrillation (AF) is the most common arrhythmia in routine clinical practice. Despite many years of research, its mechanisms still are not well understood, thus reducing the effectiveness of AF treatments. Nowadays, pulmonary vein isolation by catheter ablation is the treat- ment of choice for AF resistant either to pharmacological or electrical cardioversion. However, given that long-term recurrences are common, an appropriate patient selection before the procedure is of paramount relevance in the improvement of AF catheter ablation outcome. The present work studies how several spectral features of the atrial activity (AA) from a single lead of the surface electrocardio- gram (ECG) can become potential pre-ablation predictors of long-term (>2 months) sinus rhythm maintenance. Among all the analyzed spectral features, results indicated that the most significant single predictor of paroxysmal AF ablation treatment outcome was related to the amplitude of the first harmonic of the dominant frequency, providing sensitivity (Se), specificity (Sp) and accuracy (Ac) values of 90%, 42.86 and 77.78%, respectively. On the other hand, the AA harmonic structure was the most significant single predictor for persistent AF, with Se, Sp and Ac values of 100%, 54.55 and 77.27%, respectively. A logistic regression analysis, mainly based on spectral amplitudes as well as on the harmonic structure of the AA, provided a higher predictive ability both for paroxysmal AF (Se = 100%, Sp = 57.14% and Ac = 88.89%) and persistent AF (Se = 90.91%, Sp = 72.73 and Ac = 81.82%). In conclusion, the study of key AA spectral features from the surface ECG can provide a significant preoperative prognosis of AF catheter ablation outcome at long-term follow-up., Research funded by Grants TEC2014-52250-R from the Spanish Ministry of Economy and Competitiveness and PPII-2014-026-P from Junta de Comunidades de Castilla-La Mancha.

Background: Several clinical factors have been studied to predict atrial fibrillation (AF) recurrence after electrical cardioversion (ECV) with limited predictive value. Methods: A method able to predict robustly long-standing AF early recurrence by characterizing noninvasively the electrical atrial activity (AA) with parameters related to its time course and spectral features is presented. To this respect, 63 patients (20 men and 43 women; mean age 73.4 ± 9.0 years; under antiarrhythmic drug treatment with amiodarone) who were referred for ECV of persistent AF were studied. During a 4-week follow-up, AF recurrence was observed in 41 patients (65.1%). Results: RR variability and the studied AA spectral features, including dominant atrial frequency (DAF), its first harmonic and their amplitude, provided poor statistical differences between groups. On the contrary, f waves power (fWP) and Sample Entropy (SampEn) of the AA behaved as very good predictors. Patients who relapsed to AF presented lower fWP (0.036 ± 0.019 vs 0.081 ± 0.029 n.u.2, P < 0.001) and higher SampEn (0.107 ± 0.022 vs 0.086 ± 0.033, P < 0.01). Furthermore, fWP presented the highest predictive accuracy of 82.5%, whereas SampEn provided a 79.4%. The remaining features revealed accuracies lower than 70%. A stepwise discriminant analysis (SDA) provided a model based on fWP and SampEn with 90.5% of accuracy. Conclusions: The fWP has proved to predict long-standing AF early recurrence after ECV and can be combined with SampEn to improve its diagnostic ability. Furthermore, a thorough analysis of the results allowed outlining possible associations between these two features and the concomitant status of atrial remodeling. PACE 2011; 34:1241–1250)

Nowadays, several non-linear regularity estimators have been successfully applied to invasive atrial electrograms in order to characterize the atrial electrical activity organization during atrial fibrillation (AF). This arrhythmia is the most common encountered in clinical practice, accounting for approximately one-third of all the hospitalizations for cardiac rhythm disturbances. However, from a clinical point of view, it would be more desired to evaluate atrial activity (AA) organization from surface electrocardiographic (ECG) recordings, since they can be obtained easily and cheaply and the risks associated with invasive recordings could be avoided. In this work, Sample Entropy (SampEn) is proposed to assess the organization degree of the AA obtained from surface ECGs. To this respect, a reliable and non-invasive organization estimator would allow the prediction of spontaneous AF termination, since invasive studies have shown more organized electrical activity signals during the preceding instants of AF termination. The proposed method computed SampEn over the AA obtained from TQ segments, free of QRST complexes, and was validated with a database containing a training set of 20 AF recordings, with known termination properties, and a test set of 30 recordings. A simulation study showed that patients with heart rates of 130 bpm and above must be handled with care, because TQ intervals could be considerably reduced (

The application of non-linear metrics to physiological signals is a valuable tool because “hidden information” related to underlying mechanisms can be obtained. In this respect, approximate entropy (ApEn) is the most popular non-linear regularity index that has been applied to physiological time series. However, ApEn presents some shortcomings, such as bias, relative inconsistency and dependence on the sample length. A modification of ApEn, named sample entropy (SampEn), was introduced to overcome these deficiencies. Recently, in the context of electrocardiography, SampEn has been applied to study non-invasively atrial fibrillation (AF), which is the most common arrhythmia encountered in clinical practice with unknown mechanisms provoking its onset and termination. Useful clinical information, that could help for a better understanding of AF mechanisms, has been obtained through the application of SampEn to electrocardiographic (ECG) recordings. This work reviews its application in the context of non-invasive analysis of AF. During this arrhythmia, atrial and ventricular components can be regarded as unsynchronized activities, whereby, the application of SampEn to the analysis of each component will be described separately. In first place, clinical challenges in which SampEn has been successfully applied to estimate AF organization from the atrial activity pattern are presented. The AF organization study can provide information on the number of active reentries, which can help to improve AF treatment and to take the appropriate decisions on its management. Next, the heart rate variability study via SampEn, to characterize ventricular response and predict AF onset, is described. Through the aforementioned applications it is remarked throughout this review that SampEn can be considered as a very promising and useful tool towards the non-invasive understanding of AF.

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.

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.

Introduction and objectives. Detaited characterization of atrial activity can be achieved by analyzing invasive recordings. Nevertheless, electrocardiographic signal processing tools can nowadays obtain equivalent information in some cases. The aim of this work is to study the electrocardiographic alterations produced during the interval prior to spontaneous AF termination. Methods. Fifty patients in paroxysmal AF with an episode lasting more than 2 hours were selected. The last minute prior to spontaneous AF termination (group S) and the central minute (group N) from each episode were analyzed. Ventricular response, f waves morphology, and atrial activity spectral and organization were studied through the use of signal processing tools. Results. RR intervals variability was a discriminative parameter (group S 630 ± 200 ms vs group N 740 ± 100 ms; P=.034), although a low specificity was obtained (54.2%). Significant differences between both groups were not reported by the f waves morphological study. Group S presented a significant low dominant atrial frequency mean value than group N (group S 5.010 ± 0.671 Hz vs group N 6.514 ± 0.804 Hz, p=4.55×10 ‐9 ). Finally, group N showed a considerable low atrial activity organization degree than group S (group S 0.071 ± 0.012 vs group N 0.103 ± 0.013, p=6.73×10 ‐12 ). Conclusions. The analysis of the electrocardiographic interval prior to spontaneous AF termination has revealed an atrial activity organization process, which cannot be observed by visual inspection of f waves morphology, but can be successfully quantified through signal processing.

The prediction of paroxysmal atrial fibrillation (PAF) onset is an interesting clinical challenge, because the chronification of this highly prevalent arrhythmia could be avoided. Recently, the quantification of the P-wave duration variability over time has revealed a promising ability to detect accurately the onset of PAF. However, the possible scale-dependent variations in this P-wave variability have not been studied yet. In the present work that variations have been analyzed by using a m-lagged central tendency measure (CTM). Thus, once P-waves were delineated, their time course variability was quantified by computing CTM for lags m = 1;2;…;10. Statistically significant differences between ECG segments one-hour far from the onset of PAF and those immediately before the onset were obtained for every lag. Although no great differences were observed among the CTM values obtained for the studied lags, a predictive ability increase of about 3.5% was observed for m = 2 compared with m = 1. This result suggests the existence of scale-dependent dynamics within the transition process from sinus rhythm to PAF.

Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice. Although its termination mechanisms are not still completely known, previous works have reported significant electrocardiographic differences between immediately and non-immediately terminating episodes of paroxysmal AF (PAF). However, none studied how long these differences still hold as we move backward in time. The present work studies a time interval long enough to identify earlier signs forecasting atrial reverse remodeling that will provoke spontaneous AF termination. Reverse remodeling was estimated by measuring AF organisation with the second-order generalised Hurst exponent, H(2), which provided the highest accuracy. Organisation is associated with the number of simultaneous reentries in the atria, which plays a key role in reverse remodeling, thus causing arrhythmia termination. The computation of H(2) depended on two critical parameters: the analysed interval length (L) and the maximum search window for self-similarities (tau(max)). A study with 660 combinations on these two parameters was performed together with the sampling frequency (f(s)) of the recording. The results reported that optimal computational parameters are L = 15 s, tau(max) = 20 milliseconds and f(s) = 1024 Hz. A statistically significant organisation increase was observed during the last 2 min before AF termination, thus suggesting that reverse remodeling only occurs very close to the termination event., This work was supported by the projects TEC2014-52250-R from the Spanish Ministry of Economy and Competitiveness and PPII-2014-026-P from Junta de Comunidades de Castilla La Mancha.

This work introduces for the first time the application of wavelet entropy (WE) to detect episodes of the most common cardiac arrhythmia, atrial fibrillation (AF), automatically from the electrocardiogram (ECG). Given that AF is often asymptomatic and usually presents very brief initial episodes, its early automatic detection is clinically relevant to improve AF treatment and prevent risks for the patients. After discarding noisy TQ intervals from the ECG, the WE has been computed over the median TQ segment obtained from the 10 previous noise-free beats under study. In this way, the P-waves or the fibrillatory waves present in the recording were highlighted or attenuated, respectively, thus enabling the patient's rhythm identification (sinus rhythm or AF). Results provided a discriminant ability of about 95%, which is comparable to previous works. However, in contrast to most of them, which are mainly based on quantifying RR series variability, the proposed algorithm is able to deal with patients under rate-control therapy or with a reduced heart rate variability during AF. Additionally, it also presents interesting properties, such as the lowest delay in detecting AF or sinus rhythm, the ability to detect episodes as brief as five beats in length or its integration facilities under real-time beat-by-beat ECG monitoring systems. Consequently, this tool may help clinicians in the automatic detection of a wide variety of AF episodes, thus gaining further knowledge about the mechanisms initiating this arrhythmia., This work was supported by the projects TEC2014-52250-R from the Spanish Ministry of Economy and Competitiveness and PPII-2014-026-P from Junta de Comunidades de Castilla La Mancha.

[EN] A normal cardiac activation starts in the sinoatrial node and then spreads throughout the atrial myocardium, thus defining the P-wave of the electrocardiogram. However, when the onset of paroxysmal atrial fibrillation (PAF) approximates, a highly disturbed electrical activity occurs within the atria, thus provoking fragmented and eventually longer P-waves. Although this altered atrial conduction has been successfully quantified just before PAF onset from the signal-averaged P-wave spectral analysis, its evolution during the hours preceding the arrhythmia has not been assessed yet. This work focuses on quantifying the P-wave spectral content variability over the 2 h preceding PAF onset with the aim of anticipating as much as possible the arrhythmic episode envision. For that purpose, the time course of several metrics estimating absolute energy and ratios of high- to low-frequency power in different bands between 20 and 200 Hz has been computed from the P-wave autoregressive spectral estimation. All the analyzed metrics showed an increasing variability trend as PAF onset approximated, providing the P-wave high-frequency energy (between 80 and 150 Hz) a diagnostic accuracy around 80% to discern between healthy subjects, patients far from PAF and patients less than 1 h close to a PAF episode. This discriminant power was similar to that provided by the most classical time-domain approach, i.e., the P-wave duration. Furthermore, the linear combination of both metrics improved the diagnostic accuracy up to 88.07%, thus constituting a reliable noninvasive harbinger of PAF onset with a reasonable anticipation. The information provided by this methodology could be very useful in clinical practice either to optimize the antiarrhythmic treatment in patients at high-risk of PAF onset and to limit drug administration in low risk patients. (C) 2015 Elsevier Ireland Ltd. All rights reserved., This work was supported by the projects TEC2014-52250-R from the Spanish Ministry of Economy and Competitiveness and PPII-2014-026-P from Junta de Comunidades de Castilla La Mancha. The authors are grateful to cardiologists Fernando Hornero, Lorenzo Facila and Federico Paredes, from the Cardiac University Hospital of Valencia, for their valuable assistance in providing and inspecting the ECG recordings.

The Cox-maze surgery is an effective procedure for terminating atrial fibrillation (AF) in patients requiring open-heart surgery associated with another heart disease. After the intervention, regardless of the patient's rhythm, all are treated with oral anticoagulants and antiarrhythmic drugs prior to discharge. Furthermore, patients maintaining AF before discharge could also be treated with electrical cardioversion (ECV). In view of this, a preoperative prognosis of the patient's rhythm at discharge would be helpful for optimizing drug therapy planning as well as for advancing ECV therapy. This work analyzes 30 preoperative electrocardiograms (ECGs) from patients suffering from AF in order to predict the Cox-maze surgery outcome at discharge. Two different characteristics of the AF pattern have been studied. On the one hand, the atrial activity (AA) organization, which provides information about the number of propagating wavelets in the atria, was investigated. AA organization has been successfully used in previous studies related to spontaneous reversion of paroxysmal AF and to the outcome of ECV. To assess organization, the dominant atrial frequency (DAF) and sample entropy (SampEn) have been computed. On the other hand, the second characteristic studied was the fibrillatory wave (f-wave) amplitude, which has been demonstrated to be a valuable indicator of the Cox-maze surgery outcome in previous studies. Moreover, this parameter has been obtained through a new methodology, based on computing the f-wave average power (fWP). Finally, all the computed indices were combined in a decision tree in order to improve prediction capability. Results for the DAF yielded a sensitivity (Se), a specificity (Sp) and an accuracy (Acc) of 61.54%, 82.35% and 73.33%, respectively. For SampEn the values were 69.23%, 76.00% and 73.33%, respectively, and for fWP they were 92.31%, 82.35% and 86.67%, respectively. Finally, the decision tree combining the three parameters analyzed improved the preoperative prognosis of the Cox-maze outcome with values of Se, Sp and Acc of 100%, 82.35% and 90%, respectively. As a consequence, the analysis of parameters related to the f-wave pattern, extracted from the preoperative ECG, has provided a considerable ability to predict the outcome of AF Cox-maze surgery at discharge. © 2014 Institute of Physics and Engineering in Medicine., This work was supported by the projects TEC2010-20633 from the Spanish Ministry of Science and Innovation, TEC2013-41428-R from the Spanish Ministry of Economy and Competitiveness and PPII11-0194-8121 from Junta de Comunidades de Castilla La Mancha.

The present work introduces a novel methodology to quantitatively assess interdependence between real atrial and ventricular activation series during atrial fibrillation (AF), which is the most common arrhythmia in clinical practice. The method is based on a nonlinear index, such as cross-sample entropy (CSE), which estimates the conditional probability to find similar patterns within both activation series. The study has been carried out on patients with paroxysmal and persistent AF in order to be applied over atrial activation series with different properties in their organization. In agreement with previous findings, results showed a statistically significant positive correlation between atrial activity organization and the synchronization between atrial and ventricular activations (R = 0.53, p < 0.01). Moreover, higher CSE values were also observed for persistent (0.759 ± 0.053) than for paroxysmal AF episodes (0.662 ± 0.091). As a consequence, CSE could be used to reveal clinically useful information in the improvement of current rate control therapies, which are mainly focused on controlling ventricular rate without paying much attention to the atrial fibrillatory process.

Analysis of the electrical activity organization within the atria can reveal interesting clinical information related to atrial fibrillation (AF), which is the most commonly diagnosed arrhythmia in clinical practice. However, a very reduced number of indirect AF organization estimators from the surface electrocardiogram (ECG) have been proposed to date. The present work introduces a new method for direct and short-time AF organization estimation from each surface lead of ECG recordings. The temporal arrhythmia organization was estimated through the computation of morphological variations among single fibrillatory (f) waves. They were delineated and extracted from the atrial activity (AA) signal using an adaptive signed correlation index. The algorithm was tested on real AF recordings to discriminate atrial signals with different organization degrees. Results indicated a notably higher global accuracy (90.3%) in comparison with the two non-invasive AF organization estimates widely used today: the dominant atrial frequency (70.5%) and sample entropy (76.1%). Moreover, due to its ability to assess AA regularity wave-to-wave, the proposed method was also able to pursue AF organization time course more precisely than the aforementioned indices.

Despite recent advances in the treatment of atrial fibrillation (AF), Cox-Maze surgery still is the therapy with the highest success rate. Long-term outcome prediction of this procedure has beenwidely studied in previousworks. However, only few studies addressed the short-time prediction issue. Moreover, they all presented a very limited predictive ability. In the present work, preoperative information from the surface electrocardiogram (ECG) is analyzed to predict the patient’s rhythm after the blanking period, which lasts 3 months after the surgery. Three aspects have been studied: the dominant atrial frequency (DAF), the pattern repetitiveness in the atrial activity via sample entropy (SampEn) and the fibrillatory waves mean power (fWP). They all revealed a considerably high prognosis ability of 68.97%, 72.42% and 75.86%, respectively. Additionally, the combination of these parameters through a decision tree improved substantially the prognosis accuracy up to 82.76%, with sensitivity of 81.25% and specificity of 84.62%. Hence, the preoperative ECG can yield clinically relevant information on the patient’s rhythm after the blanking period and could be helpful in the development of tailored postoperative therapies.

This work evaluates the use of the Generalized Hurst Exponents (H(q)) in the prediction of paroxysmal atrial fibrillation (PAF) spontaneous termination from surface electrocardiographic recordings. Although atrial fibrillation (AF) is the most common supraventricular tachyarrhythmia, the mechanisms leading to its spontaneous termination are yet not fully explained. Hurst Exponents, H(q), measure the existence of long-term statistical dependencies in a time series. Therefore, they could provide an estimation of the atrial activity organization during PAF. Since the likelihood of PAF spontaneous termination relates to AF organization, H(q) might predict PAF termination. H(q) has been applied to the prediction of PAF termination using a reference database to allow an easy comparison with previous works. Two established AF organization estimators, the Dominant Atrial Frequency (DAF) and Sample Entropy (SampEn), have been used as a reference. H(q) reached an accuracy of 95.0% in the prediction of PAF termination, thus yielding better results than DAF and SampEn (both obtaining 88.3% accuracy). As a consequence, H(q) could be considered as a promising tool for the prediction of PAF termination.

Although atrial fibrillation (AF) is the most common cardiac arrhythmia, the knowledge about its causes and mechanisms still is uncompleted. Several studies suggest that structural and electrophysiological remodeling are directly related to its development and perpetuation. To this respect, the surface electrocardiogram (ECG) has been used to assess different aspects of atrial remodeling. However, specific ECG parameters have never been used to provide valuable clinical information in the study of AF aggressive treatments, such as the Cox-Maze surgery. In this work, parameters from the fibrillatory (f) waves, such as their regularity or their amplitude, are studied after a six months follow-up period from the Cox-Maze surgery. This study culminates previous works on Cox-Maze outcome prognosis at discharge and after the blanking period. The results obtained reported a remarkable prediction capability decrease of the preoperative ECG parameters, with respect to similar studies at the time of discharge and after the surgery blanking period. More concretely, the prognosis accuracy of the f waves amplitude, the dominant atrial frequency and the f waves regularity was 58.3%, 62.5% and 66.6%, respectively. Pharmacological and electrical cardioversion treatments administered during the follow-up period could be the main reason to this result. Therefore, atrial substrate remodeling during the follow-up causes a remarkable influence on the preoperative surgery outcome prognosis based on the f waves analysis.

The present work introduces the first study on the P-wave morphological variability two hours preceding the onset of paroxysmal atrial fibrillation (PAF). The development of non-invasive methods able to track P-wave alterations over time is a clinically relevant tool to anticipate as much as possible the envision of a new PAF episode. This information is essential for further improvement of preventive and patient-tailored treatment strategies, which could avert the loss of sinus rhythm. In this way, risks for the patients could be minimized and their quality of life improved. Recently, the P-wave morphological analysis is drawing increasing attention because differences in morphology can reflect different atrial activation patterns. Indeed, the P-wave morphology study has recently proved to be useful for determining the presence of an underlying pathophysiological condition in patients prone to atrial fibrillation. However, the P-wave morphology variability over time has not been studied yet. In this respect, the present work puts forward some parameters related to the P-wave shape and energy with the ability to quantify non-invasively the notable atrial conduction alterations preceding the onset of PAF. Results showed that P-wave fragmentation and area presented higher variability over time as the onset of PAF approximates. By properly combining these indices, an average global accuracy of 86.33% was achieved to discern between electrocardiogram segments from healthy subjects, far from a PAF episode and less than one hour close to a PAF episode. As a consequence, the P-wave morphology long-term analysis seems to be a useful tool for the non-invasive envision of PAF onset with a reasonable anticipation. Nonetheless, further research is required to corroborate this finding and to validate the capability of the proposed P-wave metrics in the earlier prediction of PAF onset. © 2014 Institute of Physics and Engineering in Medicine., This work was supported by the projects TEC2010-20633 from the Spanish Ministry of Science and Innovation and PPII11-0194-8121 from Junta de Comunidades de Castilla-La Mancha.

Atrial fibrillation (AF) is the most common form of arrhythmia encountered in clinical practice. Its presence causes a rapid and irregular ventricular response, being the topic of intensive research in rate control therapies of AF. To this respect, recent studies suggest that ventricular response is notably influenced by atrial activity (AA) temporal organization. However, the interdependency between atrial and ventricular activations has not been adequately explored to date in real-life AF patients. The present work introduces a novel methodology to quantitatively assess synchronization and coupling between real atrial and ventricular activation series. Furthermore, the method operates on surface ECG recordings, thus providing an easy and cost-effective way to be applied. The method is based on a nonlinear index, such as cross-sample entropy (CSE), which estimates the conditional probability to find similar patterns within both activation series. The study has been carried out on patients with paroxysmal and persistent AF in order to be applied over atrial activation series with different properties in their organization. Results showed a statistically significant positive correlation between AA organization and the synchronization between atrial and ventricular activations (R = 0.53, p < 0.01). Furthermore, higher CSE values were observed for persistent (0.759 ± 0.053) than for paroxysmal AF episodes (0.662 ± 0.091), thus suggesting more synchronization between atrial and ventricular activations in paroxysmal AF. As a consequence, CSE provided findings consistent with previous works and could be used to reveal clinically useful information in the improvement of current rate control therapies, which are mainly focused on controlling ventricular rate without paying much attention to the atrial fibrillatory process., This work was supported by the projects TEC2010-20633 from the Spanish Ministry of Science and Innovation and PPII11-0194-8121 from Junta de Comunidades de Castilla-La Mancha.

Analysis of cardiac time series by nonlinear metrics has recently gained great interest, because the latter observations suggest that the mechanisms involved in cardiovascular regulation likely interact with each other in a nonlinear way [1]. Furthermore, chaotic behavior can be appreciated in the diseased heart with atrial fibrillation (AF) at cellular level and atrial electrophysiological remodeling during this arrhythmia is a far-from-linear process [2]. Hence, the purpose of this chapter is to review the use of nonlinear methods in the analysis of AF, highlighting the clinically useful revealed information that can improve the understanding of this arrhythmia mechanisms and the existing treatments.

Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice, increasing the risk of stroke and all-cause mortality. Its mechanisms are poorly understood, thus leading to different theories and controversial interpretation of its behavior. In this respect, it is unknown why AF is self-terminating in certain individuals, which is called paroxysmal AF (PAF), and not in others. Within the context of biomedical signal analysis, predicting the onset of PAF with a reasonable advance has been a clinical challenge in recent years. By predicting arrhythmia onset, the loss of normal sinus rhythm could be addressed by means of preventive treatments, thus minimizing risks for the patients and improving their quality of life. Traditionally, the study of PAF onset has been undertaken through a variety of features characterizing P-wave spatial diversity from the standard 12-lead electrocardiogram (ECG) or from signal-averaged ECGs. However, the variability of features from the P-wave time course before PAF onset has not been exploited yet. This work introduces a new alternative to assess time diversity of the P-wave features from single-lead ECG recordings. Furthermore, the method is able to assess the risk of arrhythmia 1 h before its onset, which is a relevant advance in order to provide clinically useful PAF risk predictors. Results were in agreement with the electrophysiological changes taking place in the atria. Hence, P-wave features presented an increasing variability as PAF onset approximates, thus suggesting intermittently disturbed conduction in the atrial tissue. In addition, high PAF risk prediction accuracy, greater than 90%, has been reached in the two considered scenarios, i.e. discrimination between healthy individuals and PAF patients and between patients far from PAF and close to PAF onset. Nonetheless, more long-term studies have to be analyzed and validated in future works., 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.

This paper proposes the first non-invasive method for direct and short-time regularity quantification of atrial fibrillatory (f) waves from the surface ECG in atrial fibrillation (AF). Regularity is estimated by computing individual morphological variations among f waves, which are delineated and extracted from the atrial activity (AA) signal, making use of an adaptive signed correlation index. The algorithm was tested on real AF surface recordings in order to discriminate atrial signals with different organization degrees, providing a notably higher global accuracy (90.3%) than the two non-invasive AF organization estimates defined to date: the dominant atrial frequency (70.5%) and sample entropy (76.1%). Furthermore, due to its ability to assess AA regularity wave to wave, the proposed method is also able to pursue AF organization time course more precisely than the aforementioned indices. As a consequence, this work opens a new perspective in the non-invasive analysis of AF, such as the individualized study of each f wave, that could improve the understanding of AF mechanisms and become useful for its clinical treatment., The authors are grateful to Drs Javier Vinas, Elio Martin and Alejandro Vazquez for their contribution to classify blindly the AF episodes used in this work. 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.

Ectopic beats are early heart beats remarkably different to the normal beat morphology that provoke serious disturbances in electrocardiographic analysis. These beats are very common in atrial fibrillation (AF), causing important residua when ventricular activity has to be removed for atrial activity (AA) analysis. In this work, a method is proposed to cancel out ectopics by discriminating between normal and abnormal beats, with an accuracy higher than 99%, through QRS morphological delineation and characterization. The most similar ectopics to the one under cancellation are clustered to provide a very precise cancellation template. Simulated and real AF recordings were used to validate the method. A new index, able to estimate the presence of ventricular residue after ectopics cancellation, was defined. Results by using the 2, 4, 6, , 30 most similar ectopics to the one under study yielded optimal cancellation for templates composed of 10 beats. Furthermore, these beats were very likely located close to the ectopic under cancellation, which could facilitate the algorithm implementation. As conclusion, the proposed method is an effective way to remove ectopics from long term AF recordings and get them ready for the application of any QRST cancellation technique able to extract the AA in optimal conditions. Moreover, it could also detect, characterize and remove ectopics in any other type of non-AF recordings., 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.