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Alcaraz R.,University of Castilla - La Mancha | Rieta J.J.,Synergy Biomedical
Biomedical Signal Processing and Control | Year: 2010

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. © 2009 Elsevier Ltd. All rights reserved.


Alcaraz R.,University of Castilla - La Mancha | Hornero F.,University of Valencia | Rieta J.J.,Synergy Biomedical
PACE - Pacing and Clinical Electrophysiology | Year: 2011

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. © 2010 Wiley Periodicals, Inc.


Alcaraz R.,University of Castilla - La Mancha | Hornero F.,University of Valencia | Rieta J.J.,Synergy Biomedical
Physiological Measurement | Year: 2011

The standard electrocardiogram (ECG) is the most common non-invasive way to study atrial fibrillation (AF). In this respect, previous works have shown that the surface lead V1 reflects mainly the dominant atrial frequency (DAF) of the right atrium (RA), which has been widely used to study AF. In a similar way, AF organization and fibrillatory (f) wave amplitude are two recently proposed non-invasive AF markers. These markers need to be validated with invasive recordings in order to assess their capability to reliably reflect the internal fibrillatory activity dynamics. In this work, these two non-invasive metrics have been compared with similar measures recorded from two unipolar atrial electrograms (AEGs). For both ECG and AEG signals, AF organization has been computed by applying a nonlinear regularity index, such as sample entropy (SampEn), to the atrial activity (AA) and to its fundamental waveform, defined as the main atrial wave (MAW). The surface and epicardial f wave amplitude has been estimated through their mean power. Results obtained for 38 patients showed statistically significant correlations between the values measured from surface and invasive recordings, thus corroborating the usefulness of the aforesaid markers in the non-invasive study of AF. Precisely, for AF organization computed from the MAW, the correlation coefficients between surface and both AEGs were R = 0.926 (p < 0.001) and R = 0.932 (p < 0.001). For f wave amplitude, slightly lower significant relationships were noticed, the correlation coefficients being R = 0.765 (p < 0.001) and R = 0.842 (p < 0.001). These outcomes together with interesting linear relationships found among the parameters suggest that AF regularity estimated via SampEn and f wave amplitude can non-invasively characterize the epicardial activity related to AF. © 2011 Institute of Physics and Engineering in Medicine.


Mateo J.,University of Castilla - La Mancha | Joaquin Rieta J.,Synergy Biomedical
Computers in Biology and Medicine | Year: 2013

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. © 2012 Elsevier Ltd.


Alcaraz R.,University of Castilla - La Mancha | Rieta J.J.,Synergy Biomedical
Nonlinear Analysis: Real World Applications | Year: 2010

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 (<50 ms). As an overall result, spontaneous AF termination in 90% of the learning and test recordings was correctly predicted through this novel approach. As a conclusion, this work introduces the application of a non-linear regularity index able to assess significative differences in AA organization from surface ECG recordings during AF. © 2009 Elsevier Ltd. All rights reserved.

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