Applications of Signal Analysis to Atrial Fibrillation

This work was supported by projects TEC2010–20633 from the Spanish Ministry of Science and Innovation and PPII11–0194–8121 from Junta de Comunidades de Castilla-La ManchaRieta Ibañez, JJ.; Alcaraz Martínez, R. (2013). Applications of Signal Analysis to Atrial Fibrillation. En Atrial Fibrillation - Mechanisms and Treatment. InTech. 155-180. https://doi.org/10.5772/5340915518

Detection and measurement and of repolarisation features in atrial fibrillation and healthy subjects

Major cardiac organisations recommended U wave abnormalities should be reported during ECG interpretation. However, U waves cannot be measured in patients with atrial fibrillation (AF) due to the obscuring fibrillatory wave.The first aim of the research was to provide a validated algorithm to clean the ECGs of AF patients by removing the atrial fibrillatory waves so that the characteristics of ventricle repolarisation components, U and T waves, could be detected and measured accurately without fibrillatory wave contamination.Having established a validated algorithm to measure the waveform features, the second aim was to use this algorithm to investigate the effect of beat interval dependency on the repolarisation waves, especially U waves, during AF and to compare them to those in sinus rhythm (SR) of healthy subjects. The research could provide mechanistic insight into the origin of U waves since AF is unique in its rapidly changing ventricular beat intervals. The preceding beat interval has a direct impact on ventricular filling dynamics and hence also on mechano-electrical coupling, one of the leading hypotheses of U wave genesis.Algorithms were developed to remove the contaminating fibrillatory waves in AF recordings and to measure features of the ventricular repolarisation waves.The ventricular repolarisation features, U and T waves, are measurable and dependent on preceding beat interval in AF and SR. The beat interval dependency of repolarisation features, especially the U wave, supported the mechano-electrical hypothesis during AF and SR.The research provides tools to facilitate the detection and reporting of U waves and their abnormalities in AF patients and provides mechanistic insight into rate dependency of ventricular repolarisation features

Analysis of Atrial Electrograms

This work provides methods to measure and analyze features of atrial electrograms - especially complex fractionated atrial electrograms (CFAEs) - mathematically. Automated classification of CFAEs into clinical meaningful classes is applied and the newly gained electrogram information is visualized on patient specific 3D models of the atria. Clinical applications of the presented methods showed that quantitative measures of CFAEs reveal beneficial information about the underlying arrhythmia

High-Density Mapping Analysis of Electrical Spatiotemporal Behaviour in Atrial Fibrillation

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas), 2022, Universidade de Lisboa, Faculdade de CiênciasDoenças cardiovasculares, tais como arritmias, são a principal causa de morte no mundo, especialmente no Sul e no Este da Ásia, e nos Estados Unidos da América [1]. As arritmas são caracterizadas pela alteração no ritmo sinusal normal do coração. Em particular, a fibrilhação auricular (FA) é a arritmia cardíaca mais comum na prática clínica, contribuindo para mais de 200 mil mortes globalmente em 2017 [2]. Caracteriza-se pela contração rápida e dessincronizada das aurículas, e está associada ao aumento da mortalidade e afecta de forma negativa a qualidade de vida dos pacientes. A FA é geralmente tratada através de medicação, porém quando esta falha, a ablação por cateter é indicada, sendo um tratamento de referência para combater esta patologia. A ablação apresenta uma taxa de sucesso de aproximadamente 50% no primeiro procedimento, sendo necessário efectuar vários procedimentos para aumentar a eficácia do tratamento [3]. A detecção desta patologia envolve, numa primeira fase, a realização de um electrocardiograma (ECG) e, posteriormente um estudo electrofisiológico para saber com precisão onde se localiza e o mecanismo subjacente à mesma. Este último implica o registo da actividade eléctrica através de electrogramas (EGM) locais em diferentes pontos das aurículas e dos ventrículos, com o auxílio de sistemas de mapeamento tridimensionais (3D) electroanatómicos, sendo um procedimento invasivo. Existem diversos métodos lineares e não lineares que permitem a análise dos EGMs nos domínios do tempo, frequência, fase, entre outros, com a finalidade de melhor compreender os mecanismos subjacentes à FA e, consequentemente aumentar a taxa de sucesso do processo de ablação e melhorar a sua eficiência. Esta área de estudo progrediu significativamente, tanto a nível de hardware, como de software. Apesar disso, os métodos desenvolvidos não têm nem acrescentado benefícios adicionais, nem melhorado significativamente a taxa de sucesso do processo de ablação. Existem várias razões para tal, e grande parte deve-se ao facto destes métodos de análise estarem incorporados nos sistemas de mapeamento e o seu software ser exclusivo. Isto leva a que não consigamos perceber como é que os algoritmos funcionam nos diferentes sistemas de mapeamento para comparar as suas diferenças e semelhanças. Devido a estes constrangimentos, os investigadores são compelidos a desenvolver os seus próprios métodos de análise e técnicas de mapeamento, o que leva à existência de uma multitude de métodos e técnicas de mapeamento que parecem ser diferentes entre si, resultando em informação ambígua e conflituosa no que diz respeito aos mecanismos da FA, e a conclusões distintas entre estudos. O sucesso do tratamento poderia aumentar se tivéssemos uma melhor compreensão dos métodos de análise e da sua aplicação no contexto da FA; perceber se os métodos apontam para o mesmo fenómeno de fibrilhação, se existe alguma correlação entre os métodos, e se a informação fornecida pelos mesmos é complementar ou redundante. Assim, o objectivo deste trabalho consistiu em implementar diferentes métodos para analisar os EGMs e a estrutura 3D da aurícula esquerda (AE) de doentes com FA, numa tentativa de responder às questões que motivaram a realização deste projecto. Em última análise, ao observar os mapas 3D da AE tendo uma melhor compreensão dos métodos, poderemos identificar com precisão as regiões na AE responsáveis por iniciar a FA, e ter mais conhecimento sobre os mecanismos responsáveis pela mesma. Desta forma, o processo de ablação poderá alcançar o seu potencial. Para este projecto, foram incluídos os mapas 3D electroanatómicos da AE de dez doentes com FA paroxística ou persistente do hospital de Santa Marta, recolhidos com o sistema de mapeamento CARTO 3. Cada ponto electroanatómico dos mapas inclui as 12 derivações do ECG, e os EGMs unipolares e bipolares registados com o cateter de mapeamento Pentaray de 20 pólos. Porém, apenas os EGMs bipolares foram incluídos na análise. Processaram-se os sinais bipolares e, devido a algumas limitações, foi possível apenas a implementação de dois métodos diferentes para os analisar: um no domínio da frequência – Frequência Dominante (FD) –, e outro no domínio da Teoria da Informação – a entropia de Shannon. De seguida, criaram-se três tipos de mapas 3D electroanatómicos da AE para cada doente: um de voltagem, cuja informação foi adquirida com o sistema de mapeamento, um de FD, e outro de entropia. A informação de cada mapa estava organizada segundo um padrão de cores. Observando os diferentes tipos de mapas da AE paralelamente, foi possível comparar os métodos, e perceber que tipo de informação cada um deles fornecia, numa tentativa de melhor compreender os mecanismos da FA. Foi possível observar em algumas regiões da AE, principalmente nos mapas de voltagem e de FD, a presença de “centros de activação” ou “centros de fibrilhação”, que poderão ser os gatilhos responsáveis por desencadear ou manter o mecanismo de fibrilhação. Para confirmar se de facto aquelas regiões eram os gatilhos de fibrilhação, seria necessário submeter os doentes ao processo de ablação e queimar essas zonas; e posteriormente acompanhar os doentes para observar os efeitos do procedimento e confirmar a hipótese. Contudo, dadas as limitações do trabalho e o facto desta área de investigação ser pouco explorada, é fulcral obter um maior número de estudo comparativos entre mais métodos de diferentes domínios e confirmar se apontam ou não para o mesmo fenómeno de fibrilhação. Apesar de terem sido implementados apenas dois métodos de análise dos EGMs, o projecto permitiu a comparação entre os mesmos, uma área de estudo por onde ainda há muito para investigar. Com mais conhecimento sobre os diferentes métodos, a sua aplicação, inter-relação e adequação no estudo dos mecanismos da FA e das propriedades electrofisiológicas desta patologia, é possível desenvolver procedimentos de ablação mais eficientes e selectivos, de forma a diminuir os riscos e aumentar a taxa de sucesso do tratamento.Atrial fibrillation (AF) is the most frequent cardiac arrhythmia in clinical practice and is described by rapid and irregular contractions of the atria. Despite catheter ablation (CA) being a well-established treatment for AF, it is sub-optimal, with a success rate of approximately 50 % after a single procedure, with some patients requiring multiple procedures to achieve long-term freedom from this pathology. This prompted the proposal and development of various quantitative electrogram (EGM)-based methods along with different mapping systems with their respective mapping techniques, to better understand the mechanisms responsible for initiating and maintaining AF, thus improving ablation outcomes. However, this diversification of methods and tools resulted in disperse and inconsistent data regarding the mechanisms of AF. This work consisted of employing two different methods to analyse the electrograms (EGM): dominant frequency (DF) and Shannon entropy (ShEn). From these EGMs, metrics were then extracted and displayed in colour-coded fashion on a 3D mesh of the left atrium (LA) from patients with paroxysmal or persistent AF. The two methods were compared to understand whether or not these indicated different phenomena/mechanisms, and if these could locate sites suspected of triggering and maintaining AF. The results, while not fully conforming to the literature, allowed the comparison between different EGM analysis methods, a field of study that requires further research. Overall, this project highlighted the limited data available within the topic, hindering our understanding of AF mechanisms and development of more effective and selective ablation procedures to avoid unnecessary complications, and ultimately improve the effects of the treatment's outcomes

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

Modeling Human Atrial Patho-Electrophysiology from Ion Channels to ECG - Substrates, Pharmacology, Vulnerability, and P-Waves

Half of the patients suffering from atrial fibrillation (AF) cannot be treated adequately, today. This thesis presents multi-scale computational methods to advance our understanding of patho-mechanisms, to improve the diagnosis of patients harboring an arrhythmogenic substrate, and to tailor therapy. The modeling pipeline ranges from ion channels on the subcellular level up to the ECG on the body surface. The tailored therapeutic approaches carry the potential to reduce the burden of AF

Modeling Human Atrial Patho-Electrophysiology from Ion Channels to ECG - Substrates, Pharmacology, Vulnerability, and P-Waves

Half of the patients suffering from atrial fibrillation (AF) cannot be treated adequately, today. This book presents multi-scale computational methods to advance our understanding of patho-mechanisms, to improve the diagnosis of patients harboring an arrhythmogenic substrate, and to tailor therapy. The modeling pipeline ranges from ion channels on the subcellular level up to the ECG on the body surface. The tailored therapeutic approaches carry the potential to reduce the burden of AF

Modeling Human Atrial Patho-Electrophysiology from Ion Channels to ECG - Substrates, Pharmacology, Vulnerability, and P-Waves

Half of the patients suffering from atrial fibrillation (AF) cannot be treated adequately, today. This book presents multi-scale computational methods to advance our understanding of patho-mechanisms, to improve the diagnosis of patients harboring an arrhythmogenic substrate, and to tailor therapy. The modeling pipeline ranges from ion channels on the subcellular level up to the ECG on the body surface. The tailored therapeutic approaches carry the potential to reduce the burden of AF