Multi-scale Entropy Evaluates the Proarrhythmic Condition of Persistent Atrial Fibrillation Patients Predicting Early Failure of Electrical Cardioversion.

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 20-30% 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 3-9% and other previously proposed predictors of ECV by 15-30%. 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.

Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest.

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.

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

BACKGROUND: Atrial fibrillation (AF) is the most common supraventricular arrhythmia in the clinical practice, being the subject of intensive research. METHODS: The present work introduces two different Wavelet Transform (WT) applications to electrocardiogram (ECG) recordings of patients in AF. The first one predicts spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the prediction of electrical cardioversion (ECV) outcome in persistent AF patients. In both cases, the central tendency measure (CTM) from the first differences scatter plot was applied to the AF wavelet decomposition. In this way, the wavelet coefficients vector CTM associated to the AF frequency scale was used to assess how atrial fibrillatory (f) waves variability can be related to AF events. RESULTS: Structural changes into the f waves can be assessed by combining WT and CTM to reflect atrial activity organization variation. This fact can be used to predict organization-related events in AF. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity and accuracy were 100%, 91.67% and 96%, respectively. On the other hand, for ECV outcome prediction, 82.93% sensitivity, 90.91% specificity and 85.71% accuracy were obtained. Hence, CTM has reached the highest diagnostic ability as a single predictor published to date. CONCLUSIONS: Results suggest that CTM can be considered as a promising tool to characterize non-invasive AF signals. In this sense, therapeutic interventions for the treatment of paroxysmal and persistent AF patients could be improved, thus, avoiding useless procedures and minimizing risks.

Time and frequency recurrence analysis of persistent atrial fibrillation after electrical cardioversion.

Electrical cardioversion (ECV) has become a mainstay of therapy for the treatment of persistent atrial fibrillation (AF), which is an arrhythmia that affects up to 1% of the general population. The procedure is initially effective, but it is also characterized by a high rate of AF recurrence. As a consequence, it would be clinically useful to predict normal sinus rhythm (NSR) maintenance after ECV before it is attempted. In this respect, several clinical, echocardiographic and demographic parameters have been analyzed by other authors. However, these indices are weak predictors of ECV outcome. In this work, surface electrocardiographic (ECG) recordings were used to extract the atrial activity (AA) signal and parametrize the fibrillatory (f) waves, both in time and frequency, to obtain AF recurrence predictors. Parameters as f waves amplitude (fWA), AA mean power, dominant atrial frequency (DAF), its first harmonic, etc were studied. Obtained results showed that fWA was the most significant predictor of AF recurrence 1 month later. Concretely, 72.73% of the patients resulting in NSR, 83.87% relapsing to AF and 80.0% with unsuccessful ECV, were correctly identified. Therefore, fWA classified satisfactorily 79.37% of the analyzed patients. In addition, a forward stepwise discriminant analysis, with a leave-one-out cross validation approach, proved that fWA and DAF combination provided an improved diagnostic ability of 85.71%. In this case 86.36%, 83.87% and 90% of the patients who resulted in NSR, relapsed to AF and with unsuccessful ECV, were correctly discerned, respectively. In conclusion, fWA could be considered as a promising predictor of ECV outcome during the first month following the procedure. Additionally, time and frequency indices could yield complementary information useful to predict the cardioversion outcome. Finally, further studies are needed to validate the robustness of these parameters and the repeatability of the obtained results on wider databases.

Wavelet bidomain sample entropy analysis to predict spontaneous termination of atrial fibrillation.

The ability to predict if an atrial fibrillation (AF) episode terminates spontaneously or not through non-invasive techniques is a challenging problem of great clinical interest. This fact could avoid useless therapeutic interventions and minimize the risks for the patient. The present work introduces a robust AF prediction methodology carried out by estimating, through sample entropy (SampEn), the atrial activity (AA) organization increase prior to AF termination from the surface electrocardiogram (ECG). This regularity variation appears as a consequence of the decrease in the number of reentries wandering throughout the atrial tissue. AA was obtained from surface ECG recordings by applying a QRST cancellation technique. Next, a robust and reliable classification process for terminating and non-terminating AF episodes was developed, making use of two different wavelet decomposition strategies. Finally, the AA organization both in time and wavelet domains (bidomain) was estimated via SampEn. The methodology was validated using a training set consisting of 20 AF recordings with known termination properties and a test set of 30 recordings. All the training signals and 93.33% of the test set were correctly classified into terminating and sustained AF, obtaining 93.75% sensitivity and 92.86% specificity. It can be concluded that spontaneous AF termination can be reliably and noninvasively predicted by applying wavelet bidomain sample entropy.

Adaptive singular value cancelation of ventricular activity in single-lead atrial fibrillation electrocardiograms.

The proper analysis and characterization of atrial fibrillation (AF) from surface electrocardiographic (ECG) recordings requires to cancel out the ventricular activity (VA), which is composed of the QRS complex and the T wave. Historically, for single-lead ECGs, the averaged beat subtraction (ABS) has been the most widely used technique. However, this method is very sensitive to QRST wave variations and, moreover, high-quality cancelation templates may be difficult to obtain when only short length and single-lead recordings are available. In order to overcome these limitations, a new QRST cancelation method based on adaptive singular value cancelation (ASVC) applied to each single beat is proposed. In addition, an exhaustive study about the optimal set of complexes for better cancelation of every beat is also presented for the first time. The whole study has been carried out with both simulated and real AF signals. For simulated AF, the cancelation performance was evaluated making use of a cross-correlation index and the normalized mean square error (nmse) between the estimated and the original atrial activity (AA). For real AF signals, two additional new parameters were proposed. First, the ventricular residue (VR) index estimated the presence of ventricular activity in the extracted AA. Second, the similarity (S) evaluated how the algorithm preserved the AA segments out of the QRST interval. Results indicated that for simulated AF signals, mean correlation, nmse, VR and S values were 0.945 +/- 0.024, 0.332 +/- 0.073, 1.552 +/- 0.386 and 0.986 +/- 0.012, respectively, for the ASVC method and 0.866 +/- 0.042, 0.424 +/- 0.120, 2.161 +/- 0.564 and 0.922 +/- 0.051 for ABS. In the case of real signals, the mean VR and S values were 1.725 +/- 0.826 and 0.983 +/- 0.038, respectively, for ASVC and 3.159 +/- 1.097 and 0.951 +/- 0.049 for ABS. Thus, ASVC provides a more accurate beat-to-beat ventricular QRST representation than traditional techniques. As a consequence, VA cancelation is optimized and the AA can be extracted more precisely. Finally, the study has proven that optimal VA cancelation is achieved when a number between 20 and 30 complexes is selected following a correlation-based strategy.

Comparative study of methods for ventricular activity cancellation in atrial electrograms of atrial fibrillation.

Atrial fibrillation is a very common cardiovascular disease in clinical practice. One relevant issue to understand its pathophysiological mechanisms is the analysis and interpretation of atrial electrograms (AEG). To study these signals properly, ventricular activity has to be removed from the AEG. In this work, a new application of independent component analysis (ICA) to the AEG is presented, where ventricular activity is removed from atrial epicardial 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. In addition to the proposed new methodology this work also presents the first comparative study, making use of unipolar epicardial AEGs, among the ICA-based technique, template matching and subtraction (TMS), and adaptive ventricular cancellation (AVC) on a database of 20 patients. A performance comparative analysis was carried out by evaluating epicardial atrial waveform similarity (S) and ventricular depolarization reduction (VDR) as a function of atrial rhythm regularity on a beat-by-beat basis. Results indicate that, when the epicardial atrial rhythm is quite organized, ICA is able to preserve the atrial waveform very precisely and better than the other methods (median S = 99.64% +/- 0.31% in contrast to 95.18% +/- 2.71% for TMS and 94.76% +/- 4.12% for AVC). Moreover, ventricular reduction is the best for ICA (median VDR = 6.32 +/- 4.41 dB in contrast to 4.98 +/- 4.48 dB for TMS and 4.12 +/- 2.72 dB for AVC). On the other hand, when the atrial activity is disorganized, TMS notably improves performance (S = 97.72% +/- 1.87%), but ICA still is the best in waveform preservation (S = 98.22% +/- 1.53%) whereas AVC remains similar (S = 93.74% +/- 4.38%). In conclusion, ICA can be considered as notably the best approach to reduce ventricular activity from unipolar atrial electrograms in organized atrial arrhythmias. On the other hand, both TMS and ICA give quite similar results when the atrial arrhythmia is disorganized.

Automatic segmentation and 3D reconstruction of intravascular ultrasound images for a fast preliminar evaluation of vessel pathologies.

Intravascular ultrasound (IVUS) imaging is used along with X-ray coronary angiography to detect vessel pathologies. Manual analysis of IVUS images is slow and time-consuming and it is not feasible for clinical purposes. A semi-automated method is proposed to generate 3D reconstructions from IVUS video sequences, so that a fast diagnose can be easily done, quantifying plaque length and severity as well as plaque volume of the vessels under study. The methodology described in this work has four steps: a pre-processing of IVUS images, a segmentation of media-adventitia contour, a detection of intima and plaque and a 3D reconstruction of the vessel. Preprocessing is intended to remove noise from the images without blurring the edges. Segmentation of media-adventitia contour is achieved using active contours (snakes). In particular, we use the gradient vector flow (GVF) as external force for the snakes. The detection of lumen border is obtained taking into account gray-level information of the inner part of the previously detected contours. A knowledge-based approach is used to determine which level of gray corresponds statistically to the different regions of interest: intima, plaque and lumen. The catheter region is automatically discarded. An estimate of plaque type is also given. Finally, 3D reconstruction of all detected regions is made. The suitability of this methodology has been verified for the analysis and visualization of plaque length, stenosis severity, automatic detection of the most problematic regions, calculus of plaque volumes and a preliminary estimation of plaque type obtaining for automatic measures of lumen and vessel area an average error smaller than 1mm(2) (equivalent aproximately to 10% of the average measure), for calculus of plaque and lumen volume errors smaller than 0.5mm(3) (equivalent approximately to 20% of the average measure) and for plaque type estimates a mismatch of less than 8% in the analysed frames.

Comparison of atrial signal extraction algorithms in 12-lead ECGs with atrial fibrillation.

Analysis of atrial rhythm is important in the treatment and management of patients with atrial fibrillation. Several algorithms exist for extracting the atrial signal from the electrocardiogram (ECG) in atrial fibrillation, but there are few reports on how well these techniques are able to recover the atrial signal. We assessed and compared three algorithms for extracting the atrial signal from the 12-lead ECG. The 12-lead ECGs of 30 patients in atrial fibrillation were analyzed. Atrial activity was extracted by three algorithms, Spatiotemporal QRST cancellation (STC), principal component analysis (PCA), and independent component analysis (ICA). The amplitude and frequency characteristics of the extracted atrial signals were compared between algorithms and against reference data. Mean (standard deviation) amplitude of QRST segments of V1 was 0.99 (0.54) mV, compared to 0.18 (0.11) mV (STC), 0.19 (0.13) mV (PCA), and 0.29 (0.22) mV (ICA). Hence, for all algorithms there were significant reductions in the amplitude of the ventricular activity compared with that in V1. Reference atrial signal amplitude in V1 was 0.18 (0.11) mV, compared to 0.17 (0.10) mV (STC), 0.12 (0.09) mV (PCA), and 0.18 (0.13) mV (ICA) in the extracted atrial signals. PCA tended to attenuate the atrial signal in these segments. There were no significant differences for any of the algorithms when comparing the amplitude of the reference atrial signal with that of the extracted atrial signals in segments in which ventricular activity had been removed. There were no significant differences between algorithms in the frequency characteristics of the extracted atrial signals. There were discrepancies in amplitude and frequency characteristics of the atrial signal in only a few cases resulting from notable residual ventricular activity for PCA and ICA algorithms. In conclusion, the extracted atrial signals from these algorithms exhibit very similar amplitude and frequency characteristics. Users of these algorithms should be observant of residual ventricular activities which can affect the analysis of the fibrillatory waveform in clinical practice.

Gaussian modeling of the P-wave morphology time course applied to anticipate paroxysmal atrial fibrillation.

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 2 h 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 1 h 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.

Atrial activity extraction for atrial fibrillation analysis using blind source separation.

This contribution addresses the extraction of atrial activity (AA) from real electrocardiogram (ECG) recordings of atrial fibrillation (AF). We show the appropriateness of independent component analysis (ICA) to tackle this biomedical challenge when regarded as a blind source separation (BSS) problem. ICA is a statistical tool able to reconstruct the unobservable independent sources of bioelectric activity which generate, through instantaneous linear mixing, a measurable set of signals. The three key hypothesis that make ICA applicable in the present scenario are discussed and validated: 1) AA and ventricular activity (VA) are generated by sources of independent bioelectric activity; 2) AA and VA present non-Gaussian distributions; and 3) the generation of the surface ECG potentials from the cardioelectric sources can be regarded as a narrow-band linear propagation process. To empirically endorse these claims, an ICA algorithm is applied to recordings from seven patients with persistent AF. We demonstrate that the AA source can be identified using a kurtosis-based reordering of the separated signals followed by spectral analysis of the sub-Gaussian sources. In contrast to traditional methods, the proposed BSS-based approach is able to obtain a unified AA signal by exploiting the atrial information present in every ECG lead, which results in an increased robustness with respect to electrode selection and placement.

Preoperative study of the surface ECG for the prognosis of atrial fibrillation maze surgery outcome at discharge.

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.

Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation.

The most extended noninvasive technique for medical diagnosis and analysis of atrial fibrillation (AF) relies on the surface elctrocardiogram (ECG). In order to take optimal profit of the ECG in the study of AF, it is mandatory to separate the atrial activity (AA) from other cardioelectric signals. Traditionally, template matching and subtraction (TMS) has been the most widely used technique for single-lead ECGs, whereas multi-lead ECGs have been addressed through statistical signal processing techniques, like independent component analysis. In this contribution, a new QRST cancellation method based on a radial basis function (RBF) neural network is proposed. The system is able to provide efficient QRST cancellation and can be applied both to single and multi-lead ECG recordings. The learning algorithm used for training the RBF makes use of a special class of network, known as cosine RBF, by updating selected adjustable parameters to minimize the class-conditional variances at the outputs of the network. The experiments verify that RBFs trained by the proposed learning algorithm are capable of reducing the QRST complex dramatically, a property that is not shared by other methods and conventional feed-forward neural networks. Average Results (mean ± std) for the RBF method in cross-correlation (CC) between original and estimated AA are CC=0.95±0.038 being the mean square error (MSE) for the same signals, MSE=0.311±0.078. Regarding spectral parameters, the dominant amplitude (DA) and the mean power spectral (MP) were DA=1.15±0.18 and MP=0.31±0.07, respectively. In contrast, traditional TMS-based methods yielded, for the best case, CC=0.864±0.041, MSE=0.577±0.097, DA=0.84±0.25 and MP=0.24±0.07. The results prove that the RBF based method is able to obtain a remarkable reduction of ventricular activity and a very accurate preservation of the AA, thus providing high quality dissociation between atrial and ventricular activities in AF recordings.

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

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

Classification of paroxysmal and persistent atrial fibrillation in ambulatory ECG recordings.

The problem of classifying short atrial fibrillatory segments in ambulatory ECG recordings as being either paroxysmal or persistent is addressed by investigating a robust approach to signal characterization. The method comprises preprocessing estimation of the dominant atrial frequency for the purpose of controlling the subbands of a filter bank, computation of the relative subband (harmonics) energy, and the subband sample entropy. Using minimum-error-rate classification of different feature vectors, a data set consisting of 24-h ambulatory recordings from 50 subjects with either paroxysmal (26) or persistent (24) atrial fibrillation (AF) was analyzed on a 10-s segment basis; a total of 212,196 segments were classified. The best performance in terms of area under the receiver operating characteristic curve was obtained for a feature vector defined by the subband sample entropy of the dominant atrial frequency and the relative harmonics energy, resulting in a value of 0.923, whereas that of the dominant atrial frequency was equal to 0.826. It is concluded that paroxysmal and persistent AFs can be discriminated from short segments with good accuracy at any time of an ambulatory recording.

Surface ECG organization time course analysis along onward episodes of paroxysmal atrial fibrillation.

The complete understanding of the mechanisms leading to the initiation, maintenance and self-termination of atrial fibrillation (AF) still is an unsolved challenge for cardiac electrophysiology. Studies in which AF has been induced have shown that electrophysiological and structural remodeling of the atria during the arrhythmia could play an important role in the transition from paroxysmal to persistent AF. However, to this day, the time course of the atrial remodeling along onward episodes of non-induced paroxysmal AF has not been investigated yet. In this work, a non-invasive method, based on the regularity estimation of AF through sample entropy (SampEn), has been used to assess the organization evolution along onward episodes of paroxysmal AF. Given that AF organization has been associated to the number of existing wavelets wandering throughout the atrial tissue, SampEn could be considered as a concomitant estimator of atrial remodeling. The achieved results, in close agreement with previous findings obtained from invasive recordings, proved several relevant aspects of arial remodeling. Firstly, a progressive disorganization increase (SampEn increase) along onward episodes of AF has been observed for 63% of the analyzed patients, whereas a stable AF organization degree has been appreciated in the remaining 37%. Next, a positive correlation between episode duration and SampEn has been obtained (R=0.541, p<0.01). Finally, a remarkable influence of the fibrillation-free interval, preceding each episode, on the corresponding level of AF organization at the onset of the subsequent AF episode has been observed, with a correlation between these two indices of R=0.389 (p<0.01). As a consequence, it could be considered that atrial electrophysiological dynamics that occur along onward paroxysmal AF episodes are reflected and can be quantified from ECG recordings through non-invasive organization estimation.

The application of nonlinear metrics to assess organization differences in short recordings of paroxysmal and persistent atrial fibrillation.

Atrial fibrillation (AF) is the most common arrhythmia in clinical practice. In the first stages of the disease, AF may terminate spontaneously and it is referred to as paroxysmal AF. The arrhythmia is called persistent AF when external intervention is required to its termination. In the present work, a method to non-invasively assess AF organization has been applied to discern between paroxysmal and persistent AF episodes at any time. Previous works have suggested that the probability of AF termination is inversely related to the number of reentries wandering throughout the atrial tissue. Given that it has also been hypothesized that the number of reentries is directly correlated with AF organization, a fast and robust method able to assess organization differences in AF could be of great interest. In fact, the distinction between paroxysmal and persistent episodes in patients without previously known AF history, making use of short ECG recordings, could contribute to taking earlier decisions on AF management in daily clinical practice, without the need to require 24 h or 48 h Holter recordings. The method was based on a nonlinear regularity index, such as sample entropy (SampEn), and evidenced to be a significant discriminator of the AF type. Its diagnostic accuracy of 91.80% was demonstrated to be superior to previously proposed parameters, such as dominant atrial frequency (DAF) and fibrillatory waves amplitude, and to others analyzed for the first time in this context, such as atrial activity mean power, 3 dB bandwidth around the DAF, first harmonic frequency, harmonic exponential decay, etc. Additionally, according to previous invasive works, paroxysmal AF episodes (0.0716 +/- 0.0143) presented lower SampEn values and, consequently, more organized activity, than persistent episodes (0.1080 +/- 0.0145).

Application of the phasor transform for automatic delineation of single-lead ECG fiducial points.

This work introduces a new single-lead ECG delineator based on phasor transform. The method is characterized by its robustness, low computational cost and mathematical simplicity. It converts each instantaneous ECG sample into a phasor, and can precisely manage P and T waves, which are of notably lower amplitude than the QRS complex. The method has been validated making use of synthesized and real ECG sets, including the MIT-BIH arrhythmia, QT, European ST-T and TWA Challenge 2008 databases. Experiments with the synthesized recordings reported precise detection and delineation performances in a wide variety of ECGs, with signal-to-noise ratios of 10 dB and above. For real ECGs, the QRS detection was characterized by an average sensitivity of 99.81% and positive predictivity of 99.89%, for all the analyzed databases (more than one million beats). Regarding delineation, the maximum localization error between automatic and manual annotations 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. Furthermore, after revising and reannotating some ECG recordings by expert cardiologists, the delineation error decreased notably, becoming lower than 3.5 ms, on average, and reducing by a half its standard deviation. This new proposed strategy outperforms the results provided by other well-known delineation algorithms and, moreover, presents a notably lower computational cost.

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

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

Time and frequency series combination for non-invasive regularity analysis of atrial fibrillation.

In this work, we present a new method based on electrocardiogram signal processing to distinguish between the atrial fibrillation (AF) episodes that terminate immediately and those that sustain. The spectrogram of the atrial activity is computed and 12 numerical series of spectral parameters are constructed. The sample entropy (SampEn) of six series are relevant in the characterization of AF termination (p < 0.05). Furthermore, a combined discriminant analysis in both time and frequency domains is performed, which improves the univariant time-frequency analysis. The discriminant analysis achieves optimal combination of parameters so that the percentage of correctly classified recordings reaches 100% for the learning set and 93.33% for the test set. The main conclusion is that the combined analysis of time and frequency series regularity might be used to predict spontaneous termination of paroxysmal AF and could provide information about the organization of atrial activation in AF.