These studies are directed toward evaluating the prognostic power of the electrocardiogram when analyzed by advanced computer methodology and the predictive accuracy of diagnostic criteria when implemented in ECG computer programs. Appropriate use of digital signal processing in electrocardiography requires application of statistically based techniques of information theory and mathematically based engineering methods as well as knowledge about their clinical relevance. The Framingham Heart Study has shown that left ventricular hypertrophy (LVH) is an independent risk factor. Since more than 100 million ECGs are performed annually in the U.S., it is an important screening modality for LVH. Collaborative studies show that when age and body habitus indices, stratified by sex, are combined the E?G parameters, the accuracy for LVH diagnosis are significantly improved, resulting in earlier diagnoses in a larger population, especially in women. These studies have been redirected toward the analysis of ambulatory electrocardiography (AECGs). Despite extensive literature showing that information extracted by computer analysis of AECGs can be related to cardiac risk factors in this rapidly evolving field there are no standard methods for the routine analysis of AECGs. The objectives of this research are to carry forward previous work in biosignal analysis and adapt methodologies with the goal of implementing as much automation as possible to enable and expedite the interpretation of the huge streams of AECG data.
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