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.

The Olivia Framework:Concepts for Use in Finely-Grained, Integrated Social Policy Analysis (Working Paper 45)

The Olivia framework is a set of concepts and measures that, when mature, will allow users to describe, in a consistent and integrated manner, everything about individuals and institutions that is of potential interest to social policy. The present paper summarizes the current stage of development in achieving this highly ambitious goal. The current version of the framework supports analysis of social trends and policy responses from many perspectives: • The point-in-time, resource-flow perspectives that underlie most traditional, economics-based policy analysis. • Life-course perspectives, including both transitions/trajectories analysis and asset-based analysis. • Spatial perspectives that anchor people in space and history and that provide a link to macro-analysis. • The perspective of the purposes/goals of individuals and institutions, including the objectives of different types of government programming. The concepts of the framework, which are all potentially measurable, provide a language that can support integrated analysis in all these areas at a much finer level of description than is customary. It provides a language that is especially well suited for analysis of the incremental policy changes that are typical of a mature welfare state. It supports both qualitative and quantitative analysis, enabling some integration between the two. It supports citizen-centric as well as a government-centric view of social policy. In its current version, the concepts are most highly developed as they related to social policies as they related to labour markets, equality and social integration, care-giving, immigration, income security, sustainability, and social and economic well-being more generally. However the paper points to likely extensions in the areas of health, justice and safety.