Cardiac arrhythmias are caused by disordered propagation of electrical activity. Progress in understanding and controlling arrhythmias requires novel methods to characterize and control the spatiotemporal propagation of electrical activity. We used patterned illumination of cardiomyocytes derived from optogenetic human induced pluripotent stem cells to create dynamic conduction blocks, and to test spatially extended control schemes. Using this model, we demonstrated the ability to initiate, circumscribe, relocate, and terminate pathologic spiral waves that drive many arrhythmias. When cells were derived from patients with long QT syndrome, longer action potential durations made spiral waves more resistant to termination. This work lays the foundation for personalized models of cardiac injury and disease, and the development of tailored approaches to the management of arrhythmias.

Non-invasive electrical imaging of the heart aims to quantitatively reconstruct information about the electrical activity of the heart from multiple thoracic ECG signals. The computational framework required to produce such electrical images of the heart from non-invasive torso surface signals is presented. It is shown reliable electrical images of the heart can be obtained under a controlled environment. This has been demonstrated using an anatomically realistic boundary element porcine torso model. The procedures required to create a subject specific model using a small number of control points and to create a specific heart model from three-dimensional ultrasound images using a linear fitting procedure are presented. From discrete ECG electrodes a continuous representation of the potential field over the entire torso surface can also be produced using this linear fitting procedure. The construction of the transfer matrices for the two predominant electrocardiographic sources (epicardial potentials and myocardial activation times) are described in detail. The transfer matrices are used to compute activation times within the heart and epicardial potentials on the heart surface. Myocardial activation times are computed using an algorithm based on the Critical Point Theorem while epicardial potentials are computed using standard Tikhonov and Truncated SVD spatially regularised methods as well as Greensite's spatial and temporal regularisation method. The regularisation parameters for the epicardial potentials are determined using a variety of methods (e.g., CRESO criterion, L-curve, zero-crossing). The potential and activation based formulations are compared in a comprehensive inverse simulation study. To try and capture the dynamic and variable nature of cardiac electrical activity, the study is performed with three different types of cardiac sources with a realistic porcine model. These simulations investigate the effect on the computed solutions of individual and combinations of modelling errors. These errors include corruption in the torso surface signals, changes in material properties and geometric distortion. In general, the activation based formulation is preferred over the epicardial potential formulations, with Greensite's method found to be the best method for reconstructing epicardial potentials. Under optimal conditions, the activation approach could reconstruct the activation times to within RMS. Both potential and activation based formulations were found to be relatively insensitive to changes in material properties such as lung conductivities and activation function shapes. When examining individual errors, the geometry and positions of the torso and heart had the greatest effects on the inverse solutions. The relative heart position needed to be determined to within to obtain results within of the solutions obtained under control conditions. When the modelling errors are combined to produce errors which can be expected in a clinical or experimental situation the activation based solutions were consistently more accurate than potential based solutions. The next necessary step in this project is the detailed validation of the results against in-vivo data. This step is necessary before such algorithms can be reliably used to aid in the assessment of heart function in a clinical environment.

Non-invasive electrical imaging of the heart aims to quantitatively reconstruct information about the electrical activity of the heart from multiple thoracic ECG signals. The computational framework required to produce such electrical images of the heart from non-invasive torso surface signals is presented. It is shown reliable electrical images of the heart can be obtained under a controlled environment. This has been demonstrated using an anatomically realistic boundary element porcine torso model. The procedures required to create a subject specific model using a small number of control points and to create a specific heart model from three-dimensional ultrasound images using a linear fitting procedure are presented. From discrete ECG electrodes a continuous representation of the potential field over the entire torso surface can also be produced using this linear fitting procedure. The construction of the transfer matrices for the two predominant electrocardiographic sources (epicardial potentials and myocardial activation times) are described in detail. The transfer matrices are used to compute activation times within the heart and epicardial potentials on the heart surface. Myocardial activation times are computed using an algorithm based on the Critical Point Theorem while epicardial potentials are computed using standard Tikhonov and Truncated SVD spatially regularised methods as well as Greensite's spatial and temporal regularisation method. The regularisation parameters for the epicardial potentials are determined using a variety of methods (e.g., CRESO criterion, L-curve, zero-crossing). The potential and activation based formulations are compared in a comprehensive inverse simulation study. To try and capture the dynamic and variable nature of cardiac electrical activity, the study is performed with three different types of cardiac sources with a realistic porcine model. These simulations investigate the effect on the computed solutions of individual and combinations of modelling errors. These errors include corruption in the torso surface signals, changes in material properties and geometric distortion. In general, the activation based formulation is preferred over the epicardial potential formulations, with Greensite's method found to be the best method for reconstructing epicardial potentials. Under optimal conditions, the activation approach could reconstruct the activation times to within RMS. Both potential and activation based formulations were found to be relatively insensitive to changes in material properties such as lung conductivities and activation function shapes. When examining individual errors, the geometry and positions of the torso and heart had the greatest effects on the inverse solutions. The relative heart position needed to be determined to within to obtain results within of the solutions obtained under control conditions. When the modelling errors are combined to produce errors which can be expected in a clinical or experimental situation the activation based solutions were consistently more accurate than potential based solutions. The next necessary step in this project is the detailed validation of the results against in-vivo data. This step is necessary before such algorithms can be reliably used to aid in the assessment of heart function in a clinical environment.

Non-invasive electrical imaging of the heart aims to quantitatively reconstruct information about the electrical activity of the heart from multiple thoracic ECG signals. The computational framework required to produce such electrical images of the heart from non-invasive torso surface signals is presented. It is shown reliable electrical images of the heart can be obtained under a controlled environment. This has been demonstrated using an anatomically realistic boundary element porcine torso model. The procedures required to create a subject specific model using a small number of control points and to create a specific heart model from three-dimensional ultrasound images using a linear fitting procedure are presented. From discrete ECG electrodes a continuous representation of the potential field over the entire torso surface can also be produced using this linear fitting procedure. The construction of the transfer matrices for the two predominant electrocardiographic sources (epicardial potentials and myocardial activation times) are described in detail. The transfer matrices are used to compute activation times within the heart and epicardial potentials on the heart surface. Myocardial activation times are computed using an algorithm based on the Critical Point Theorem while epicardial potentials are computed using standard Tikhonov and Truncated SVD spatially regularised methods as well as Greensite's spatial and temporal regularisation method. The regularisation parameters for the epicardial potentials are determined using a variety of methods (e.g., CRESO criterion, L-curve, zero-crossing). The potential and activation based formulations are compared in a comprehensive inverse simulation study. To try and capture the dynamic and variable nature of cardiac electrical activity, the study is performed with three different types of cardiac sources with a realistic porcine model. These simulations investigate the effect on the computed solutions of individual and combinations of modelling errors. These errors include corruption in the torso surface signals, changes in material properties and geometric distortion. In general, the activation based formulation is preferred over the epicardial potential formulations, with Greensite's method found to be the best method for reconstructing epicardial potentials. Under optimal conditions, the activation approach could reconstruct the activation times to within RMS. Both potential and activation based formulations were found to be relatively insensitive to changes in material properties such as lung conductivities and activation function shapes. When examining individual errors, the geometry and positions of the torso and heart had the greatest effects on the inverse solutions. The relative heart position needed to be determined to within to obtain results within of the solutions obtained under control conditions. When the modelling errors are combined to produce errors which can be expected in a clinical or experimental situation the activation based solutions were consistently more accurate than potential based solutions. The next necessary step in this project is the detailed validation of the results against in-vivo data. This step is necessary before such algorithms can be reliably used to aid in the assessment of heart function in a clinical environment.

Non-invasive electrical imaging of the heart aims to quantitatively reconstruct information about the electrical activity of the heart from multiple thoracic ECG signals. The computational framework required to produce such electrical images of the heart from non-invasive torso surface signals is presented. It is shown reliable electrical images of the heart can be obtained under a controlled environment. This has been demonstrated using an anatomically realistic boundary element porcine torso model. The procedures required to create a subject specific model using a small number of control points and to create a specific heart model from three-dimensional ultrasound images using a linear fitting procedure are presented. From discrete ECG electrodes a continuous representation of the potential field over the entire torso surface can also be produced using this linear fitting procedure. The construction of the transfer matrices for the two predominant electrocardiographic sources (epicardial potentials and myocardial activation times) are described in detail. The transfer matrices are used to compute activation times within the heart and epicardial potentials on the heart surface. Myocardial activation times are computed using an algorithm based on the Critical Point Theorem while epicardial potentials are computed using standard Tikhonov and Truncated SVD spatially regularised methods as well as Greensite's spatial and temporal regularisation method. The regularisation parameters for the epicardial potentials are determined using a variety of methods (e.g., CRESO criterion, L-curve, zero-crossing). The potential and activation based formulations are compared in a comprehensive inverse simulation study. To try and capture the dynamic and variable nature of cardiac electrical activity, the study is performed with three different types of cardiac sources with a realistic porcine model. These simulations investigate the effect on the computed solutions of individual and combinations of modelling errors. These errors include corruption in the torso surface signals, changes in material properties and geometric distortion. In general, the activation based formulation is preferred over the epicardial potential formulations, with Greensite's method found to be the best method for reconstructing epicardial potentials. Under optimal conditions, the activation approach could reconstruct the activation times to within RMS. Both potential and activation based formulations were found to be relatively insensitive to changes in material properties such as lung conductivities and activation function shapes. When examining individual errors, the geometry and positions of the torso and heart had the greatest effects on the inverse solutions. The relative heart position needed to be determined to within to obtain results within of the solutions obtained under control conditions. When the modelling errors are combined to produce errors which can be expected in a clinical or experimental situation the activation based solutions were consistently more accurate than potential based solutions. The next necessary step in this project is the detailed validation of the results against in-vivo data. This step is necessary before such algorithms can be reliably used to aid in the assessment of heart function in a clinical environment.

Non-invasive electrical imaging of the heart aims to quantitatively reconstruct information about the electrical activity of the heart from multiple thoracic ECG signals. The computational framework required to produce such electrical images of the heart from non-invasive torso surface signals is presented. It is shown reliable electrical images of the heart can be obtained under a controlled environment. This has been demonstrated using an anatomically realistic boundary element porcine torso model. The procedures required to create a subject specific model using a small number of control points and to create a specific heart model from three-dimensional ultrasound images using a linear fitting procedure are presented. From discrete ECG electrodes a continuous representation of the potential field over the entire torso surface can also be produced using this linear fitting procedure. The construction of the transfer matrices for the two predominant electrocardiographic sources (epicardial potentials and myocardial activation times) are described in detail. The transfer matrices are used to compute activation times within the heart and epicardial potentials on the heart surface. Myocardial activation times are computed using an algorithm based on the Critical Point Theorem while epicardial potentials are computed using standard Tikhonov and Truncated SVD spatially regularised methods as well as Greensite's spatial and temporal regularisation method. The regularisation parameters for the epicardial potentials are determined using a variety of methods (e.g., CRESO criterion, L-curve, zero-crossing). The potential and activation based formulations are compared in a comprehensive inverse simulation study. To try and capture the dynamic and variable nature of cardiac electrical activity, the study is performed with three different types of cardiac sources with a realistic porcine model. These simulations investigate the effect on the computed solutions of individual and combinations of modelling errors. These errors include corruption in the torso surface signals, changes in material properties and geometric distortion. In general, the activation based formulation is preferred over the epicardial potential formulations, with Greensite's method found to be the best method for reconstructing epicardial potentials. Under optimal conditions, the activation approach could reconstruct the activation times to within RMS. Both potential and activation based formulations were found to be relatively insensitive to changes in material properties such as lung conductivities and activation function shapes. When examining individual errors, the geometry and positions of the torso and heart had the greatest effects on the inverse solutions. The relative heart position needed to be determined to within to obtain results within of the solutions obtained under control conditions. When the modelling errors are combined to produce errors which can be expected in a clinical or experimental situation the activation based solutions were consistently more accurate than potential based solutions. The next necessary step in this project is the detailed validation of the results against in-vivo data. This step is necessary before such algorithms can be reliably used to aid in the assessment of heart function in a clinical environment.

Stretch of cardiac muscle is known to activate various physiological processes that result in changes to cardiac function, contractility and electrophysiology. To date, however, the precise relationship between mechanical stretch and changes in the electrophysiology of the heart remain unclear. This relationship, termed mechanoelectric feedback (MEF), is thought to underlie many cardiac arrhythmias associated with pathological conditions. These electrophysiological changes are observed not only in the whole heart, but also at the single cardiomyocyte level, and can be explained by the presence of stretch-activated ion channels (SACs). Most investigations of the actions of stretch have concentrated on these sacrolemmal ionic currents thought responsible for the proposed MEF-induced changes in contractility. While these studies have provided some useful insight into possible mechanisms, the inappropriate use of solutions and non-physiological degrees of stretch, may have caused somewhat misleading results. Currently, little is known about the involvement or contribution of non-selective or K+ selective SACs to the normal cardiac cycle. Here, I investigate the concept that stretch-induced changes in cardiac electrophysiology (MEF) are important in normal cardiac cycle and demonstrate the effects of stretch on the Frank-Starling mechanism (stretch induced increases in cardiac contractility) while pharmacologically manipulating stretch-activated ion currents. Experiments were conducted using a number of agents known to influence stretch-activated channels either in a positive or antagonistic manner. Results proved somewhat negative toward MEF theory with only substantial or pathological levels of stretch being able to elicit any electrophysiological change in the heart. Furthermore, where electrophysiological changes were associated with pathological stretch they were not consistently modulated by stretch-activated ion channel activators or blockers. Of equal importance was the observation that smaller levels of myocardial stretch associated with positive changes in contractility via the Frank-Starling mechanism were not associated with any electrophysiological changes in the Langendorff perfused heart (as observed by monophasic action potentials) nor in isolated muscle preparations (as observed through transcellular membrane potential recordings). As such, the present research undertaken in this thesis confirms an absence of electrophysiological changes with stretch except under extreme conditions suggesting that MEF is not a robust and necessarily repeatable phenomenon in the mammalian heart. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1320476 / Thesis (Ph.D.) - University of Adelaide, School of Molecular and Biomedical Science, 2008

In this study, lumped parameter cardiovascular modeling has been used to understand the influence of muscle properties on mechanical dyssynchrony (MD) as well as general muscle dynamics. Incorporating viscous influence into the model allowed for an expanded view when analyzing muscle parameter response to MD. A unique method of ventricle segmentation was introduced that allowed fast analysis of regional and global ventricular properties. This segmentation process produced a ventricle with four identical sections each consisting of separately tunable muscle properties in the form of minimum and maximum elastance, elastance waveform delay, and myocardial viscous friction, yet these regional sections remained globally dependent. Elastance waveform delay proved to be the most influential property on MD as measured by internal flow fraction (IFF), followed by regional elastance magnitude, and finally regional viscosity influence. Due to the unique segmentation of this model, two metrics for IFF were derived: (1) the "true" IFF (IFF-4seg) and (2) the IFF as would be measured by an ideal conductance catheter (IFF-CC). The results of IFF-CC versus IFF-4seg show that conductance catheters are not capable of measuring IFF during a side-to-side volume transfer within the stacked cylinder under measurement. Finally, unique energetic situations were observed with this model that point to likely myocardium remodeling situations.