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An Integrated Framework For Cardiac Sounds Diagnosis, Zichun Tong
An Integrated Framework For Cardiac Sounds Diagnosis, Zichun Tong
Masters Theses
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 …
Wavelet-Based Non-Homogeneous Hidden Markov Chain Model For Hyperspectral Signature Classification, Siwei Feng
Wavelet-Based Non-Homogeneous Hidden Markov Chain Model For Hyperspectral Signature Classification, Siwei Feng
Masters Theses
Hyperspectral signature classification is a kind of quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at pixel level in the scene. The classification procedure can be operated directly on hyperspectral data or performed by using some features extracted from corresponding hyperspectral signatures containing information like signature energy or shape. In this paper, we describe a technique that applies non-homogeneous hidden Markov chain (NHMC) models to hyperspectral signature classification. The basic idea is to use statistical models (NHMC models) to characterize wavelet coefficients which capture the spectrum structural information at multiple levels. Experimental results …