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[EN] Catheter ablation (CA) is the most popular treatment of atrial fibrillation (AF) with good results in paroxysmal AF, while its efficiency is significantly reduced in persistent AF. With the equipment used for CA strongly depending on electro-gram (EGM) fractionation quantification, the use of a reliable fractionation estimator is crucial to reduce the high recurrence rates in persistent AF. This work introduces a non-linear EGM fractionation quantification technique, which is based on coarse-grained correlation dimension (CGCD) computed over epochs of 1 second. Recordings were firstly normalized, denoised and lowpass filtered. The final CGCD value was calculated by the median CGCD value of all the epochs that a recording consisted of. Results were evaluated on three groups. Groups 1 and 2 contained 24 high-quality and 119 mid-range EGMs, respectively, manually pre-classified by AF types following Wells' criteria, then classified according to their CGCD values. 20 pseudo-real Type IV EGMs formed group 3 that was also automatically classified by AF type. In Groups 1 and 2, classification accuracy was 100% and 84-85.7%, respectively, using 10-fold cross-validation. The receiver-operating characteristics (ROC) analysis for highly fractionated EGMs, showed 100% specificity and sensitivity in Group 1 and 87.5% specificity and 93.6% sensitivity in Group 2. CGCD was always consistent with the fractionation degree of EGMs. 100% of the EGMs in Group 3 were correctly identified as Type IV AF. High accuracy results indicate that the method can estimate precisely the AF Type and detect the existence of AF Type IV cases. Both things are crucial in assisting improved substrate mapping during CA procedures of persistent AF., 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] Algorithms developed so far for the detection of local activation waves (LAWs) in electrograms (EGMs) of atrial fibrillation (AF) analyze each channel of the catheter separately, while dominant reentries of AF are captured by most of the catheter channels simultaneously. This encompasses the risk of losing or oversensing activations in the case of complex fractionated atrial electrograms (CFAEs). The proposed method analyzes multichannel recordings of AF from the least to the most fractionated. Activations are manually annotated and then EGMs are denoised, band-pass filtered by an alternative Bot-teron approach and thresholded. LAW detection is performed sequentially on all the channels by ascending fractionation order. Activations in the first EGM are found by an amplitude-based search, followed by a cycle length (CL) based search. Detection algorithm in the remaining signals is based on the previously detected LAWs, applying a varying search window according to the repetition rate of each activation. A CL-based search is also applied and activation time is computed from the barycenter of each LAW. The final step contains a secondary control that deletes all the low amplitude activations showing low repetition rate, so that any bias regarding the LAW detection of the first EGM is avoided. The method was compared with the equivalent sequential single-channel analysis, with the former showing better accuracy (90.82%) and positive predictive value (96.52%) than the single-channel approach (89.65% and 92.70%, respectively). Multichannel analysis performed a more selective analysis, ignoring some obvious but not repeated LAWs. Consequently, it can localize AF triggers and propagation phenomena more robustly than single channel method., 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] Detailed analysis of the electrocardiogram (ECG) recording allows to obtain relevant information about the heart state and its behavior. Indeed, the P-wave study has proven to be helpful in diagnosing many atrial electrical conduction disorders. However, manual delineation of this wave is a very time-consuming task, and a wide variety of automatic algorithms have been proposed for that purpose. The performance of these methods has been mostly validated on the QT database, since it contains manual annotations from clinical experts and is freely available at PhysioNet. Unfortunately, in the last years several authors have shown some major limitations of this database and this work hence presents a more robust validation of a previously published P-wave delineator. Thus, two novel databases which have been very recently made available to the scienti¿c community have been analyzed. The results show that the methods ability to detect P-waves is close to 100% in stable sinus rhythm episodes, but it decreases to 94% when the heart rhythm is irregular or consecutive P-waves exhibit notable morphological variations. Regarding the error between the annotations obtained automatically and those available as a reference, no relevant differences have been observed in terms of standard deviation, thus suggesting that the algorithm achieves a consistent delineation of the P-waves. Contrarily, a notable error has been noticed in average values, thus highlighting the need of a more extensive training for the P-wave delineator than that offered by the QT database., 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] Objective: This study proposes a reference database, composed of a large number of simulated ECG signals in atrial fibrillation (AF), for investigating the performance of methods for extraction of atrial fibrillatory waves (f -waves). Approach: The simulated signals are produced using a recently published and validated model of 12-lead ECGs in AF. The database is composed of eight signal sets together accounting for a wide range of characteristics known to represent major challenges in f -wave extraction, including high heart rates, high morphological QRST variability, and the presence of ventricular premature beats. Each set contains 30 5 min signals with different f -wave amplitudes. The database is used for the purpose of investigating the statistical association between different indices, designed for use with either real or simulated signals. Main results: Using the database, available at the PhysioNet repository of physiological signals, the performance indices unnormalized ventricular residue (uVR), designed for real signals, and the root mean square error, designed for simulated signals, were found to exhibit the strongest association, leading to the recommendation that uVR should be used when characterizing performance in real signals. Significance: The proposed database facilitates comparison of the performance of different f -wave extraction methods and makes it possible to express performance in terms of the error between simulated and extracted f -wave signals., This work was supported by project DPI2017-83952-C3 of the Spanish Ministry of Economy, Industry and Competitiveness, project SBPLY/17/180501/000411 of the Junta de Comunidades de Castilla-La Mancha, Grant 'Jose Castillejo' (CAS17/00436) from the Spanish Ministry of Education, Culture and Sport, Grant No. BEST/2017/028 from the Education, Research, Culture and Sports Department of Generalitat Valenciana, European Regional Development Fund, and Grant No. 03382/2016 from the Swedish Research Council.

Available online 10 June 2019 This paper is concerned with dynamic and entropy analyses of a hyperchaotic financial system, as well as with its hyperchaos suppression and synchronization. The dynamic behaviour of the system is analyzed for several parameters and initial conditions making use of bifurcation diagrams, Lyapunov exponents and phase portraits. Moreover, entropy from resulting time series is also characterized by estimating ordinal pattern distributions. These analyses have been able to determine and locate accurately chaotic and periodic attractors in the system, thus enabling successful design of its control. In general, financial systems are not always completely synchronized; therefore, some robust synchronization technique should be considered. This study proposes a novel fuzzy disturbance-observer based integral terminal sliding mode control method for the hyperchaotic financial system. The presented control technique guarantees robustness against uncertainties, external disturbances and control input saturation. Fuzzy rules are employed to adaptively tune the gains of the proposed control scheme. Also, the fuzzy inference engine avoids the chattering problem in the system response. Simulation results illustrate the efficient performance of the proposed control technique in presence of dynamic uncertainties, external disturbances and control input saturation.

Recent advances and clinical applications of signal analysis in the characterization of the most common supra-ventricular arrhythmia, i.e. atrial fibrillation (AF), are summarized in this chapter. The analysis of invasive and non-invasive electrocardiographic signals has revealed useful clinical information in a broad variety of scenarios, thus opening new perspectives in the understanding of the currently unknown mechanisms triggering and maintaining the arrhythmia.

[EN] Objective :The most relevant source of signal contamination in the cardiac electrophysiology (EP) laboratory is the ubiquitous powerline interference (PLI). To reduce this perturbation, algorithms including common fixed-bandwidth and adaptive-notch filters have been proposed. Although such methods have proven to add artificial fractionation to intra-atrial electrograms (EGMs), they are still frequently used. However, such morphological alteration can conceal the accurate interpretation of EGMs, specially to evaluate the mechanisms supporting atrial fibrillation (AF), which is the most common cardiac arrhythmia. Given the clinical relevance of AF, a novel algorithm aimed at reducing PLI on highly contaminated bipolar EGMs and, simultaneously, preserving their morphology is proposed. Approach: The method is based on the wavelet shrinkage and has been validated through customized indices on a set of synthesized EGMs to accurately quantify the achieved level of PLI reduction and signal morphology alteration. Visual validation of the algorithm¿s performance has also been included for some real EGM excerpts. Main results: The method has outperformed common filtering-based and wavelet-based strategies in the analyzed scenario. Moreover, it possesses advantages such as insensitivity to amplitude and frequency variations in the PLI, and the capability of joint removal of several interferences. Significance: The use of this algorithm in routine cardiac EP studies may enable improved and truthful evaluation of AF mechanisms., Research supported by grants DPI2017-83952-C3 MINECO/AEI/FEDER, UE and SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha.

An immense body of research has focused on chaotic systems, mainly because of their interesting applications in a wide variety of fields. A comprehensive understanding and synchronization of chaotic systems play pivotal roles in practical applications. To this end, the present study investigates a multi-stable fractional-order chaotic system. Firstly, some dynamical features of the system are described, and the chaotic behaviour of the system is verified. Then, both spectral entropy and spectral Min-Entropy are computed, and the phenomenon of multi-stability is shown. Besides, the combination of a new chattering-free robust sliding mode controller with a neural network observer is proposed for the synchronization of the fractional-order system. With the neural network estimator, unknown functions of the system are obtained, and the effects of disturbances are completely taken into account. Also, based on the Lyapunov stability theorem, the asymptotical stability of the closed-loop system is confirmed. Lastly, the proposed control technique is applied to the fractional-order system. Numerical results demonstrate the chattering-free and effective performance of the proposed control method for uncertain systems in the presence of unknown time-varying external disturbances.

Economic systems, due to their substantial effects on any society, are interesting research subject for a large family of researchers. Despite all attempts to study economic and financial systems, studies on discrete-time macroeconomic systems are rare. Hence, in the current study, we aim to investigate dynamical behavior and synchronization of these systems. At first, the discrete-time mathematical model of the macroeconomic system is presented. Then, the system is studied through topological classification, bifurcation analysis, Lyapunov exponents, and manifold theory, which are powerful tools in the investigation of nonlinear systems. This way, the features of the system are disclosed, and the existence of chaos in the system is shown. For the adequate performance of the economy, the economic systems are desired to operate in a unified manner. To this end, in the present research, a fuzzy based-activation feedback controller is proposed for the synchronization of the system. To enhance the celerity and accuracy of the proposed control for synchronization purposes, it is equipped with a fuzzy logic engine. Finally, the numerical simulations of the synchronization are presented and compared with those of a conventional activation feedback control. Numerical results verify that the proposed control technique can successfully push the states of the response system to the desired value.

Background and Objectives: Atrial fibrillation (AF) is the most common sustained cardiac ar- rhythmia and a growing healthcare burden worldwide. It is often asymptomatic and may appear as episodes of very short duration; hence, the development of methods for its au- tomatic detection is a challenging requirement to achieve early diagnosis and treatment strategies. The present work introduces a novel method exploiting the relative wavelet energy (RWE) to automatically detect AF episodes of a wide variety in length. Methods: The proposed method analyzes the atrial activity of the surface electrocardio- gram (ECG), i.e., the TQ interval, thus being independent on the ventricular activity. To improve its performance under noisy recordings, signal averaging techniques were applied. The meth- od s performance has been tested with synthesized recordings under different AF variable conditions, such as the heart rate, its variability, the atrial activity amplitude or the pres- ence of noise. Next, the method was tested with real ECG recordings. Results: Results proved that the RWE provided a robust automatic detection of AF under wide ranges of heart rates, atrial activity amplitudes as well as noisy recordings. Moreover, the method s detection delay proved to be shorter than most of previous works. A trade-off between detection delay and noise robustness was reached by averaging 15 TQ intervals. Under these conditions, AF was detected in less than 7 beats, with an accuracy higher than 90%, which is comparable to previous works. Conclusions: Unlike most of previous works, which were mainly based on quantifying the irregular ventricular response during AF, the proposed metric presents two major advan- tages. First, it can perform successfully even under heart rates with no variability. Second, it consists of a single metric, thus turning its clinical interpretation and real-time imple- mentation easier than previous methods requiring combined indices under complex classifiers., 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.

In the present work, a new nonequilibrium four-dimensional chaotic jerk system is presented. The proposed system includes only one constant term and has coexisting and hidden attractors. Firstly, the dynamical behavior of the system is investigated using bifurcation diagrams and Lyapunov exponents. It is illustrated that this system either possesses symmetric equilibrium points or does not possess an equilibrium. Rich dynamics are found by varying system parameters. It is shown that the system enters chaos through experiencing a cascade of period doublings, and the existence of chaos is verified. Then, coexisting and hidden chaotic attractors are observed, and basin attraction is plotted. Moreover, using the multiscale C0 algorithm, the complexity of the system is investigated, and a broad area of high complexity is displayed in the parameter planes. In addition, the chaotic behavior of the system is studied by field-programmable gate array implementation. A novel methodology to discretize, simulate, and implement the proposed system is presented, and the successful implementation of the proposed system on FPGA is verified through the simulation outcome. Finally, a robust sliding mode controller is designed to suppress the chaotic behavior of the system. To deal with unexpected disturbances and uncertainties, a disturbance observer is developed along with the designed controller. To show the successful performance of the designed control scheme, numerical simulations are also presented.

Analysis of intra-atrial electrograms (EGMs) nowadays constitutes the most common way to gain new insights about the mechanisms triggering and maintaining atrial fibrillation (AF). However, these recordings are highly contaminated by powerline interference (PLI) due to the large amount of electrical devices operating simultaneously in the electrophysiology laboratory. To remove this perturbation, conventional notch filtering has been widely used. However, this method adds artificial fractionation to the EGMs, thus concealing their accurate interpretation. Hence, the development of novel algorithms for PLI suppression in EGMs is still an unresolved challenge. Within this context, the present work introduces the joint application of common notch filtering and Wavelet denoising for enhanced PLI removal in AF EGMs. The algorithm was validated on a set of 100 unipolar EGM signals, which were synthesized with different noise levels. Original and denoised EGMs were compared in terms of a signed correlation index (SCI), computed both in time and frequency domains. Compared with the single use of notch filtering, improvements between 4 and 15% were reached with Wavelet denoising in both domains. As a consequence, the proposed algorithm was able to efficiently reduce high levels of PLI and simultaneously preserve the original morphology of AF EGMs.

Emotion recognition has become an important area of study for the development of human-machine interfaces able to recognize and interpret human emotions. In order to construct emotional systems, signals from physiological variables have to be registered and processed rapidly to provide a fast emotional response from the computer system to the user. In this regard, several studies have claimed that nonlinear methodologies applied to electroencephalographic signals can provide relevant information about emotions recognition. However, given the multimodal nature and nonlinear behaviour of that signals, the data processing is often very slow to give a fast response, producing an important delay between feeling an emotion and receiving the adequate response from the emotional system. In order to overcome this difficulty, this work computes a modification of quadratic sample entropy accelerating the computation by exploiting vectors with dissimilarity.

Distress has become one of the major issues in developed countries because of its negative effects in physical and mental health. In order to control its consequences, a number of researchers have studied distress from an electroencephalographic point of view by means of the use of different nonlinear metrics. However, those studies are only based on non-lag approaches, thus many nonlinear dynamics of brain signals could not be properly assessed. In this sense, this work applies a multilag extension of a nonlinear regularity-based metric called quadratic sample entropy, in order to check the influence of the selection of a time lag for the recognition of distress with electroencephalographic recordings.

Automatic identification of negative stress is an unresolved challenge that has received great attention in the last few years. Many studies have analyzed electroencephalographic (EEG) recordings to gain new insights about how the brain reacts to both short- and long-term stressful stimuli. Although most of them have only considered linear methods, the heterogeneity and complexity of the brain has recently motivated an increasing use of nonlinear metrics. Nonetheless, brain dynamics reflected in EEG recordings often exhibit a multiscale nature and no study dealing with this aspect has been developed yet. Hence, in this work two nonlinear indices for quantifying regularity and predictability of time series from several time scales are studied for the first time to discern between visually elicited emotional states of calmness and negative stress. The obtained results have revealed the maximum discriminant ability of 86.35% for the second time scale, thus suggesting that brain dynamics triggered by negative stress can be more clearly assessed after removal of some fast temporal oscillations. Moreover, both metrics have also been able to report complementary information for some brain areas.

[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.

[EN] Objective: The electrocardiogram (ECG) is currently the most widely used recording to diagnose cardiac disorders, including the most common supraventricular arrhythmia, such as atrial fibrillation (AF). However, different types of electrical disturbances, in which power-line interference (PLI) is a major problem, can mask and distort the original ECG morphology. This is a significant issue in the context of AF, because accurate characterization of fibrillatory waves (f-waves) is unavoidably required to improve current knowledge about its mechanisms. This work introduces a new algorithm able to reduce high levels of PLI and preserve, simultaneously, the original ECG morphology. Approach: The method is based on stationary wavelet transform shrinking and makes use of a new thresholding function designed to work successfully in a wide variety of scenarios. In fact, it has been validated in a general context with 48 ECG recordings obtained from pathological and non-pathological conditions, as well as in the particular context of AF, where 380 synthesized and 20 long-term real ECG recordings were analyzed. Main results: In both situations, the algorithm has reported a notably better performance than common methods designed for the same purpose. Moreover, its effectiveness has proven to be optimal for dealing with ECG recordings affected by AF, sincef-waves remained almost intact after removing very high levels of noise. Significance: The proposed algorithm may facilitate a reliable characterization of thef-waves, preventing them from not being masked by the PLI nor distorted by an unsuitable filtering applied to ECG recordings with AF., Research supported by grants DPI2017-83952-C3 MINECO/AEI/FEDER, UE and SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha.

This chapter reviews different approaches to f wave characterization. The two fundamental characteristics f wave amplitude and atrial fibrillatory rate are first considered, followed by a description of linear and nonlinear techniques for characterizing wave morphology and regularity. The analysis of spatial ECG information, manifested as a vectorcardiographic loop or a body surface potential map, is reviewed. The chapter concludes with a brief overview of popular clinical applications where the described approaches have been explored.

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.

Catheter ablation (CA) is nowadays the first-line therapy for the treatment of atrial fibrillation (AF). This approach relies heavily on cardiac mapping systems, which provide intra-cardiac electrograms (EGMs) to study the heart's electrical activity. To get precise clinical information from these signals, the elimination of noise and nuisance interferences, as well as the preservation of their original waveform, are key aspects to treat carefully. However, how they should be preprocessed to remove successfully high-frequency noise has only received limited attention to date. Hence, in this study the most commonly bandpass filtering applied to this recording is compared with two advanced denoising methods, based on Wavelet Transform (WT) and Empirical Mode Decomposition (EMD), on a set of 150 synthesized bipolar EGMs with different levels of noise. The resulting signals have been contrasted with the original ones in terms of a signed correlation index (SCI), computed both from time and frequency domains. Thus, notably more significant alterations in the original waveform have been observed for the regular filtering than for the WT-based approach. As a conclusion, this method is a more interesting option than the regular filtering to preserve the morphology of the bipolar EGM and obtain more accurate electrophysiological information.

Complex fractionated atrial electrograms (CFAEs) are electrograms (EGMs) characterized by a high variability both in amplitude and waveforms, making the estimation of their cycle length (CL) a difficult task to perform. The CL is a widely employed parameter for the characterization of the electrical activity within the atria, thus serving to guide catheter ablation, one of the most effective cardiac procedures for the treatment of AF nowadays. The present study aims to compare the performance of three different methods estimating the CL from a set of 50 recordings with CFAEs previously annotated by two expert physicians. The first algorithm is based on an adaptive amplitude threshold, while the second method performs the detection of the highest activations and a later detection of peaks within intervals longer than 1.5 times the median cycle length of the EGM. Finally, we introduce a novel hybrid method which performs a main amplitude detection, which is facilitated by the equalization of high and low amplitude activations in CFAEs according to a fractionation parameter, and a subsequent search for lower activations within intervals longer than the median CL, decreasing the amplitude threshold proportionally to the intervals' length. Our method outperformed the other analyzed detectors, which confirms that the method proposed is a more accurate estimator of atrial CL for CFAEs.

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 manual annotations, the PhysioNet QT database (QTDB) has been widely used as the reference of ECG delineators. However, a significant percentage of its annotations have been reported as inaccurate. Thus, any precise ECG delineator will never be able to meet, without error, all its annotations. The present work analyzes these inaccuracies and also how noise altered the final timing of annotations. As this effect is higher for low amplitude waveforms, P-waves were studied through a robust P-wave delineator. Its delineation results were compared with manual annotations under two scenarios. Firstly, a direct comparison without ECG denoising was performed. Secondly, the P-waves were delineated after efficient Wavelet-based denoising. Results showed that automatic annotations were closer to manual annotations for noisy ECGs and farther in the case of denoised ECGs, thus proving that noise altered the timing of manual annotations. An unreal improvement in delineation performance for noisy ECGs was obtained for P-wave onset, peak and offset in 45.83%, 57.29% and 56.25% of the recordings, respectively. Thus, to improve delineators reliability, either the need to review the QTDB annotations or its replacement by a better annotated database are suggested.

The study of the P-wave on the electrocardiogram is essential in the characterization of atrial conduction defects that may anticipate cardiac pathologies, such as atrial fibrillation. This evidence exhibits the need to develop reliable methods for accurate automatic delineation of P-waves. Many different strategies for delineating P-waves have been introduced. Nonetheless, they all share the same principle of smoothing aggressively the P-wave pattern to facilitate its delineation. However, that strategy may provoke morphological alterations in the P-wave under study that could lead to inaccurate delineation. Alternatively, the present work introduces a new delineation strategy grounded on the generation of a Gaussian model of the P-wave under study to assist its delineation and an adaptive slope threshold that takes into account the morphology of the preceding P-waves. The method was validated using the annotated QT database from Physionet. Delineation results provided a detection sensitivity of 100%, whereas the mean and standard deviation of the delineation error for the P-wave onset, peak and offset were 4.71 ± 9.59 ms, 2.82 ± 6.69 ms and 0.6 ± 9.79 ms, respectively. These results demonstrate that the proposed strategy provides accurate delineation of P-waves that outperforms others presented in the literature, in particular in terms of stability.

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.

The atrial electrical activity analysis is nowadays playing a key role to improve current knowledge about the mechanisms triggering and maintaining atrial arrhythmias, such as atrial fibrillation (AF). Indeed, the intra-atrial electrograms (EGMs) provide essential information to guide the prevailing cornerstone treatment of AF, i.e., catheter ablation. Bearing in mind that cardiac electro-physiology laboratories are plenty of technology, such a kind of operating rooms are a very adverse environment when it comes to avoid power-line interference in EGM recordings. However, the reduction ofthis nuisance signal deserves more attention. Thus, aimed at reducing powerline noise but, at the same time, preserving the original EGM morphology, a novel algorithm based on the stationary Wavelet transform (SWT) is proposed. To validate the method, 150 bipolar EGMs with 10 seconds in length have been synthesized with different noise levels. The original and denoised EGMs have then been compared by means of an adaptive signed correlation index (ASCI), computed both in time and frequency domains. The obtained results have shown improvements between 9% and 17% for the proposed method regarding a reference notch filtering. Consequently, this new algorithm may enable more reliable and truthful further analyses of atrial EGMs.

Atrial fibrillation (AF) electrograms (EGMs) present high heterogeneity of morphologies and amplitudes that turns the detection of their local activation waves (LAWs) a very hard task to perform. In this study, a novel fractionation-based LAW detector for bipolar EGMs is introduced. The method modifies traditional Botteron's band-pass filtering, decreasing its low cut-off frequency from 40 to 20 Hz, thus benefiting slow local activations detection. Furthermore, high and low amplitude activations in complex fractionated atrial electrograms (CFAEs) are equalized, thus facilitating the detection of low amplitude activations. A minimum refractory period of 50 ms is imposed between activations, however, additional minor activations are later sought for intervals longer than the median cycle length. All the LAWs from a set of 40 real bipolar EGMs, mostly CFAEs, were manually annotated by expert physicians and served to evaluate performance. Detections closer than 40 ms to a manual annotation were considered as correct. Detection results provided 95.41%, 92.13% and 96.37% in Sensitivity, Accuracy and Precision for CFAEs, respectively, whereas for less fractionated EGMs they were 100% in any case. Therefore, the new LAW detector has provided robust performance even under highly fractionated EGMs.

Development of algorithms for automatic detection of emotions is essential to improve affective skills of human-computer interfaces. In the literature, a wide variety of linear methodologies have been applied with the aim of defining the brain’s performance under different emotional states. Nevertheless, recent findings have demonstrated the nonlinear and dynamic behavior of the brain. Thus, the use of nonlinear analysis techniques has notably increased, reporting promising results with respect to traditional linear methods. In this sense, this work presents a review of the latest advances in the field, exploring the main nonlinear metrics used for emotion recognition from EEG recordings.

Automatic recognition of emotions is an emerging field, because it plays a key role to improve current affective human-computer interactions. Although for that purpose a variety of linear methods have been applied to the electroencephalographic (EEG) recording, nonlinear analysis has recently revealed novel and more useful insights about the brain behavior under different emotional states. This work briefly reviews the main progresses in this context, also highlighting the main challenges that will have to be mandatory tackled in future.

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.

The electroencephalogram (EEG) is the most common tool used to study mental disorders. In the last years, the use of this recording for recognition of negative stress has been receiving growing attention. However, precise identification of this emotional state is still an interesting unsolved challenge. Nowadays, stress presents a high prevalence in developed countries and, moreover, its chronic condition often leads to concomitant physical and mental health problems. Recently, a measure of time series irregularity, such as quadratic sample entropy (QSEn), has been suggested as a promising single index for discerning between emotions of calm and stress. Unfortunately, this index only considers repetitiveness of similar patterns and, hence, it is unable to quantify successfully dynamics associated with the data temporal structure. With the aim of extending QSEn ability for identification of stress from the EEG signal, permutation entropy (PEn) and its modification to be amplitude-aware (AAPEn) have been analyzed in the present work. These metrics assess repetitiveness of ordinal patterns, thus considering causal information within each one of them and obtaining improved estimates of predictability. Results have shown that PEn and AAPEn present a discriminant power between emotional states of calm and stress similar to QSEn, i.e., around 65%. Additionally, they have also revealed complementary dynamics to those quantified by QSEn, thus suggesting a synchronized behavior between frontal and parietal counterparts from both hemispheres of the brain. More precisely, increased stress levels have resulted in activation of the left frontal and right parietal regions and, simultaneously, in relaxing of the right frontal and left parietal areas. Taking advantage of this brain behavior, a discriminant model only based on AAPEn and QSEn computed from the EEG channels P3 and P4 has reached a diagnostic accuracy greater than 80%, which improves slightly the current state of the art. Moreover, because this classification system is notably easier than others previously proposed, it could be used for continuous monitoring of negative stress, as well as for its regulation towards more positive moods in controlled environments.

Nowadays, electroencephalographic (EEG) recordings receive increasing attention in the field of emotions recognition with physiological variables. Moreover, the nonlinear nature of EEG signals suggests that nonlinear techniques could be more suitable than linear methodologies for the assessment of mental processes triggered under different emotions. One of the most relevant states is distress (the negative aspect of stress), because of its enormous influence in developed countries and its countless adverse effects in health. As a result, many researches have shown their interest in distress in the last few years. In the present study, a predictability-based entropy measure called amplitude-aware permutation entropy (AAPE) was applied to discern between calm and distress states. EEG signals from 32 channels were individually assessed to obtain the discriminatory ability of each single electrode. After that, only 2 out of 32 EEG channels were combined in a logistic regression model, reaching a global classification accuracy over 73%.

Nowadays, emotional intelligence plays a key role in improving human-machine interaction (HMI). The main objective of HMI is to fill the gap between human emotional states and the reaction of a computer in accordance with this feeling. However, there is a lack of mathematical emotional models to implement affective computing systems into real applications. Consequently, this paper explores the properties of the nonlinear methodology based on Quadratic Sample Entropy (QSE) for the recognition of different emotional subspaces. Precisely, 665 segments of 32-channel electroencephalographic recordings from 32 subjects elicited with different emotional stimuli have been analyzed to validate the proposed model. Results conclude that QSE is a promising feature to be taken into account. Indeed, this metric has reported a discriminant ability around 72% using a support vector machine classifier. This result is comparable with the outcomes reported by other more complex methodologies which use multi-parametric analysis.

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.

Prediction of defibrillation success is of vital importance to guide therapy and improve the survival of patients suffering out-of-hospital cardiac arrest (OHCA). Currently, the most efficient methods to predict shock success are based on the analysis of the electrocardiogram (ECG) during ventricular fibrillation (VF), and recent studies suggest the efficacy of waveform indices that characterize the underlying non-linear dynamics of VF. In this study we introduce, adapt and fully characterize six entropy indices for VF shock outcome prediction, based on the classical definitions of entropy to measure the regularity and predictability of a time series. Data from 163 OHCA patients comprising 419 shocks (107 successful) were used, and the performance of the entropy indices was characterized in terms of embedding dimension (m) and matching tolerance (r). Six classical predictors were also assessed as baseline prediction values. The best prediction results were obtained for fuzzy entropy (FuzzEn) with m = 3 and an amplitude-dependent tolerance of r = 80 μV. This resulted in a balanced sensitivity/specificity of 80.4%/76.9%, which improved by over five points the results obtained for the best classical predictor. These results suggest that a FuzzEn approach for a joint quantification of VF amplitude and its non-linear dynamics may be a promising tool to optimize OHCA treatment., This work received financial support from Spanish Ministerio de Economia y Competitividad, projects TEC2013-31928 and TEC2014-52250-R, and jointly with the Fondo Europeo de Desarrollo Regional (FEDER), project TEC2015-64678-R; from Junta de Comunidades de Castilla La Mancha, project PPII-2014-026-P; and from UPV/EHU through the grant PIF15/190 and through its research unit UFI11/16.

Recognition of emotions is still an unresolved challenge, which could be helpful to improve current human-machine interfaces. Recently, nonlinear analysis of some physiological signals has shown to play a more relevant role in this context than their traditional linear exploration. Thus, the present work introduces for the first time the application of three recent entropy-based metrics: sample entropy (SE), quadratic SE (QSE) and distribution entropy (DE) to discern between emotional states of calm and negative stress (also called distress). In the last few years, distress has received growing attention because it is a common negative factor in the modern lifestyle of people from developed countries and, moreover, it may lead to serious mental and physical health problems. Precisely, 279 segments of 32-channel electroencephalographic (EEG) recordings from 32 subjects elicited to be calm or negatively stressed have been analyzed. Results provide that QSE is the first single metric presented to date with the ability to identify negative stress. Indeed, this metric has reported a discriminant ability of around 70%, which is only slightly lower than the one obtained by some previous works. Nonetheless, discriminant models from dozens or even hundreds of features have been previously obtained by using advanced classifiers to yield diagnostic accuracies about 80%. Moreover, in agreement with previous neuroanatomy findings, QSE has also revealed notable differences for all the brain regions in the neural activation triggered by the two considered emotions. Consequently, given these results, as well as easy interpretation of QSE, this work opens a new standpoint in the detection of emotional distress, which may gain new insights about the brain’s behavior under this negative emotion.

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.

Developed countries are experiencing a dramatic increase in population ageing. Moreover, it is well known that elderly prefer stay at their homes over other options, increasing this way the medical expenditures. This also involves a major impact on the social and economic balance of the countries. Consequently, an important number of works have been carried out to improve the elderly quality of life and reduce the healthcare costs. However, few efforts have been made in monitoring the mental and emotional states of the ageing adults. This paper introduces a new approach based on the dynamic quadratic entropy for distinguishing different arousal levels. 278 one-minute-length EEG recordings from Dataset for Emotion Analysis using Physiological Signals are used in this study. The results show a decreasing brain complexity under excitement stimuli in central and parietal areas. These findings could be useful to train an emotional system for recognizing some basics emotions.

Nowadays, assistive technologies together with ubiquitous and pervasive computing are emerging as main alternative to help ageing population. In this respect, an important number of works have been carried out to improve the quality of life in elderly from a physical point of view. However, less efforts have been made in monitoring the mental and emotional states of the elderly. This work presents a non-linear model for discriminating different arousal levels through quadratic entropy and a decision tree-based algorithm. Two hundred and seventy eight EEG recordings lasting one minute each were used to train the proposed model. The recordings belong to the Dataset for Emotion Analysis using Physiological signals (DEAP). In agreement with the complexity and variability observed in other works, our results report a low quadratic entropy when subjects face high arousal stimuli. Finally, the model achieves a global performance around 70 % when discriminating between calm and excitement events.

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.

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.

Introduccion y objetivos Mediante procesado de senal se puede analizar la actividad auricular en fibrilacion auricular (FA) desde los registros de superficie y obtener una informacion muy proxima a la de los registros intracavitarios. El objetivo del trabajo es estudiar las modificaciones electrocardiograficas en la FA durante el intervalo previo a su conversion espontanea a ritmo sinusal. Metodos De 50 pacientes, se selecciono un episodio de FA paroxistica de mas de 2 h. De cada episodio se analizo el ultimo minuto antes de la reversion a ritmo sinusal (grupo S) y el minuto central (grupo N). Se comparo a los dos grupos analizando la respuesta ventricular, la morfologia de ondas f y el analisis frecuencial y de organizacion de la actividad auricular aplicando procesado de senal. Resultados La variabilidad RR fue un parametro discriminativo, aunque con poca especificidad (grupo S, 630 ± 200 ms; grupo N, 740 ± 100 ms; p = 0,034). El analisis morfologico de ondas f no mostro diferencias significativas. El grupo S presento una frecuencia auricular dominante media significativamente menor (grupo S, 5,01 ± 0,671 Hz; grupo N = 6,514 ± 0,804 Hz; p = 4,55 × 10−9). La medida de organizacion de la actividad auricular fue significativamente mayor en el grupo S (grupo S, 0,071 ± 0,012; grupo N, 0,103 ± 0,013; p = 6,73 × 10−12). Conclusiones El minuto electrocardiografico previo a la conversion espontanea de la FA muestra un proceso de organizacion de la actividad auricular dificilmente apreciable mediante analisis morfologico de las ondas f, aunque predecible mediante procesado de senal.

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.

Phase Mapping (PhM) guides catheter ablation of long-standing rotors in atrial fibrillation (AF) by applying the Hilbert transform (HT) to electrogram (EGM) potential maps (PM). However, no stable rotors have been reported by other works with high density PM of the fibrillating human atria. This work studies how PhMs transform PMs due to preprocessing, interpolation and the application of the HT. A biophysical 3D model of the human atria simulated a 3.5 seconds AF episode with 5.594 EGM recordings. Three sets of PhMs were generated: a) By direct application of the HT to PMs; b) Achieving high-density by the interpolation of a simulated 64 poles basket catheter; c) preprocessing the basket catheter with low-pass filtering at 30 Hz and 2 Hz band-pass filtering centered on the dominant frequency. PhMs from a rotor location were compared with their corresponding PMs through the Earth Mover's Distance algorithm. An average concordance of 89.26%±6.24% was obtained for case a), lowering down to 72.26%±11.24% for case b) and being 34.43%±7.81% for case c). Hence, PhMs may notably transform real PMs creating fibrillatory activity that may not be observed in high-density potential maps.