The overall objective of proposed work is to develop and evaluate improved methods for providing robust estimates of spatial distributions of cardiac electrophysiologic properties from extracellular potential fields. The rationale for the studies is the need for sensitive techniques to better characterize cardiac behavior from direct (invasive) and indirect (non-invasive) measurements, particularly important for assessing diseased myocardium where signals have low amplitude and are often fractionated. Specific objectives include: 1) development and evaluation of a new technique, multi-reference histograms (MRHs), for providing estimates of local, myocardial activation and recovery times, 2) assessment of utility of MRHs for delineating electrically active from passive (non-depolarizing) tissue and for detecting multiple, close but uncoupled activation fronts, 3) characterization of 2 and 3 dimensional, macro-level myocardial anisotropy and inhomogeneity by electrical and statistical means, 4) development and evaluation of methods using non-linear time alignment, spatial correlation and electrocardiographic deflection areas to quantitatively compare subtle differences and detect small changes in cardiac behavior, both locally and globally, 5) assessment of the extent to which measurement uncertainty, data dimensionality and sampling completeness affect information transfer from the heart to the body surface, and 6) application of inverse techniques to the problem of localizing early ventricular depolarization from epicardial and cavitary potential distributions. Objectives will be met through theoretical and experimental studies. Significance of the proposed work lies in the likelihodd that improved methods for characterizing cardiac electrophysiology at the macro level will lead to improvement in distinguishing normal and abnormal cardiac electrical activity, reduction of uncertainty in the interpretation of cardiac electrical signals, and improved assessment of cardiac events from body surface potentials. Health relatedness of the work lies in the need for sensitive, non-invasive methods to detect and characterize cardiac diseases and conditions early in and during their evolution.
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