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.

INVESTIGATING THE MECHANISM OF AUTOIMMUNE DISEASE ASSOCIATED-LQTS

The human ether-a-go-go-related gene (hERG) protein passes the rapidly activating delayed rectifier potassium channel (IKr), and malfunction of hERG protein/IKr is the primary cause of acquired long QT syndrome (LQTS). Autoimmune diseases are significantly correlated with prolonged QT intervals, for which autoantibodies have been implicated. The anti-Ro52 autoantibody is the most frequently evaluated, and importantly has been correlated with prolonged QT intervals. Pathological anti-Ro52-hERG interactions have been discussed as a mechanism for autoimmune disease-related LQTS. However, the mechanism is unclear, and it does not explain LQTS in autoimmune diseases which do not commonly express anti-Ro52. In this thesis, I investigated the effects of anti-Ro52 on hERG/IKr function. Through Western blot analysis, whole-cell patch-clamp, and immunofluorescence, I show that anti-Ro52 chronically (12 h) reduced hERG protein expression and hERG current by over 50%, but did not acutely block the channel. My work revealed a novel mechanism in which the Fc portion of anti-Ro52 interacts with the extracellular S5-pore linker of the channel to induce internalization through a tyrosine phosphorylation dependent pathway. This phenomenon extends beyond anti-Ro52 IgG, as other IgG, regardless of their antigen binding specificity, have the potential to reduce hERG expression/current. Rather, the ability of IgG to reduce hERG expression and current is dependent on the IgG subclass, as we show mouse IgG2A was the only mouse IgG subclass which reduced hERG expression. These results provide a novel explanation for autoimmune disease associated LQTS. It also has implications in the development of safe monoclonal antibody drugs.

Processing the Intracardiac Electrogram for Atrial Fibrillation Ablation

Atrial fibrillation (AF) is a major global health issue as it is the most prevalent sustained supraventricular arrhythmia. Catheter-based ablation of some parts of the atria is considered an effective treatment of AF. The main objective of this research is to analyze atrial intracardiac electrograms (IEGMs) and extract insightful information for the ablation therapy. Throughout this thesis we propose several computationally efficient algorithms that take streams of IEGMs from different atrial sites as the input signals, sequentially analyze them in various domains (e.g., time and frequency), and create color-coded three-dimensional map of the atria to be used in the ablation therapy.