Date of Award
Master of Science in Engineering
Electrical and Computer Engineering
Dr. Ikhlas Abdel-Qader
Dr. Raghe Gejji
Dr. Azim Houshyar
Cardiac sounds, phonocardiogram, wavelet, Hilbert-Huang transform, Support Vector Machine
Masters Thesis-Open Access
The Phonocardiogram (PCG) signal contains valuable information about the cardiac condition and is a useful tool in recognizing dysfunction and heart failure. By analyzing the PCG, early detection and diagnosis of heart diseases can be accomplished since many pathological conditions of the cardiovascular system cause murmurs or abnormal heart sounds. This thesis presents an algorithm to classify normal and abnormal heart sound signals using PCG. The proposed analysis is based on a framework composed of several statistical signal analysis techniques such as wavelet based de-noising, energy-based segmentation, Hilbert-Huang transform based feature extraction, and Support Vector Machine based classification. The MATLAB test results using PCG recordings validate the model used in the proposed system with very high classification accuracy averaged at 90.5%.
Tong, "An Integrated Framework for Cardiac Sounds Diagnosis" (2015). Master's Theses. 671.