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.

Variable 360° Vector-Sum Phase Shifter With Coarse and Fine Vector Scaling

A CMOS vector-sum phase shifter covering the full 360° range is presented in this paper. Broadband operational transconductance amplifiers with variable transconductance provide coarse scaling of the quadrature vector amplitudes. Fine scaling of the amplitudes is accomplished using a passive resistive network. Expressions are derived to predict the maximum bit resolution of the phase shifter from the scaling factor of the coarse and fine vector-scaling stages. The phase shifter was designed and fabricated using the standard 130-nm CMOS process and was tested on-wafer over the frequency range of 4.9–5.9 GHz. The phase shifter delivers root mean square (rms) phase and amplitude errors of 1.25° and 0.7 dB, respectively, at the midband frequency of 5.4 GHz. The input and output return losses are both below 17 dB over the band, and the insertion loss is better than 4 dB over the band. The circuit uses an area of 0.303 mm2 excluding bonding pads and draws 28 mW from a 1.2 V supply.