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Catheter ablation (CA) is a commonly used treatment for persistent atrial fibrillation (AF). Since its medium/long-term success rate remains limited, preoperative prediction of its outcome is gaining clinical interest to optimally select candidates for the procedure. Among predictors based on the surface electrocardiogram, the dominant frequency (DF) and harmonic exponential decay (γ) of the fibrillatory waves (f-waves) have reported promising but clinically insufficient results. Hence, the main goal of this work was to conduct a broader analysis of the f-wave harmonic spectral structure to improve CA outcome prediction through several entropy-based measures computed on different frequency bands. On a database of 151 persistent AF patients under radio-frequency CA and a follow-up of 9 months, the newly introduced parameters discriminated between patients who relapsed to AF and those who maintained SR at about 70%, which was statistically superior to the DF and approximately similar to γ. They also provided complementary information to γ through different combinations in multivariate models based on lineal discriminant analysis and report classification performance improvement of about 5%. These results suggest that the presence of larger harmonics and a proportionally smaller DF peak is associated with a decreased probability of AF recurrence after CA.

Traditionally, the brain has been studied as an ensemble of independent structures with determined functions. However, it has been demonstrated that the brain operates as a network in which all regions are interconnected. Apart from physical links, the brain presents functional associations between non-physically connected regions that work synchronized in a common mental process. For this reason, the study of functional connectivity is essential to reveal new insights about brain’s behavior. In this work, a nonlinear functional connectivity metric called cross-sample entropy is applied for the first time to emotions recognition. Concretely, it has been computed for the detection of distress because of being one of the most influencing emotions in developed societies with several negative implications for health. Results reveal a strong coordinated activity between channels in central, parietal and occipital areas in each brain hemisphere separately, and also in the inter-hemispheric interactions among the same regions. Moreover, an augmented amount of similar dynamics under distress conditions in all brain regions with respect to a calm state reveals an increase in self-coordination of brain activity in distressful situations.

Contribution: An improved inverted lecturing (IIL) framework based on blending flipped lectures and hands-on experiments provides instructional benefits, compared to traditional teaching (TT) and inverted lecturing (IL), in an introductory course in digital systems. Background: IL has proven more effective than TT in improving student learning in engineering courses, but has mostly been used for theory sessions. The impact of combining inverted lectures and hands-on experiments on student learning has not still been thoroughly assessed in engineering courses. Intended Outcomes: Attendance, marks, and satisfaction should improve for students in IL-based theory lectures, compared with those receiving TT, and should improve still further for students receiving the IIL-based method. Workload both for student and instructor should not increase significantly. Application Design: The three methods were compared in six consecutive offerings of the course. In the first two, TT was used for both theory and laboratory classes; in the next two, IL was used for theory lectures; and in the final two offerings the IIL-based scheme was used. The instructor, intended learning outcomes (ILOs), course syllabus, and student grading scheme were constant over the six semesters. A total of 184 students with similar backgrounds participated. Findings: Students under the IL- and IIL-based frameworks were more engaged than those receiving TT, and were more satisfied with their learning process. The IIL-based learners achieved the deepest conceptual understanding. Finally, the IL- and IIL-based methods did not significantly increase workload for either the students or the instructor.

[EN] Since the discovery of pulmonary veins (PVs) as foci of atrial fibrillation (AF), the commonest cardiac arrhythmia, investigation revolves around PVs catheter ablation (CA) results. Notwith-standing, CA process itself is rather neglected. We aim to decompose crucial CA steps: coronary sinus (CS) catheterization and the impact of left and right PVs isolation (LPVI, RPVI), separately. We recruited 40 paroxysmal AF patients undergoing first-time CA and obtained five-minute lead II and bipolar CS recordings during sinus rhythm (SR) before CA (B), after LPVI (L) and after RPVI (R). Among others, duration, amplitude and atrial-rate variability (ARV) were calculated for P-waves and CS local activation waves (LAWs). LAWs features were compared among CS channels for reliability analysis. P-waves and LAWs features were compared after each ablation step (B, L, R). CS channels: amplitude and area were different between distal/medial (p = 93%) than distal (+73.12%, p, This research has received partial financial support from public grants DPI2017-83952-C3, PID2021-00X128525-IV0, PID2021-123804OB-I00 and TED2021-129996B-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund (EU), SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2021/286 from Generalitat Valenciana.

The human immunodeficiency virus (HIV), as one of the most hazardous viruses, causes destructive effects on the human bodies’ immune system. Hence, an immense body of research has focused on developing antiretroviral therapies for HIV infection. In the current study, we propose a new control technique for a fractional-order HIV infection model. Firstly, a fractional model of the HIV model is investigated, and the importance of the fractional-order derivative in the modeling of the system is shown. Afterward, a type-2 fuzzy logic controller is proposed for antiretroviral therapy of HIV infection. The developed control scheme consists of two individual controllers and an aggregator. The optimal aggregator modifies the output of each individual controller. Simulations for two different strategies are conducted. In the first strategy, only reverse transcriptase inhibitor (RTI) is used, and the superiority of the proposed controller over a conventional fuzzy controller is demonstrated. Lastly, in the second strategy, both RTI and protease inhibitors (PI) are used simultaneously. In this case, an optimal type-2 fuzzy aggregator is also proposed to modify the output of the individual controllers based on optimal rules. Simulations results demonstrate the appropriate performance of the designed control scheme for the uncertain system.

The brain has been typically assessed as a group of independent structures focused on the realization of determined processes separately. Nevertheless, recent findings have confirmed the existence of interconnections between all brain regions, thus demonstrating that the brain works as a network. These areas can be interconnected either physically, by anatomical links, or functionally, through functional associations created for a coordinated development of mental tasks. In this sense, the assessment of functional connectivity is crucial for discovering new information about the brain’s behavior in different scenarios. In the present study, the nonlinear functional connectivity metric cross-sample entropy (CSE) is applied in the research field of emotions recognition from EEG recordings. Concretely, CSE is computed to discern between four different emotional states. The results obtained indicated that the strongest coordination appears in intra- and inter-hemispheric interactions of central, parietal and occipital brain regions, whereas associations between left frontal and temporal lobes with the rest of areas show the most dissimilar dynamics, thus a higher uncoordinated activity. In addition, coordination is globally higher under emotional conditions of high arousal/low valence (like fear or distress) and low arousal/high valence (such as relaxation or calmness).

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

[EN] Many indices aimed at discriminating between paroxysmal and persistent atrial fibrillation (ParAF vs. PerAF) have been previously studied and assessed via statistical tests in order to suggest optimized approaches to catheter ablation (CA) of AF. However, clinicians demand the use of simple classification methods of straightforward comprehension. The present work exploits AF cycle length (AFCL), dominant frequency (DF), sample entropy (SE) and determinism (DET) of recurrent quantification analysis, applied to AF recordings of complex fractionated atrial electrograms (CFAEs), aimed at creating straightforward models to discriminate between ParAF and PerAF. AFCL and DF were calculated on the full AF recordings, whereas SE and DET were computed on segments of 1, 2 and 4s. First, correlation matrix filters removed redundant information and Random Forests made a ranking of variables by relevance. Next, coarse tree classificators were created, combining optimally high-ranked indexes which were tested with leave-one-out cross-validation. After analyzing all the possible combinations of highly ranked features, the best classification performance provided an Accuracy (Acc) of 88.2% to discriminate ParAF from PerAF, while DET provided the highest single Acc of 82.4%. As conclusion, the careful selection of limited sets of indices feeding straightforward classificators are able to discriminate accurately between CFAEs of ParAF and PerAF., 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] Pulmonary vein isolation is nowadays the cornerstone for the treatment of paroxysmal atrial fibrillation (AF). However, its success rate is not as high as clinically desirable, and repeated procedures are often required. In this context, the development of noninvasive, pre-ablation predictors of the procedure failure is essential to enable tailored approaches. For that purpose several P-wave features have been analyzed from short electrocardiogram (ECG) recordings, but their time variability has not been studied yet. Hence, this work explores the ability of time variability of two common P-wave parameters, such as duration and amplitude, to anticipate cryoablation failure. Before ablation, a standard 12-lead ECG was recorded continuously for 5 minutes from 45 paroxysmal AF patients. All P-waves in lead II were delineated with an automatic algorithm. Duration and amplitude of all waves were computed, and then mean, standard deviation (std), and coefficient of variation (CV) were obtained for both parameters and correlated with ablation outcome after a follow-up of 9 months. The CV for both P-waves features reported the best separation between the patients who maintained sinus rhythm and those who relapsed to AF. Moreover, improvements about 10% in diagnostic ability were obtained by the CV with regards to the mean, reaching values about 70%. These results suggest that time variability of the P-wave can provide new insights about atrial conduction heterogeneity, which could be helpful in improving the selection strategy of optimal candidates for cryoablation., This research has been supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-la Mancha and AICO/2019/036 from Generalitat Valenciana.

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

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

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

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

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

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

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

[EN] The analysis of coronary sinus (CS) electrograms (EGMs) during catheter ablation (CA) of atrial fibrillation (AF) is highly important for AF substrate evaluation. However, channels of the CS catheter may be affected by vigorous cardiac movement and bad contact. This work investigates the most reliable channels in preserving the AF dynamics during sinus rhythm (SR). Local activation waves (LAWs) were detected in 44 bipolar CS recordings of 60-300 seconds duration in 28 paroxysmal AF patients undergoing CA. Recordings consisted of five channels: distal, mid-distal, medial, mid-proximal and proximal. LAW duration, amplitude, area and correlation between dominant morphologies of each channel were calculated. Multichannel comparison and analysis in pairs of channels were performed using KruskalWallis and Mann-Whitney U-test, respectively. The latter, with Bonferroni correction, was also used for comparison between one and the remaining channels. Median values were calculated. Distal channel presented the longest duration (p = 0.047) and the lowest amplitude LAWs (pmax, 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] Obstructive sleep apnea (OSA) is a respiratory syndrome of high incidence in the general population and correlated with some cardiovascular diseases. Several techniques have been proposed in the last decades to find a surrogate method to polysomnography (PSG), the gold standard for the diagnosis of OSA. The present study comprises an experimental review on the state-of-the-art methods for OSA detection through the public Apnea-ECG database, which is available at PhysioNet. Precisely, traditional time-frequency domain features were extracted from the heart rate variability (HRV) signal, together with some common complexity measures. Given their ability to deal with real-world time series, two additional entropy-based measures were also tested, i.e., Rènyi and Tsallis entropies. Moreover, univariate and multivariate classifiers were applied, including diagnostic test, support vectors machine, and k-nearest neighbors. Ultimately, two sequential feature selection (SFS) algorithms were employed to reduce the computational cost of the resulting discriminant models. The major findings reported that multivariate classifiers reached similar results to those found in the literature. Moreover, univariate classification results suggested that the frequency domain features provided the best OSA detection, although a well-known entropy index also obtained a good performance., This research has been supported by grants DPI2017-83952- C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/ 000411 from Junta de Comunidades de Castilla-la Mancha and AICO/2019/036 from Generalitat Valenciana. Moreover, Daniele Padovano has held graduate research scholarships from Escuela Politecnica de Cuenca and Instituto de Tecnolog ¿ ¿¿as Audiovisuales, University of Castilla-La Mancha

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

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

A novel approach to solve optimal control problems dealing simultaneously with fractional differential equations and time delay is proposed in this work. More precisely, a set of global radial basis functions are firstly used to approximate the states and control variables in the problem. Then, a collocation method is applied to convert the time-delay fractional optimal control problem to a nonlinear programming one. By solving the resulting challenge, the unknown coefficients of the original one will be finally obtained. In this way, the proposed strategy introduces a very tunable framework for direct trajectory optimization, according to the discretization procedure and the range of arbitrary nodes. The algorithm&rsquo, s performance has been analyzed for several non-trivial examples, and the obtained results have shown that this scheme is more accurate, robust, and efficient than most previous methods.

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

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

[EN] The effects of sleep-related disorders, such as obstructive sleep apnea (OSA), can be devastating either in children or adults. Misdiagnosis may lead to severe cardiovascular diseases. Besides, OSA consequences are often related to bad job performance, and road accidents. Nowadays, polysomnography (PSG) is still considered the gold standard for OSA diagnosis, but the required facilities are extremely high, thus reducing availability worldwide. For this reason, simpler and cost-effective diagnosing methods have been proposed in the late years. In this regard, the heart rate variability (HRV) has been demonstrated to strongly reflect apnea episodes during sleep. Hence, this work reviews the latest advances in the evaluation of OSA from the HRV perspective to consider its potentialities for a future revisited CinC Challenge., This research has been supported by grants DPI201783952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-la Mancha and AICO/2019/036 from Generalitat Valenciana. Moreover, Daniele Padovano has held graduate research scholarships from Escuela Polit ' ecnica de Cuenca and Instituto de Tecnolog ' ias Audiovisuales, University of CastillaLa Mancha

Negative stress, also named distress, is nowadays one of the most studied emotional states due to its high impact on advanced societies. Its automatic identification from physiological recordings can be extremely useful to prevent concomitant physical health problems as well as other mental disorders. However, this task is still an unresolved challenge. Recently, quadratic sample entropy (QSE) applied to the electroencephalogram signal has proven to be the most promising single metric to discern between emotional states of calm and negative stress. This index estimates repetitive patterns in a time series without considering time data order within each one, thus ignoring some relevant dynamics. Hence, in this work conditional entropy (CEn) and its correction (CCEn) are studied to complement and improve QSE ability in detection of distress. Both CEn and CCEn symbolize original time series to consider ordinal patterns and, thus, quantify accurately data predictability. Results obtained from 279 samples (146 associated with calm and 133 to distress) provide a similar discriminant power, about 65%, both for conditional entropies and QSE. More interestingly, these metrics also reveal the presence of complementary brain dynamics under a emotional state of distress. Thus, CCEn and QSE suggest a synchronization between opposite frontal and parietal brain regions from both hemispheres, such that when the level of negative stress increases, a more irregular activity is found in left frontal and right parietal areas. Simultaneously, more predictable activity is noticed in right frontal and left parietal regions. These findings agree with previous neurophysiological studies and allow to improve the identification of distress. Precisely, a very simple discriminant model based on CCEn and QSE is able to discern more than 80% of samples, thus overcoming slightly the results reported by previous works, where dozens or hundreds of variables have to be combined with advanced classifiers.

Negative stress, or distress, represents a serious problem in advanced societies given its adverse consequences for health. Many studies have focused on the detection of distress from physiological signals such as the electroencephalogram (EEG). To this respect, the combination of regularity-based quadratic sample entropy (QSampEn) and symbolic amplitude-aware permutation entropy (AAPE) has reported valuable outcomes in distress recognition. In the present work, the recently introduced symbolic metric called dispersion entropy (DispEn) is applied for the first time to the same problem. Statistically significant results reported by the single metric have demonstrated its capability for calm and distress detection. Furthermore, relevant differences have been found between the combination of QSampEn with either AAPE or DispEn, finding that the assessment of ordinal and dispersion patterns leads to distinct and complementary outcomes. Finally, the combination of the three entropy metrics has considerably overcome the results ever reported by other indices in similar studies.

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