The monophasic action potential: Applications in myocardial ischemia

Ischemic heart disease is a condition which involves the lack of blood flow and oxygen to heart muscle, often caused by narrowed heart arteries. A patient experiencing this condition may experience chest pain known as angina. Angina is often a warning sign for a myocardial infarction (MI). According to the American Heart Association, of the 15.8 million Americans who have this type of coronary heart disease, approximately 8 million have suffered an MI and 9 million have suffered angina pectoris [1]. The focus of this thesis is to understand further and quantitatively assess myocardial ischemia in terms of the monophasic action potential (MAP).;The goals of this thesis are accomplished in several stages. A literature review was first conducted in order to summarize the current state of MAP analysis. Current technologies and trends at the forefront of MAP physiology are reviewed. A series of in situ and in vitro acute experiments with a swine model of cardiac ischemia were used to collect data from normal and ischemic hearts. In order to acquire the signal, a high fidelity circuit was designed, tested, and developed with a unique combination of sensing electrodes with a minute sensing radius, enabling insight into local electrophysiological changes.;Another phase of this research involved the development of a high-speed, photodiode array based imaging system to optically map cardiac excitation wavefronts from isolated myocardial preparations. This system was designed to be robust enough to image Langendorff perfused whole heart preparations as well.;To facilitate the process of data analysis, an algorithm was developed to automatically annotate the MAP signal via an interactive graphical user interface. This algorithm incorporates error checking mechanisms to minimize the need for manual intervention. The results were validated with annotation results from two independent observers who examined identical data sets.;Finally, this research involved the extraction of numerous features from paired ischemic and normal data sets in order to find the best characteristic that separates the two states. The algorithm employs a means clustering algorithm implemented in multidimensional space.;The findings of this thesis have clinical relevance for advancing the early detection of myocardial ischemia.;References. 1. Rosamond W. et al. "Heart disease and stroke statistics---2007 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee." Circulation. 2007 Feb 6;115(5): p.69--171.