Improved Hybrid/GPU Algorithm for Solving Cardiac Electrophysiology Problems on Purkinje Networks

Cardiac Purkinje fibres provide an important pathway to the coordinated contraction of the heart. We present a numerical algorithm for the solution of electrophysiology problems across the Purkinje network that is efficient enough to be used in in-silico studies on realistic Purkinje networks with physiologically detailed models of ion exchange at the cell membrane. The algorithm is based on operator splitting and is provided with three different implementations: pure CPU, hybrid CPU/GPU, and pure GPU. Compared to our previous work, we modify the explicit gap junction term at network bifurcations in order to improve its mathematical consistency. Due to this improved consistency of the model, we are able to perform an empirical convergence study against analytical solutions. The study verified that all three implementations produce equivalent convergence rates, which shows that the algorithm produces equivalent result across different hardware platforms. Finally, we compare the efficiency of all three implementations on Purkinje networks of increasing spatial resolution using membrane models of increasing complexity. Both hybrid and pure-GPU implementations outperform the pure-CPU implementation, but their relative performance difference depends on the size of the Purkinje network and the complexity of the membrane model used

Application Of Digital Signal Analysis, Mass Data Acquisition and Processing Techniques, and Automated Experiment Protocols to the Study of Cardiac Cell Membrane Electrophysiology, with Mathematical Modeling

Traditional methods of collecting, analyzing and storing data from cardiac cell membrane electrophysiology experiments have become increasingly cumbersome and unwieldy as experimental protocols have become more sophisticated and complex. A global approach to collecting, analyzing, refining and storing electrophysiologic data, as well as a new approach to mathematical modeling of cell membrane single ion channel kinetics, was developed. This utilizes a comprehensive microcomputer based system of software with specialized analog and digital electronics for data acquisition, analysis and archiving. Unique discrete signal processing techniques for characterizing the electronic recording system, including specialized hardware and software adapted for minimizing distortions in biosignal recordings, are discussed in detail

Spatio-Temporal and Multisensory Integration: the relationship between sleep and the cerebellum

Does the cerebellum sleep? If so, does sleep contribute to cerebellar cognition? In this thesis, the sleep contribution to the consolidation process of spatial-temporal and multisensory integration was investigated in relation to the human cerebellum. Multiple experimental approaches were used to answer research questions addressed in the various chapters. Summarizing the evidence of the electrophysiology and neuroimaging studies, in Chapter1 we present intriguing evidence that the cerebellum is involved in sleep physiology, and that cerebellar-dependent memory formation can be consolidated during sleep. In Chapter 2, using functional neuroimaging in healthy participants during various forms of the Serial interception sequential learning (SISL) task, i.e., predictive timing, motor coordination, and motor imagination, we assessed the cerebellar involvement in spatio-temporal predictive timing; and possible cerebellar interactions with other regions, most notably the hippocampus. In Chapter 3, we add to the findings of Chapter 2 that indicate the cerebellum and hippocampus are involved in the task, by showing that more than simply activated, the cerebellum is a necessary and responsible region for the establishment of the spatio-temporal prediction. This follows from the deficits in behavioral properties of the predictive and reactive timing in the cerebellar ataxia type 6 patients, using the modified version of the SISL task. In Chapter 4, we assessed the subsequent post-interval behavioral performances on the learning of the fixed and random timing sequences in the SISL task, comparing a sleep group and wake group in healthy participants. Our findings show that sleep consolidates the process of cerebellar-dependent spatio-temporal integration. In Chapter 5, we investigated the establishment of visual-tactile integration during sleep through the examination of tactile motion stimulation during sleep and showed that, subsequent to sleep, directional visual motion discrimination i

Protective Role of False Tendon in Subjects with Left Bundle Branch Block: A Virtual Population Study.

False tendons (FTs) are fibrous or fibromuscular bands that can be found in both the normal and abnormal human heart in various anatomical forms depending on their attachment points, tissue types, and geometrical properties. While FTs are widely considered to affect the function of the heart, their specific roles remain largely unclear and unexplored. In this paper, we present an in silico study of the ventricular activation time of the human heart in the presence of FTs. This study presents the first computational model of the human heart that includes a FT, Purkinje network, and papillary muscles. Based on this model, we perform simulations to investigate the effect of different types of FTs on hearts with the electrical conduction abnormality of a left bundle branch block (LBBB). We employ a virtual population of 70 human hearts derived from a statistical atlas, and run a total of 560 simulations to assess ventricular activation time with different FT configurations. The obtained results indicate that, in the presence of a LBBB, the FT reduces the total activation time that is abnormally augmented due to a branch block, to such an extent that surgical implant of cardiac resynchronisation devices might not be recommended by international guidelines. Specifically, the simulation results show that FTs reduce the QRS duration at least 10 ms in 80% of hearts, and up to 45 ms for FTs connecting to the ventricular free wall, suggesting a significant reduction of cardiovascular mortality risk. In further simulation studies we show the reduction in the QRS duration is more sensitive to the shape of the heart then the size of the heart or the exact location of the FT. Finally, the model suggests that FTs may contribute to reducing the activation time difference between the left and right ventricles from 12 ms to 4 ms. We conclude that FTs may provide an alternative conduction pathway that compensates for the propagation delay caused by the LBBB. Further investigation is needed to quantify the clinical impact of FTs on cardiovascular mortality risk

Characterization and modeling of the purkinje system for biophysical simulations

The usability of computer models of the heart depends mostly on their capacity to accurately represent heart anatomy, microstructure and function. However, integrating such a variety of biological data is often not possible. This is the case of the cardiac conduction system (CCS), which is responsible for the fast and coordinated distribution of the electrical impulses. The CCS cannot be observed in-vivo but it is mandatory in several cardiac modeling applications involving arrhythmias. The aims of this thesis are to show the importance of explicitly modeling the CCS structure and function for an accurate description of the electrical activation of the ventricles and to present a novel technique to build automatically a CCS structure that meets physiological observations. Pursuing that goal has required a multidisciplinary effort to build models for cardiac electrophysiology, and imaging techniques to acquire and analyze data of the CCS at different scales.La usabilidad de modelos computacionales cardíacos depende del poder representar con precisión la anatomía del corazón, su microestructura y su función. Sin embargo, la integración de tal variedad de datos biológicos no siempre es posible. Este es el caso del sistema de conducción cardiaco (CCS), que es responsable de la distribución rápida y coordinada de los impulsos eléctricos. El CCS no puede ser observado in vivo pero es imprescindible en los modelos del corazón que involucran las arritmias. Los objetivos de esta tesis son el modelar la estructura y función del CCS para obtener una descripción precisa de la activación eléctrica del corazón y el construir la estructura de un CCS que cumpla con las observaciones fisiológicas. La persecución de este objetivo ha requerido un esfuerzo multidisciplinar para construir modelos de la electrofisiología cardiaca y las técnicas de imagen necesarias para adquirir y analizar datos del CCS a diferentes escalas

Multiscale Modeling of the Ventricles: From Cellular Electrophysiology to Body Surface Electrocardiograms

This work is focused on different aspects within the loop of multiscale modeling: On the cellular level, effects of adrenergic regulation and the Long-QT syndrome have been investigated. On the organ level, a model for the excitation conduction system was developed and the role of electrophysiological heterogeneities was analyzed. On the torso level a dynamic model of a deforming heart was created and the effects of tissue conductivities on the solution of the forward problem were evaluated

Exploration of the Human Purkinje Network in Virtual Populations

This thesis investigates the Purkinje network (PN) and its dependency on the heart shape (HS) through cardiac simulation on virtual populations (VPs). The heart is a complex organ and essential to the wellbeing of humans; its dysfunction is responsible for more than 27% of all deaths in the UK. The PN delivers the activation impulse to the ventricles of the heart and ensures their synchronous activation. Thus, the morphology of the PN is important, but it varies between species and in vivo imaging is not feasible. However, computer simulation could provide an alternative experimental tool. In simulation of the cardiac electrophysiology, the PN is often replaced by stimulus points on the HS that are fitted to physiological measurements (heart activation times, ECG). Thus, not allowing the study of the PN morphology, nor studies of arrhythmia involving re-entry into the PN. In this thesis, three studies involving explicit models of PNs have been conducted. First, an efficient algorithm for solving electrophysiology models for the PN is introduced. These allow performing simulations of physiological activations. To minimise the time for simulations, parallelisation with CPU and GPU architectures are investigated, which is of interest for VP studies. In the second study, false tendons (FTs) are studied, which provide an additional connection from the left bundle branch (LBB) and are potentially beneficial in case of LBB block. Therefore, the reduction in activation times by FT is studied as a function of the HS. In the third study, an automatically generated VP is used to explore uncertainty in the PN morphology. The conjecture is that the PN structure adapts to the HS. The coverage of the septum and the minimum distance of the PN to the base are varied. The features of the resulting ECG are used to find the PN that gives maximally synchronised contraction

Inverse estimation of the cardiac purkinje system from electroanatomical maps

Las enfermedades cardiovasculares son la primera causa de mortalidad en el mundo, con 17.7 millones de muertes cada año, aproximadamente el 31% de las muertes en todo el mundo (Organización Mundial de la Salud (OMS) 2018). Las arritmias ventriculares son una causa importante de muerte súbita, que representa aproximadamente la mitad de la mortalidad cardíaca. Algunas de esas arritmias se atribuyen a la red de Purkinje (PKN), que bajo ciertas condiciones puede generar ritmos focales automáticos, y su configuración de red puede sostener circuitos eléctricos reentrantes. Los ritmos focales originados desde la red de Purkinje pueden servir como puntos de inicio en casos de fibrilación ventricular en un amplio espectro de pacientes. El manejo de las enfermedades eléctricas cardíacas es un área clínica en expansión. Las nuevas tecnologías de imágenes y mapeo no invasivas, permiten adquirir imágenes clínicas de alta resolución (MRI, CT) que se pueden utilizar para localizar y caracterizar el tejido cardíaco patológico. Además, los sistemas de navegación electroanatómica (EAM) pueden ayudar al electrofisiólogo a encontrar las fuentes de actividad o circuitos arritmogénicos que mantienen la arritmia y eliminarlos mediante ablación por radiofrecuencia (RFA). A pesar de todos los avances técnicos, los tratamientos clínicos para esas enfermedades todavía se perciben como subóptimos, con tasas de éxito del tratamiento a largo plazo en el rango de 60 a 65%. Por lo tanto, existe una necesidad imperiosa de mejorar los resultados clínicos en beneficio de los pacientes y el sistema de salud. El área del modelado biofísico computacional ha comenzado a penetrar en entornos clínicos en unos pocos hospitales tecnológicamente avanzados y orientados a la investigación en el mundo. El objetivo principal de estas técnicas es el desarrollo de modelos 3D realistas de diferentes órganos, como el corazón, que incluyen, con un alto grado de detalle, características genéticas de las corrientes iónicas, sus mutaciones, las características electrofisiológicas de los diferentes tipos de células cardíacas, la estructura anatómica de los tejidos cardíacos y, en general, del cuerpo humano. A continuación, los modelos se utilizan para simular la función cardíaca, por ejemplo, electrofisiología, para tratar de estratificar a los pacientes o mejorar la planificación y ejecución de la terapia. Los enfoques por computador aún se enfrentan a varios desafíos que impiden su penetración completa en entornos clínicos. Podría decirse que uno de los obstáculos más importantes es el tiempo y la experiencia necesarios para construir un modelo del corazón personalizado a paciente, incluso si todos los datos clínicos necesarios están disponibles. En ese sentido, uno de los componentes del modelo que se ha mantenido elusivo a los modeladores ha sido la PKN, que es clave para la electrofisiología cardíaca. La razón principal es que debido a sus pequeñas dimensiones no existe una técnica clínica con resolución suficiente para permitir su visualización in vivo. El objetivo principal de esta tesis es desarrollar una metodología capaz de estimar inversamente un PKN reducido de paciente a partir de su EAM. Eso implica, primero encontrar en el EAM las fuentes de activación eléctrica, llamadas uniones de Purkinje-miocardio (PMJ), y seguir la estructura que interconecta esos PMJ y reproduce la secuencia de activación del paciente. En resumen, las principales contribuciones de esta tesis son: - Metodología para estimar los PMJ, o las fuentes de actividad eléctrica, sobre una representación 3D del endocardio ventricular, proporcionada por un EAM. El método desarrollado puede procesar directamente los datos adquiridos por un electrofisiólogo en el Cathlab, volver a anotar los tiempos en las muestras adquiridas y obtener las ubicaciones de los PMJs y los tiempos de activación, considerando explicitamente ruido en las muestras. - Metodología para estimar el PKN del paciente a partir de los PMJ estimados, que es capaz de reproducir la secuencia de activación eléctrica del paciente con un error mínimo. El método ha sido validado tanto en EAM sintéticos como en 28 EAM reales, mostrando errores de unos pocos milisegundos. Además, se ha utilizado un PKN estimado para simular el ECG virtual de un paciente, donse se observa coincidencia entre el ECG real y el simulado. En conclusión, he desarrollado y validado una metodología que permite la estimación de la PKN de un paciente con errores mínimos en la secuencia de activación, y que puede usarse para personalizar simulaciones biofísicas del corazón o ayudar al electrofisiólogo en la planificación de intervenciones de RFA.Cardiovascular disease is the number one cause of mortality in the world, accounting for 17.7 million deaths each year, an estimated 31% of all deaths worldwide (World Health Organization (WHO) 2018). Ventricular arrhythmias are a major cause of sudden death, which accounts for approximately half of cardiac mortality. Some of those arrhythmias are attributed to the Purkinje network (PKN), which under certain conditions can generate both automatic and triggered focal rhythms, and its network configuration can sustain re‑entrant circuits. Focal Purkinje triggers can serve as initial points of ventricular fibrillation in a wide spectrum of patients. The management of cardiac electrical diseases is an expanding clinical activity. New non-invasive imaging and mapping technologies, allow to acquire high resolution clinical images (MRI, CT) that can be used to localize and characterize pathological cardiac tissue. Furthermore, electroanatomical navigating (EAM) systems, can aid electrophysiologist to find the sources of arrhythmogenic activity or circuits maintaining arrhythmia, and eliminate them by radio-frequency ablation (RFA). Despite all the technical advances, overall clinical outcome for those diseases is still perceived as suboptimal, with long-term treatment success rates in the range of 60 to 65%. Therefore, there is a compelling need to improve clinical outcomes for the benefit of the patients and the healthcare system. The area of computational biophysical modeling has already started to penetrate in clinical environments in a few technologically advanced research oriented hospitals in the world. The main objective of these techniques is the development of realistic 3D models of different organs, such as the heart, that include, with a high degree of detail, genetic characteristics of the ionic currents, their mutations, the electrophysiological characteristics of the different cardiac cell types, the anatomical structure of cardiac tissues, and in general of the human body. Following, the models are used to simulate the heart function, e.g., electrophysiology, to try to stratify patients or improve therapy planning and delivery. Computer-based approaches are still facing several challenges that prevent their complete penetration into clinical environments. Arguably, one of the most important obstacles is the time and expertise required to build a patient-specific model of the heart, even if all necessary clinical data are available. In that sense, one of the model components that has remained largely elusive to modelers has been the PKN, which is key for cardiac electrophysiology. The main reason is that due to its small dimensions there is no clinical technique with enough resolution to allow its visualization in vivo. The main purpose of this thesis is to develop a methodology able to inversely estimate a reduced PKN of patient from his EAM. That involves, first, finding in the EAM the sources of electrical activation, so called Purkinje-myocardial junctions (PMJs), and, following, finding the structure that interconnects those PMJs and reproduces the patient sequence of activation. In summary, the main contributions of this thesis are: - Methodology to estimate the PMJs, or the sources of electrical activity, from a 3D representation of the ventricular endocardium provided by an EAM. The method developed can process directly the data acquired by an electrophysiologist in the Cathlab, re-annotate the time samples, and obtain the PMJ locations and activation times, explicitly considering noise in the samples. - Methodology to estimate the patient PKN from the estimated PMJs, that is able to reproduce the patient's sequence of electrical activation with a minimal error. The method has been validated on synthetic EAMs as well as in 28 real EAMs, showing errors of a few milliseconds. In addition, an estimated PKN has been used to simulate the virtual ECG of a patient, showing a good match with the clinical one. In conclusion, I have developed and validated a methodology that permits the estimation of a patient's PKN with small errors in the sequence of activation, that can be used to personalize biophysical simulations of the heart or aid electrophysiologist in the planning of RFA interventions

Real-time analysis of video signals

Many practical and experimental systems employing image processing techniques have been built by other workers for various applications. Most of these systems are computer-based and very few operate in a real time environment. The objective of this work is to build a microprocessor-based system for video image processing. The system is used in conjunction with an on-line TV camera and processing is carried out in real time. The enormous storage requirement of digitized TV signals and the real time constraint suggest that some simplification of the data must take place prior to any viable processing. Data reduction is attained through the representation of objects by their edges, an approach often adopted for feature extraction in pattern recognition systems. A new technique for edge detection by applying comparison criteria to differentials at adjacent pixels of the video image is developed and implemented as a preprocessing hardware unit. A circuit for the generation of the co-ordinates of edge points is constructed to free the processing computer of this task, allowing it more time for on-line analysis of video signals. Besides the edge detector and co-ordinate generator the hardware built consists of a microprocessor system based on a Texas Instruments T.US 9900 device, a first-in-first-out buffer store and interface circuitry to a TV camera and display devices. All hardware modules and their power supplies are assembled in one unit to provide a standalone instrument. The problem chosen for investigation is analysis of motion in a visual scene. Aspects of motion studied concern the tracking of moving objects with simple geometric shapes and description of their motion. More emphasis is paid to the analysis of human eye movements and measurement of its point-of-regard which has many practical applications in the fields of physiology and psychology. This study provides a basis for the design of a processing unit attached to an oculometer to replace bulky minicomputer-based eye motion analysis systems. Programs are written for storage, analysis and display of results in real time