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.

Evaluation of semi-passive treatment technologies for septic lagoon capacity expansion

In Canada, increases in rural development has led to a growing need to effectively manage the resulting municipal and city sewage without the addition of significant cost- and energy- expending infrastructure. Storring Septic Service Limited is a family-owned, licensed wastewater treatment facility located in eastern Ontario. It makes use of a passive waste stabilization pond system to treat and dispose of waste and wastewater in an environmentally responsible manner. Storring Septic, like many other similar small-scale wastewater treatment facilities across Canada, has the potential to act as a sustainable eco-engineered facility that municipalities and service providers could utilize to manage and dispose of their wastewater. However, it is of concern that the substantial inclusion of third party material could be detrimental to the stability and robustness of the pond system. In order to augment the capacity of the current facility, and ensure it remains a self-sustaining system with the capacity to safely accept septage from other sewage haulers, it was hypothesized that pond effluent treatment could be further enhanced through the incorporation of one of three different technology solutions, which would allow the reduction of wastewater quality parameters below existing regulatory effluent discharge limits put in place by Ontario’s Ministry of the Environment and Climate Change (MOECC). Two of these solutions make use of biofilm technologies in order to enhance the removal of wastewater parameters of interest, and the third utilizes the natural water filtration capabilities of zebra mussels. Pilot-scale testing investigated the effects of each of these technologies on treatment performance under both cold and warm weather operation. This research aimed to understand the important mechanisms behind biological filtration methods in order to choose and optimize the best treatment strategy for full-scale testing and implementation. In doing so, a recommendation matrix was elaborated provided with the potential to be used as a universal operational strategy for wastewater treatment facilities located in environments of similar climate and ecology.