Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.

DIGITAL HISTORY: DESIGNING VIRTUAL ENVIRONMENTS THAT SUPPORT CRITICAL THINKING AND IDEA EXCHANGE, MOTIVATE AND ENGAGE USERS

In my thesis I argue for the use of system designs that: a) open access to a variety of users and allow for collaboration and idea exchange, while at the same time, b) are designed to motivate and engage users. To exemplify my proposed systems design, I created an interactive and open digital history project focused on Romanian culture and identity during Communism, from 1947, when the Communist Party took power by forcing the King to abdicate, until the revolution in 1989, which marked the end of Communism in Romania (Gilberg, 1990, Boia, 2014). In my project, I present the possibility to recreate Habermas’ notion of public sphere and “the unforced force of the better argument” (Habermas, 1989) and Dewey’s (2004) understanding of democracy as a mode of associated living imbued of the spirit of inquiry within contemporary digital history projects. Second, I outline system designs that motivate and engage users, by satisfying the basic psychological needs outlined in Ryan and Deci’s (2000) self-determination theory: autonomy, competence, and relatedness. Two more concepts are included to complete the proposed digital history project design: presence (Ryan, Rigby, & Przybylski, 2006) and learner hero (Rigby & Przybylski, 2009).