Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms

Atrial fibrillation, which is the most common cardiac arrhythmia, is typically classified into four clinical subtypes: paroxysmal, persistent, long-standing persistent and permanent. The ability to distinguish between them is of crucial significance in choosing the most suitable therapy for each patient. Nevertheless, classification is currently established once the natural history of the arrhythmia has been disclosed as it is not possible to make an early differentiation. This paper presents a novel method to discriminate persistent and long-standing atrial fibrillation patients by means of a time-frequency analysis of the surface electrocardiogram. Classification results provide approximately 75% accuracy when evaluating ECGs of consecutive unselected patients from a tertiary center and higher than 80% when patients are not under antiarrhythmic treatment or do not have structural heart disease (76% sensitivity and 88% specificity). Moreover, to our knowledge, this is the first study that discriminates between persistent and long-standing persistent subtypes in a heterogeneous population sample and without discontinuing antiarrhythmic therapy to patients. Thus, it can help clinicians to address the most suitable therapeutic approach for each patient.This work was supported by Generalitat Valenciana under grant PrometeoII/2013/013 and by MINECO under grants MTM2010-15200, MTM2013-43540-P.Ortigosa, N.; Fernández, C.; Galbis, A.; Cano, O. (2016). Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms. Biomedical Engineering / Biomedizinische Technik. 61(1):19-27. https://doi.org/10.1515/bmt-2014-0154S192761

Implementação de um sistema de análise automática do ECG para identificação de episódios de fibrilação atrial utilizando uma plataforma de aquisição BITalino® e um smartphone Android™

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáAs arritmias cardíacas são distúrbios que afetam a frequência e/ou o ritmo dos batimentos cardíacos. O diagnóstico da maioria das arritmias é feito através da análise do eletrocardiograma (ECG), o qual consiste na representação gráfica da atividade elétrica do coração. A fibrilação atrial (AF) é um tipo de arritmia cardíaca, sendo a mais presente na população mundial. Se não identificada nos estágios iniciais, aumenta as chances de ocorrência de paragens cardíacas e acidente vascular cerebral, que constituem uma das maiores causas de morte no mundo. Uma das principais características presentes no sinal de ECG de indivíduos com AF é a irregularidade no ritmo cardíaco, ou seja, variação no intervalo entre dois picos R consecutivos. Pelo fato da AF muitas vezes se apresentar de forma assintomática, o uso de sistemas computacionais para a análise automática do sinal de ECG se apresenta como uma alternativa interessante para auxiliar o profissional de saúde no diagnóstico dessa arritmia. Nesse contexto, o presente trabalho trata da implementação de um sistema de análise automática do sinal de ECG para identificação de episódios de AF. O sistema consiste em uma etapa de aquisição do sinal realizada por um sensor de ECG BITalino conectado à plataforma BITalino (r)evolution Core, ambos desenvolvidos pela PLUX – Wireless Biosignals S.A. O sinal adquirido é transmitido via comunicação bluetooth para um smartphone com sistema operacional Android™. O processamento do sinal é feito através de um aplicativo desenvolvido através da IDE Android™ Studio. O sistema de análise foi desenvolvido através do software MATLAB® e, posteriormente, implementado no aplicativo com o auxílio da aplicação MATLAB Coder™ e da interface JNI. Em linhas gerais, o sistema de análise é composto por um algoritmo para detecção dos picos da onda R do sinal de ECG, seguido de uma etapa de extração de características, e outra de classificação. A característica utilizada na entrada do modelo de classificação foi o intervalo entre picos R consecutivos. O modelo de classificação utilizado é baseado em redes neurais do tipo LSTM (Long Short-Term Memory). Quando validado sobre os sinais do banco de dados MIT-BIH Atrial Fibrillation, o algoritmo de detecção dos picos da onda R apresentou valores médios de sensibilidade (Se) e preditividade positiva (P+) de 98,99% e 95,95%, respectivamente. O modelo de classificação utilizado apresentou exatidão média de 94,94% na identificação de episódios de AF.Cardiac arrhythmias are disorders that affect the rate and/or rhythm of the heartbeats. The diagnosis of most arrhythmias is made through the analysis of the electrocardiogram (ECG), which consists of a graphic representation of the electrical activity of the heart. Atrial fibrillation (AF) is a type of cardiac arrhythmia, being the most present in the world population. If not identified in the early stages, it increases the chances of cardiac arrest and stroke, which are one of the biggest causes of death in the world. One of the main characteristics present in the ECG signal of individuals with AF is the irregularity in the cardiac rhythm, that is, variation in the interval between two consecutive R peaks. Since AF is often asymptomatic, the use of computer systems for the automatic analysis of the ECG signal is an interesting alternative to assist health professionals in diagnosing this arrhythmia. In this context, this work deals with the implementation of an automatic ECG signal analysis system to identify AF episodes. The system consists of a signal acquisition step performed by a BITalino ECG sensor connected to the BITalino (r)evolution Core platform, both developed by PLUX – Wireless Biosignals SA. The acquired signal is transmitted via bluetooth communication to a smartphone with Android™ operating system. The signal processing is done through an application developed using the IDE Android™ Studio. The analysis system was developed using the MATLAB® software and later implemented in the application with the help of the MATLAB Coder™ application and the JNI interface. In general terms, the analysis system is composed of an algorithm for detecting the peaks of the R wave of the ECG signal, followed by a feature extraction step, and a classification step. The feature used in the entry of the classification model was the interval between consecutive R peaks (RRi). The classification model used is based on a LSTM neural network. When validated over the signals from the MIT-BIH Atrial Fibrillation database, the R-wave peak detection algorithm showed mean values of sensitivity (Se) and positive predictivity (P+) of 98.99% and 95.95%, respectively. The classification model used had an average accuracy of 94.94% in identifying AF episodes

Classification of paroxysmal and persistent atrial fibrillation in ambulatory ECG recordings

The problem of classifying short atrial fibrillatory segments in ambulatory ECG recordings as being either paroxysmal or persistent is addressed by investigating a robust approach to signal characterization. The method comprises preprocessing estimation of the dominant atrial frequency for the purpose of controlling the subbands of a filter bank, computation of the relative subband (harmonics) energy, and the subband sample entropy. Using minimum-error-rate classification of different feature vectors, a data set consisting of 24-h ambulatory recordings from 50 subjects with either paroxysmal (26) or persistent (24) atrial fibrillation (AF) was analyzed on a 10-s segment basis; a total of 212,196 segments were classified. The best performance in terms of area under the receiver operating characteristic curve was obtained for a feature vector defined by the subband sample entropy of the dominant atrial frequency and the relative harmonics energy, resulting in a value of 0.923, whereas that of the dominant atrial frequency was equal to 0.826. It is concluded that paroxysmal and persistent AFs can be discriminated from short segments with good accuracy at any time of an ambulatory recording. © 2006 IEEE.January 11, 2011; accepted January 22, 2011. Date of publication February 10, 2011; date of current version April 20, 2011. This work was supported in part by the Spanish Ministry of Science and Innovation under Project TEC2010-20633 and the Junta de Comunidades de Castilla La Mancha under Project PII2C09-0224-5983, Project PII1C09-0036-3237, and Project PPII11-0194-8121. Asterisk indicates corresponding author.Alcaraz, R.; Sandberg, F.; Sornmo, L.; Rieta Ibañez, JJ. (2011). Classification of paroxysmal and persistent atrial fibrillation in ambulatory ECG recordings. IEEE Transactions on Biomedical Engineering. 58(5):1441-1449. https://doi.org/10.1109/TBME.2011.2112658S1441144958

Estimation of Atrial Electrical Complexity during Atrial Fibrillation by Solving the Inverse Problem of Electrocardiography

Tesis por compendio[ES] La fibrilación auricular (FA) es la arritmia más prevalente en el mundo y está asociada con una elevada morbilidad, mortalidad y costes sanitarios. A pesar de los avances en opciones de tratamiento farmacológico y terapia de ablación, el manejo de la FA todavía tiene margen de mejora. La imagen electrocardiográfica (ECGI) se ha destacado como un prometedor método no invasivo para evaluar la electrofisiología cardíaca y guiar las decisiones terapéuticas en casos de fibrilación auricular. No obstante, el ECGI se enfrenta a desafíos como la necesidad de resolver de manera precisa el denominado problema inverso de la electrocardiografía y de optimizar la calidad de las reconstrucciones de ECGI. Además, la integración del ECGI en los procesos clínicos rutinarios sigue siendo un reto, en gran medida debido a los costos que supone la necesidad de imágenes cardíacas. Por ello, los objetivos principales de esta tesis doctoral son impulsar la tecnología ECGI mediante la determinación de sus requisitos técnicos mínimos y la mejora de las metodologías existentes para obtener señales de ECGI precisas. Asimismo, buscamos evaluar la capacidad de ECGI para cuantificar de forma no invasiva la complejidad de la FA. Para lograr estos objetivos, se han llevado a cabo diversos estudios a lo largo de la tesis, desde el perfeccionamiento del ECGI hasta la evaluación de la FA utilizando esta tecnología. En primer lugar, se han estudiado los requisitos geométricos y de señal del problema inverso mediante el estudio de los efectos de la densidad de la malla del torso y la distribución de electrodos en la precisión del ECGI, lo que ha conducido a la identificación del número mínimo de nodos y su distribución en la malla del torso. Además, hemos identificado que para obtener señales de ECGI de alta calidad, es crucial la correcta disposición de los electrodos en la malla del torso reconstruido. Asimismo, se ha definido y evaluado una nueva metodología de ECGI sin necesidad de usar técnicas de imagen cardiaca. Para ello, hemos comparado métricas derivadas del ECGI calculadas con la geometría original del corazón de los pacientes con las métricas medidas en diferentes geometrías cardíacas. Nuestros resultados han mostrado que el ECGI sin necesidad de imágenes cardíacas es efectivo para la correcta cuantificación y localización de los patrones y zonas que mantienen la FA. En paralelo, hemos optimizado la regularización de Tikhonov de orden cero actual y la optimización de la curva L para el cálculo de las señales ECGI, investigando cómo el ruido eléctrico y las incertidumbres geométricas influyen en la regularización. A partir de ello, propusimos un nuevo criterio que realza la precisión de las soluciones de ECGI en escenarios con incertidumbre debido a condiciones de señal no ideales. En segundo lugar, en esta tesis doctoral, se han llevado a cabo múltiples análisis relativos a diferentes metodologías de procesado de señales y obtención métricas derivadas del ECGI con el fin de caracterizar mejor el sustrato cardíaco y la actividad reentrante en las señales de ECGI de pacientes con FA. Con el objetivo de obtener una comprensión más profunda de los mecanismos electrofisiológicos subyacentes a la FA, hemos establecido la estrategia de filtrado óptima para extraer patrones reentrantes específicos del paciente y métricas derivadas de señales ECGI. Además, hemos investigado la reproducibilidad de los mapas de reentradas derivados de las señales de ECGI y hemos encontrado su relación con el éxito de la ablación de venas pulmonares (PVI). Nuestros resultados han mostrado que una mayor reproducibilidad en los patrones reentrantes de FA detectados con ECGI está relacionada con el éxito de la PVI, creando una metodología para estratificar a los pacientes con FA antes de los procedimientos de ablación.[CA] La fibril·lació auricular (FA) és l'arrítmia més prevalent al món i està associada amb una elevada morbiditat, mortalitat i costos sanitaris. Malgrat els avanços en opcions de tractament farmacològic i teràpies d'ablació, el maneig de la FA encara té marge de millora. La imatge electrocardiogràfica (ECGI) s'ha destacat com un prometedor mètode no invasiu per a avaluar l'electrofisiologia cardíaca i guiar les decisions terapèutiques en casos de fibril·lació auricular. No obstant això, l'ECGI s'enfronta a desafiaments com la necessitat de resoldre de manera precisa el denominat problema invers de la electrocardiografia i d'optimitzar la qualitat de les reconstruccions de ECGI. A més, la integració del ECGI en els processos clínics rutinaris continua sent un repte, en gran manera a causa dels costos que suposa la necessitat d'imatges cardíaques. Per això, els objectius principals d'aquesta tesi doctoral són impulsar la tecnologia de l'ECGI mitjançant la determinació dels seus requisits tècnics mínims i la millora de les metodologies existents per obtenir senyals d'ECGI precises. A més, busquem avaluar la capacitat de l'ECGI per quantificar de forma no invasiva la complexitat de la FA. Per a aconseguir aquests objectius, s'han dut a terme diversos estudis al llarg de la tesi, des del perfeccionament de l'ECGI fins a l'avaluació de la FA utilitzant aquesta tecnologia. En primer lloc, hem estudiat els requisits geomètrics i de senyal del problema invers mitjançant l'estudi dels efectes de la densitat de la malla del tors i la distribució d'elèctrodes en la precisió de l'ECGI, el que ha conduït a la identificació del nombre mínim de nodes i la seva distribució en la malla del tors. A més, hem identificat que per obtindre senyals d'ECGI d'alta qualitat, és crucial la correcta disposició dels elèctrodes en la malla del tors reconstruïda. També s'ha definit i avaluat una nova metodologia d'ECGI sense necessitat d'utilitzar tècniques d'imatge cardíaca. Per a això, hem comparat mètriques derivades de l'ECGI calculades amb la geometria original del cor dels pacients amb les mètriques mesurades en diferents geometries cardíaques. Els nostres resultats han mostrat que l'ECGI sense necessitat d'imatges cardíaques és efectiu per a la correcta quantificació i localització dels patrons i zones que mantenen la FA. Paral·lelament, hem optimitzat la regularització de Tikhonov d'ordre zero actual i l'optimització de la corba L per al càlcul de les senyals d'ECGI, investigant com el soroll elèctric i les incerteses geomètriques influeixen en la regularització. Addicionalment, vam proposar un nou criteri que reforça la precisió de les solucions d'ECGI en escenaris amb incertesa degut a condicions de senyal no ideals. En segon lloc, en aquesta tesi doctoral, s'han dut a terme múltiples anàlisis relatius a diferents metodologies de processament de senyals i obtenció de mètriques derivades de l'ECGI amb l'objectiu de caracteritzar millor el substrat cardíac i l'activitat reentrant en les senyals d'ECGI de pacients amb FA. Amb l'objectiu d'obtindre una comprensió més profunda dels mecanismes electrofisiològics subjacents a la FA, hem establert l'estratègia de filtrat òptima per extreure patrons reentrants específics del pacient i mètriques derivades de senyals ECGI. A més, hem investigat la reproductibilitat dels mapes de reentrades derivats de les senyals d'ECGI i hem trobat la seva relació amb l'èxit de l'ablació de venes pulmonars (PVI). Els nostres resultats han mostrat que una major reproductibilitat en els patrons reentrants de FA detectats amb ECGI està relacionada amb l'èxit de la PVI, creant una metodologia per estratificar els pacients amb FA abans dels procediments d'ablació.[EN] Atrial fibrillation (AF) is the most prevalent arrhythmia in the world and is associated with significant morbidity, mortality, and healthcare costs. Despite advancements in pharmaceutical treatment alternatives and ablation therapy, AF management remains suboptimal. Electrocardiographic Imaging (ECGI) has emerged as a promising non-invasive method for assessing cardiac electrophysiology and guiding therapeutic decisions in atrial fibrillation. However, ECGI faces challenges in dealing with accurately resolving the ill-posed inverse problem of electrocardiography and optimizing the quality of ECGI reconstructions. Additionally, the integration of ECGI into clinical workflows is still a challenge that is hindered by the associated costs arising from the need for cardiac imaging. For this purpose, the main objectives of this PhD thesis are to advance ECGI technology by determining the minimal technical requirements and refining existing methodologies for acquiring accurate ECGI signals. In addition, we aim to assess the capacity of ECGI for noninvasively quantifying AF complexity. To fulfill these objectives, several studies were developed throughout the thesis, advancing from ECGI enhancement to AF evaluation using ECGI. Firstly, geometric and signal requirements of the inverse problem were addressed by studying the effects of torso mesh density and electrode distribution on ECGI accuracy, leading to the identification of the minimal number of nodes and their distribution on the torso mesh. Besides, we identified that the correct location of the electrodes on the reconstructed torso mesh is critical for the accurate ECGI signal obtention. Additionally, a new methodology of imageless ECGI was defined and assessed by comparing ECGI-derived drivers computed with the original heart geometry of the patients to the drivers measured in different heart geometries. Our results showed the ability of imageless ECGI to the correct quantification and location of atrial fibrillation drivers, validating the use of ECGI without the need for cardiac imaging. Also, the current state of-the-art zero-order Tikhonov regularization and L-curve optimization for computing ECGI signals were improved by investigating the impact of electrical noise and geometrical uncertainties on the regularization. We proposed a new criterion that enhances the accuracy and reliability of ECGI solutions in situations with uncertainty from unfavorable signal conditions. Secondly, in this PhD thesis, several analyses, signal processing methodologies, and ECGIderived metrics were investigated to better characterize the cardiac substrate and reentrant activity in ECGI signals from AF patients. With the objective of obtaining a deeper understanding of the electrophysiological mechanisms underlying AF, we established the optimal filtering strategy to extract patient-specific reentrant patterns and derived metrics in ECGI signals. Furthermore, we investigated the reproducibility of the obtained ECGI-reentrant maps and linked them to the success of PVI ablation. Our results showed that higher reproducibility on AF drivers detected with ECGI is linked with the success of PVI, creating a proof-of-concept mechanism for stratifying AF patients prior to ablation procedures.This work was supported by: Instituto de Salud Carlos III, and Ministerio de Ciencia e Innovación (supported by FEDER Fondo Europeo de Desarrollo Regional DIDIMO PLEC2021- 007614, ESSENCE PID2020-119364RB-I00, and RYC2018- 024346B-750), EIT Health (Activity code SAVE-COR 220385, EIT Health is supported by EIT, a body of the European Union) and Generalitat Valenciana Conselleria d’Educació, Investigació, Cultura i Esport (ACIF/2020/265 and BEFPI/2021/062).Molero Alabau, R. (2023). Estimation of Atrial Electrical Complexity during Atrial Fibrillation by Solving the Inverse Problem of Electrocardiography [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/199029Compendi

Mechanism and Prediction of Post-Operative Atrial Fibrillation Based on Atrial Electrograms

La fibrillation auriculaire (FA) est une arythmie touchant les oreillettes. En FA, la contraction auriculaire est rapide et irrégulière. Le remplissage des ventricules devient incomplet, ce qui réduit le débit cardiaque. La FA peut entraîner des palpitations, des évanouissements, des douleurs thoraciques ou l’insuffisance cardiaque. Elle augmente aussi le risque d'accident vasculaire. Le pontage coronarien est une intervention chirurgicale réalisée pour restaurer le flux sanguin dans les cas de maladie coronarienne sévère. 10% à 65% des patients qui n'ont jamais subi de FA, en sont victime le plus souvent lors du deuxième ou troisième jour postopératoire. La FA est particulièrement fréquente après une chirurgie de la valve mitrale, survenant alors dans environ 64% des patients. L'apparition de la FA postopératoire est associée à une augmentation de la morbidité, de la durée et des coûts d'hospitalisation. Les mécanismes responsables de la FA postopératoire ne sont pas bien compris. L'identification des patients à haut risque de FA après un pontage coronarien serait utile pour sa prévention. Le présent projet est basé sur l'analyse d’électrogrammes cardiaques enregistrées chez les patients après pontage un aorte-coronaire. Le premier objectif de la recherche est d'étudier si les enregistrements affichent des changements typiques avant l'apparition de la FA. Le deuxième objectif est d'identifier des facteurs prédictifs permettant d’identifier les patients qui vont développer une FA. Les enregistrements ont été réalisés par l'équipe du Dr Pierre Pagé sur 137 patients traités par pontage coronarien. Trois électrodes unipolaires ont été suturées sur l'épicarde des oreillettes pour enregistrer en continu pendant les 4 premiers jours postopératoires. La première tâche était de développer un algorithme pour détecter et distinguer les activations auriculaires et ventriculaires sur chaque canal, et pour combiner les activations des trois canaux appartenant à un même événement cardiaque. L'algorithme a été développé et optimisé sur un premier ensemble de marqueurs, et sa performance évaluée sur un second ensemble. Un logiciel de validation a été développé pour préparer ces deux ensembles et pour corriger les détections sur tous les enregistrements qui ont été utilisés plus tard dans les analyses. Il a été complété par des outils pour former, étiqueter et valider les battements sinusaux normaux, les activations auriculaires et ventriculaires prématurées (PAA, PVA), ainsi que les épisodes d'arythmie. Les données cliniques préopératoires ont ensuite été analysées pour établir le risque préopératoire de FA. L’âge, le niveau de créatinine sérique et un diagnostic d'infarctus du myocarde se sont révélés être les plus importants facteurs de prédiction. Bien que le niveau du risque préopératoire puisse dans une certaine mesure prédire qui développera la FA, il n'était pas corrélé avec le temps de l'apparition de la FA postopératoire. Pour l'ensemble des patients ayant eu au moins un épisode de FA d’une durée de 10 minutes ou plus, les deux heures précédant la première FA prolongée ont été analysées. Cette première FA prolongée était toujours déclenchée par un PAA dont l’origine était le plus souvent sur l'oreillette gauche. Cependant, au cours des deux heures pré-FA, la distribution des PAA et de la fraction de ceux-ci provenant de l'oreillette gauche était large et inhomogène parmi les patients. Le nombre de PAA, la durée des arythmies transitoires, le rythme cardiaque sinusal, la portion basse fréquence de la variabilité du rythme cardiaque (LF portion) montraient des changements significatifs dans la dernière heure avant le début de la FA. La dernière étape consistait à comparer les patients avec et sans FA prolongée pour trouver des facteurs permettant de discriminer les deux groupes. Cinq types de modèles de régression logistique ont été comparés. Ils avaient une sensibilité, une spécificité et une courbe opérateur-receveur similaires, et tous avaient un niveau de prédiction des patients sans FA très faible. Une méthode de moyenne glissante a été proposée pour améliorer la discrimination, surtout pour les patients sans FA. Deux modèles ont été retenus, sélectionnés sur les critères de robustesse, de précision, et d’applicabilité. Autour 70% patients sans FA et 75% de patients avec FA ont été correctement identifiés dans la dernière heure avant la FA. Le taux de PAA, la fraction des PAA initiés dans l'oreillette gauche, le pNN50, le temps de conduction auriculo-ventriculaire, et la corrélation entre ce dernier et le rythme cardiaque étaient les variables de prédiction communes à ces deux modèles.Atrial fibrillation (AF) is an abnormal heart rhythm (cardiac arrhythmia). In AF, the atrial contraction is rapid and irregular, and the filling of the ventricles becomes incomplete, leading to reduce cardiac output. Atrial fibrillation may result in symptoms of palpitations, fainting, chest pain, or even heart failure. AF is an also an important risk factor for stroke. Coronary artery bypass graft surgery (CABG) is a surgical procedure to restore the perfusion of the cardiac tissue in case of severe coronary heart disease. 10% to 65% of patients who never had a history of AF develop AF on the second or third post CABG surgery day. The occurrence of postoperative AF is associated with worse morbidity and longer and more expensive intensive-care hospitalization. The fundamental mechanism responsible of AF, especially for post-surgery patients, is not well understood. Identification of patients at high risk of AF after CABG would be helpful in prevention of postoperative AF. The present project is based on the analysis of cardiac electrograms recorded in patients after CABG surgery. The first aim of the research is to investigate whether the recordings display typical changes prior to the onset of AF. A second aim is to identify predictors that can discriminate the patients that will develop AF. Recordings were made by the team of Dr. Pierre Pagé on 137 patients treated with CABG surgery. Three unipolar electrodes were sutured on the epicardium of the atria to record continuously during the first 4 post-surgery days. As a first stage of the research, an automatic and unsupervised algorithm was developed to detect and distinguish atrial and ventricular activations on each channel, and join together the activation of the different channels belonging to the same cardiac event. The algorithm was developed and optimized on a training set, and its performance assessed on a test set. Validation software was developed to prepare these two sets and to correct the detections over all recordings that were later used in the analyses. It was complemented with tools to detect, label and validate normal sinus beats, atrial and ventricular premature activations (PAA, PVC) as well as episodes of arrhythmia. Pre-CABG clinical data were then analyzed to establish the preoperative risk of AF. Age, serum creatinine and prior myocardial infarct were found to be the most important predictors. While the preoperative risk score could to a certain extent predict who will develop AF, it was not correlated with the post-operative time of AF onset. Then the set of AF patients was analyzed, considering the last two hours before the onset of the first AF lasting for more than 10 minutes. This prolonged AF was found to be usually triggered by a premature atrial PAA most often originating from the left atrium. However, along the two pre-AF hours, the distribution of PAA and of the fraction of these coming from the left atrium was wide and inhomogeneous among the patients. PAA rate, duration of transient atrial arrhythmia, sinus heart rate, and low frequency portion of heart rate variability (LF portion) showed significant changes in last hour before the onset of AF. Comparing all other PAA, the triggering PAA were characterized by their prematurity, the small value of the maximum derivative of the electrogram nearest to the site of origin, as well as the presence of transient arrhythmia and increase LF portion of the sinus heart rate variation prior to the onset of the arrhythmia. The final step was to compare AF and Non-AF patients to find predictors to discriminate the two groups. Five types of logistic regression models were compared, achieving similar sensitivity, specificity, and ROC curve area, but very low prediction accuracy for Non-AF patients. A weighted moving average method was proposed to design to improve the accuracy for Non-AF patient. Two models were favoured, selected on the criteria of robustness, accuracy, and practicability. Around 70% Non-AF patients were correctly classified, and around 75% of AF patients in the last hour before AF. The PAA rate, the fraction of PAA initiated in the left atrium, pNN50, the atrio-ventricular conduction time, and the correlation between the latter and the heart rhythm were common predictors of these two models

Therapeutic Strategies for the Treatment of Atrial Fibrillation:New Insights from Biophysical Modeling and Signal Processing

Atrial fibrillation is the most common cardiac rhythm disorder encountered in clinical practice, often leading to severe complications such as heart failure and stroke. This arrhythmia, increasing in prevalence with age, already affects several millions of people in the United States, with a rising occurrence of the disease during the past two decades. In spite of these warning signals, atrial fibrillation is still difficult to treat, because basic mechanisms of the arrhythmia remain poorly understood and current treatments are therefore based on empirical considerations. The future of therapeutic solutions for the treatment of complex diseases such as atrial fibrillation relies on a strong collaboration between medicine, biology and engineering. Only through such synergies will efficient monitoring, diagnostic and therapeutic devices be created. The goal of the present thesis was to adopt this multidisciplinary approach, and develop new strategies for atrial fibrillation therapy using both computer modeling and advanced signal processing methods. Biophysical modeling is a practical and ethically interesting approach to develop innovative therapies, since physiological phenomena of interest are reproduced numerically and the resulting framework is then used with full repeatability to explore mechanisms and test treatments. A model of the human atria, that was developed in our group, was used to simulate atrial fibrillation and perform mechanistic and therapeutic investigations. In a first study, computer simulations were used to observe spontaneous terminations of two models of atrial fibrillation corresponding to different developmental stages of the arrhythmia. Dynamical parameters were observed during several seconds prior to termination in order to describe the underlying mechanisms of this natural phenomenon, showing that different levels of fibrillation complexity led to different termination patterns. The mechanisms highlighted by the study were successfully compared to those described in the existing literature and could suggest interesting guidelines to better investigate spontaneous terminations of atrial fibrillation in experimental and clinical settings. Moreover, a more precise understanding of the natural extinction of atrial fibrillation will certainly be crucial for future therapy developments. The potential of rapid low-energy pacing for artificially terminating atrial fibrillation was also thoroughly investigated. First, the possibility to entrain and thereby control fibrillating atrial activity by rapid pacing was studied in a systematic manner. Results showed that optimized pacing parameters provided sustained entrainment of electrical activity, although total extinction of atrial fibrillation was never observed. The ability to control atrial activity by pacing was also shown to depend on specific properties of the atrial tissue, showing that patients with atrial fibrillation may not all respond in the same way to pacing treatments. Finally, this study suggested different guidelines for the development of pace-termination algorithms for atrial fibrillation. Based on these results, a new pacing sequence for the automatic termination of atrial fibrillation was designed, implemented and tested in the biophysical model. The pacing protocol comprised two distinct phases involving a succession of rapid and slow pacing stimulations. The results of the tests suggest that this pacing scheme could represent an alternative to current treatments of atrial fibrillation, and could easily be implemented in patients who already have an indication for pacing. Advanced signal processing techniques were also used in this thesis to analyze real cardiac signals and develop new diagnosis tools. Multivariate spectral analysis and complexity measures were combined to develop an automatic method able to describe subtle changes in atrial fibrillation organization as measured by non-invasive ECG recordings. Accurate discrimination between persistent and permanent AF was shown possible, and potential applications in clinical settings to optimize patient management were demonstrated. Collectively, the results of this thesis show that major public health issues such as atrial fibrillation can strongly benefit from the contribution of biomedical engineering. The modeling and signal processing approaches used in the present dissertation proved effective and promising, and synergies between clinicians and scientists will definitely be at the basis of future therapies

Multichannel Intracardiac Electrogram Analysis to Estimate the Depolarisation Wavefront Propagation: Supporting Diagnostics and Treatment of Atrial Fibrillation

Kardiale Arrhythmien sind Störungen des Herzrhythmus, welche von unregelmäßigem Herzschlag kommen. Vorhofflimmern ist die am weitesten verbreitete Herzrhythmusstörung und ist mit zunehmendem Alter weiter verbreitet. Thromboembolische Ereignisse und Störungen der Hämodynamik können als Begleiterscheinungen von Vorhofflimmern (AFib) auftreten und eine signifikant gesteigerte Morbidität und Mortalität zur Folge haben. Die Be- handlung von AFib erfolgt mit Medikamenten und zudem mit Hilfe der Katheterablation. Im Zuge der Ablation versuchen Ärzte die Bereiche arrhythmogenen Substrats zu lokalisieren. Danach werden kleine Ablationsnarben im Herzgewebe erzeugt, welche die Ausbreitung abnormaler elektrischer Erregungen im Herzen unterdrücken sollen. Die Erfolgsraten dieser Prozedur erreichen bis zu 70% nach zwei oder drei Ablationen. Im Zuge diese Arbeiten wurden die Regionen arrhythmogenen Substrats lokalisiert, und die Details der Erregungsausbreitung über dieses Substrat wurden bestimmt. Im Verlauf dieser Arbeit wurden klinische Daten, experimentelle Daten und Simulationen für die Analyse genutzt. Simulationen wurden genutzt um die lokale Aktivierungszeit (LAT) auf klinischen Anatomien zu bestimmen. Experimentelle Daten wurden mit Hilfe eines Elektrodenpatches von einem Hund herzen erfasst. Klinische Daten wurden mit Hilfe eines elektroanatomischen Mappingsystems im Rahmen klinischer Routineuntersuchungen aufgezeichnet. Die aufgezeichneten Daten wurden einer Vorverarbeitung unterzogen um messtechnische und geometrische Artefakte wie das ventrikuläre Fernfeld (VFF) oder hoch- und niederfrequentes Rauschen zu unterdrücken. Eine Vielzahl von Merkmalen wurden aus den vorbearbeiteten Daten gewonnen. Dies waren die Bestimmung des Stimulationsprokotolls, die Abschätzung der Dauer der fraktionierten Aktivität, die Korrelation der Morphologie, Spitzen-zu-Spitzen Amplitude, Bestimmung der QRS Komplexe, lokale Aktivierungszeit, die Bestimmung einer stabilen Katheterposition und die Markierung der Region des arrhythmogenen Substrats. Die Methode zur Bestimmung von Richtung und Geschwindigkeit der Erregungsausbreitung wurde bestimmt. Ein grafisches Nutzerinterface (GUI) wurde entwickelt zur Bestimmung der Ausbreitungsgeschwindigkeit und darauf basierender regionaler Analyse. Simulierte Daten wurden genutzt um die Leistungsfähigkeit der entwickelten Algorithmen zu beurteilen. Zur Simulation der LAT auf klinischen Anatomien wurde die fast marching Methode (FaMaS) genutzt. In diesen Simulationen war die goldene Wahrheit für eine Beurteilung der Parameterabschätzung bekannt. Ein umsichtiger und erfolgreicher Versuch wurde unternommen, um Muster und Geschwindig- keit der Erregungsausbreitung auf dem Vorhof zu bestimmen. Dies wurde auf Basis der LAT Zeit und stabiler Katheterpositionen durchgeführt. Interessante Regionen wurden zudem als wahrscheinliche Regionen eines arrhythmogenen Substrats im linken Vorhof markiert. Dies wurde auf Grundlage mehr als eines Merkmals und visueller Beurteilung deren Verteilung im Vorhof durchgeführt. Für die stimulierten Daten wurde die Aktivität der S1 und S2 Erregung verglichen um Änderungen in der Erregungsausbreitung abzuschätzen. Die Auswertung der experimentellen Daten wurde in Kooperation mit internationalen Part- nern aus den USA durchgeführt. Für verschiedene Szenarien wurden dabei Richtung und Muster der Erregungsausbreitung abgeschätzt. Die zeitliche und räumliche Informationen der vorgeschlagenen Method war dabei genau kontrolliert. Mit den Auswertemethoden aus dieser Arbeit können die wahrscheinliche Region des arrhythmogenen Substrats und der Verlauf der Erregungsausbreitung auf dem Vorhof für Vorhofflimmern und Vorhofflattern bestimmt werden. Diese können dem behandelnden Arzt bei der Planung der Ablationstherapie und erfolgreicher Durchführung helfen

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

[EN] Atrial fibrillation (AF) is nowadays the most common cardiac arrhythmia, being associated with an increase in cardiovascular mortality and morbidity. When AF lasts for more than seven days, it is classified as persistent AF and external interventions are required for its termination. A well-established alternative for that purpose is electrical cardioversion (ECV). While ECV is able to initially restore sinus rhythm (SR) in more than 90% of patients, rates of AF recurrence as high as 20-30% have been found after only a few weeks of follow-up. Hence, new methods for evaluating the proarrhythmic condition of a patient before the intervention can serve as efficient predictors about the high risk of early failure of ECV, thus facilitating optimal management of AF patients. Among the wide variety of predictors that have been proposed to date, those based on estimating organization of the fibrillatory (f-) waves from the surface electrocardiogram (ECG) have reported very promising results. However, the existing methods are based on traditional entropy measures, which only assess a single time scale and often are unable to fully characterize the dynamics generated by highly complex systems, such as the heart during AF. The present work then explores whether a multi-scale entropy (MSE) analysis of thef-waves may provide early prediction of AF recurrence after ECV. In addition to the common MSE, two improved versions have also been analyzed, composite MSE (CMSE) and refined MSE (RMSE). When analyzing 70 patients under ECV, of which 31 maintained SR and 39 relapsed to AF after a four week follow-up, the three methods provided similar performance. However, RMSE reported a slightly better discriminant ability of 86%, thus improving the other multi-scale-based outcomes by 3-9% and other previously proposed predictors of ECV by 15-30%. This outcome suggests that investigation of dynamics at large time scales yields novel insights about the underlying complex processes generatingf-waves, which could provide individual proarrhythmic condition estimation, thus improving preoperative predictions of ECV early failure.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.Cirugeda Roldan, EM.; Calero, S.; Hidalgo, VM.; Enero, J.; Rieta, JJ.; Alcaraz, R. (2020). Multi-scale Entropy Evaluates the Proarrhythmic Condition of Persistent Atrial Fibrillation Patients Predicting Early Failure of Electrical Cardioversion. 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European Heart Journal, 37(38), 2893-2962. doi:10.1093/eurheartj/ehw210Scheuermeyer, F. X., Andolfatto, G., Christenson, J., Villa‐Roel, C., & Rowe, B. (2019). A Multicenter Randomized Trial to Evaluate a Chemical‐first or Electrical‐first Cardioversion Strategy for Patients With Uncomplicated Acute Atrial Fibrillation. Academic Emergency Medicine, 26(9), 969-981. doi:10.1111/acem.13669Fried, A. M., Strout, T. D., & Perron, A. D. (2021). Electrical cardioversion for atrial fibrillation in the emergency department: A large single-center experience. The American Journal of Emergency Medicine, 42, 115-120. doi:10.1016/j.ajem.2020.02.001Brandes, A., Crijns, H. J. G. M., Rienstra, M., Kirchhof, P., Grove, E. L., Pedersen, K. B., & Van Gelder, I. C. (2020). Cardioversion of atrial fibrillation and atrial flutter revisited: current evidence and practical guidance for a common procedure. EP Europace, 22(8), 1149-1161. doi:10.1093/europace/euaa057Alegret, J. M., Viñolas, X., Tajes, H., Valdovinos, P., Palomares, R., Arias, M. A., & Bazán, V. (2020). Utility of Amiodarone Pre-Treatment as a Facilitator of the Acute Success of Electrical Cardioversion in Persistent Atrial Fibrillation. Cardiovascular Drugs and Therapy, 34(1), 89-94. doi:10.1007/s10557-019-06934-5Piccini, J. P., & Fauchier, L. (2016). Rhythm control in atrial fibrillation. The Lancet, 388(10046), 829-840. doi:10.1016/s0140-6736(16)31277-6Jaakkola, S., Lip, G. Y. H., Biancari, F., Nuotio, I., Hartikainen, J. E. K., Ylitalo, A., & Airaksinen, K. E. J. (2017). Predicting Unsuccessful Electrical Cardioversion for Acute Atrial Fibrillation (from the AF-CVS Score). The American Journal of Cardiology, 119(5), 749-752. doi:10.1016/j.amjcard.2016.11.026Fujimoto, Y., Yodogawa, K., Maru, Y., Oka, E., Hayashi, H., Yamamoto, T., … Shimizu, W. (2018). Advanced interatrial block is an electrocardiographic marker for recurrence of atrial fibrillation after electrical cardioversion. International Journal of Cardiology, 272, 113-117. doi:10.1016/j.ijcard.2018.07.135GRÖNBERG, T., HARTIKAINEN, J. E. K., NUOTIO, I., BIANCARI, F., VASANKARI, T., NIKKINEN, M., … AIRAKSINEN, K. E. J. (2014). Can We Predict the Failure of Electrical Cardioversion of Acute Atrial Fibrillation? The FinCV Study. Pacing and Clinical Electrophysiology, 38(3), 368-375. doi:10.1111/pace.12561Doruchowska, A., Wita, K., Bochenek, T., Szydło, K., Filipecki, A., Staroń, A., … Trusz-Gluza, M. (2014). Role of left atrial speckle tracking echocardiography in predicting persistent atrial fibrillation electrical cardioversion success and sinus rhythm maintenance at 6 months. Advances in Medical Sciences, 59(1), 120-125. doi:10.1016/j.advms.2013.10.003Luong, C. L., Thompson, D. J. S., Gin, K. G., Jue, J., Nair, P., Lee, P.-K., … Tsang, T. S. M. (2016). Usefulness of the Atrial Emptying Fraction to Predict Maintenance of Sinus Rhythm After Direct Current Cardioversion for Atrial Fibrillation. 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Wavelet transform analysis predicts outcome of DC cardioversion for atrial fibrillation patients. Computers in Biology and Medicine, 37(4), 517-523. doi:10.1016/j.compbiomed.2006.08.003Cervigón, R., Sánchez, C., Castells, F., Blas, J. M., & Millet, J. (2007). Wavelet analysis of electrocardiograms to characterize recurrent atrial fibrillation. Journal of the Franklin Institute, 344(3-4), 196-211. doi:10.1016/j.jfranklin.2006.10.005Alcaraz, R., & Rieta, J. J. (2008). A non-invasive method to predict electrical cardioversion outcome of persistent atrial fibrillation. Medical & Biological Engineering & Computing, 46(7), 625-635. doi:10.1007/s11517-008-0348-5Alcaraz, R., & Rieta, J. J. (2009). Time and frequency recurrence analysis of persistent atrial fibrillation after electrical cardioversion. Physiological Measurement, 30(5), 479-489. doi:10.1088/0967-3334/30/5/005Lankveld, T., de Vos, C. B., Limantoro, I., Zeemering, S., Dudink, E., Crijns, H. J., & Schotten, U. (2016). Systematic analysis of ECG predictors of sinus rhythm maintenance after electrical cardioversion for persistent atrial fibrillation. Heart Rhythm, 13(5), 1020-1027. doi:10.1016/j.hrthm.2016.01.004ALCARAZ, R., HORNERO, F., & RIETA, J. J. (2011). Noninvasive Time and Frequency Predictors of Long-Standing Atrial Fibrillation Early Recurrence after Electrical Cardioversion. Pacing and Clinical Electrophysiology, 34(10), 1241-1250. doi:10.1111/j.1540-8159.2011.03125.xCosta, M., Goldberger, A. L., & Peng, C.-K. (2002). Multiscale Entropy Analysis of Complex Physiologic Time Series. Physical Review Letters, 89(6). doi:10.1103/physrevlett.89.068102Humeau-Heurtier, A. (2015). The Multiscale Entropy Algorithm and Its Variants: A Review. Entropy, 17(5), 3110-3123. doi:10.3390/e17053110Wu, S.-D., Wu, C.-W., Lin, S.-G., Wang, C.-C., & Lee, K.-Y. (2013). Time Series Analysis Using Composite Multiscale Entropy. Entropy, 15(3), 1069-1084. doi:10.3390/e15031069Valencia, J. 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Journal of Interventional Cardiac Electrophysiology, 26(1), 11-19. doi:10.1007/s10840-009-9398-3Alcaraz, R., Sandberg, F., Sörnmo, L., & Rieta, J. J. (2011). Classification of Paroxysmal and Persistent Atrial Fibrillation in Ambulatory ECG Recordings. IEEE Transactions on Biomedical Engineering, 58(5), 1441-1449. doi:10.1109/tbme.2011.211265

XXIV congreso anual de la sociedad española de ingeniería biomédica (CASEIB2016)

En la presente edición, más de 150 trabajos de alto nivel científico van a ser presentados en 18 sesiones paralelas y 3 sesiones de póster, que se centrarán en áreas relevantes de la Ingeniería Biomédica. Entre las sesiones paralelas se pueden destacar la sesión plenaria Premio José María Ferrero Corral y la sesión de Competición de alumnos de Grado en Ingeniería Biomédica, con la participación de 16 alumnos de los Grados en Ingeniería Biomédica a nivel nacional. El programa científico se complementa con dos ponencias invitadas de científicos reconocidos internacionalmente, dos mesas redondas con una importante participación de sociedades científicas médicas y de profesionales de la industria de tecnología médica, y dos actos sociales que permitirán a los participantes acercarse a la historia y cultura valenciana. Por primera vez, en colaboración con FENIN, seJane Campos, R. (2017). XXIV congreso anual de la sociedad española de ingeniería biomédica (CASEIB2016). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/79277EDITORIA

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

The Cox-maze surgery is an effective procedure for terminating atrial fibrillation (AF) in patients requiring open-heart surgery associated with another heart disease. After the intervention, regardless of the patient's rhythm, all are treated with oral anticoagulants and antiarrhythmic drugs prior to discharge. Furthermore, patients maintaining AF before discharge could also be treated with electrical cardioversion (ECV). In view of this, a preoperative prognosis of the patient's rhythm at discharge would be helpful for optimizing drug therapy planning as well as for advancing ECV therapy. This work analyzes 30 preoperative electrocardiograms (ECGs) from patients suffering from AF in order to predict the Cox-maze surgery outcome at discharge. Two different characteristics of the AF pattern have been studied. On the one hand, the atrial activity (AA) organization, which provides information about the number of propagating wavelets in the atria, was investigated. AA organization has been successfully used in previous studies related to spontaneous reversion of paroxysmal AF and to the outcome of ECV. To assess organization, the dominant atrial frequency (DAF) and sample entropy (SampEn) have been computed. On the other hand, the second characteristic studied was the fibrillatory wave (f-wave) amplitude, which has been demonstrated to be a valuable indicator of the Cox-maze surgery outcome in previous studies. Moreover, this parameter has been obtained through a new methodology, based on computing the f-wave average power (fWP). Finally, all the computed indices were combined in a decision tree in order to improve prediction capability. Results for the DAF yielded a sensitivity (Se), a specificity (Sp) and an accuracy (Acc) of 61.54%, 82.35% and 73.33%, respectively. For SampEn the values were 69.23%, 76.00% and 73.33%, respectively, and for fWP they were 92.31%, 82.35% and 86.67%, respectively. Finally, the decision tree combining the three parameters analyzed improved the preoperative prognosis of the Cox-maze outcome with values of Se, Sp and Acc of 100%, 82.35% and 90%, respectively. As a consequence, the analysis of parameters related to the f-wave pattern, extracted from the preoperative ECG, has provided a considerable ability to predict the outcome of AF Cox-maze surgery at discharge. © 2014 Institute of Physics and Engineering in Medicine.This work was supported by the projects TEC2010-20633 from the Spanish Ministry of Science and Innovation, TEC2013-41428-R from the Spanish Ministry of Economy and Competitiveness and PPII11-0194-8121 from Junta de Comunidades de Castilla La Mancha.Hernández Alonso, A.; Alcaraz, R.; Hornero, F.; Rieta, JJ. (2014). Preoperative study of the surface ECG for the prognosis of atrial fibrillation maze surgery outcome at discharge. Physiological Measurement. 35(7):1409-1423. https://doi.org/10.1088/0967-3334/35/7/1409S14091423357Ad, N. 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Pacing and Clinical Electrophysiology, 34(10), 1241-1250. doi:10.1111/j.1540-8159.2011.03125.xAlcaraz, R., & Rieta, J. J. (2008). Adaptive singular value cancelation of ventricular activity in single-lead atrial fibrillation electrocardiograms. Physiological Measurement, 29(12), 1351-1369. doi:10.1088/0967-3334/29/12/001Alcaraz, R., & Rieta, J. J. (2009). Time and frequency recurrence analysis of persistent atrial fibrillation after electrical cardioversion. Physiological Measurement, 30(5), 479-489. doi:10.1088/0967-3334/30/5/005Alcaraz, R., & Rieta, J. J. (2010). A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms. Biomedical Signal Processing and Control, 5(1), 1-14. doi:10.1016/j.bspc.2009.11.001Alcaraz, R., Sandberg, F., Sörnmo, L., & Rieta, J. J. (2011). Classification of Paroxysmal and Persistent Atrial Fibrillation in Ambulatory ECG Recordings. 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