Signal Processing Techniques used for Analysis of Acoustic Signals from Heart and Lungs to Detect Pulmonary Edema in 2015

Sharma, Pratibha

Graduate Thesis Or Dissertation

Signal Processing Techniques used for Analysis of Acoustic Signals from Heart and Lungs to Detect Pulmonary Edema in 2015 Public Deposited

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Citeable URL: https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/xg94hp65x

Abstract

This thesis uses signal processing techniques on acoustic signals of the heart and lung that help measure the changes in the Congestive heart failure patient’s pulmonary edema from decompensated to compensated and vice versa. CHF is one of the leading causes of death worldwide leading to nearly 287,000 deaths annually [1]. This thesis investigates various signal processing techniques with the potential to remotely monitor congestive heart failure. Measurements are taken on diagnosed CHF patients who are compensated and decompensated to further monitor their progress in the management of their disease. The long term goal is to create an algorithm that can be used to determine when a person changes classification from healthy to heart failure as well as from compensated to decompensated so that readings may be taken in a home setting to improve health care and reduce the occurrence of emergency hospitalization. This thesis focuses on three signals: acoustic signals of the heart due to opening and closing of heart valves, acoustic signals of the lungs due to respiration and the electrocardiogram (ECG) signals. The research is based on the application of Fourier transform, short time Fourier transform and wavelet analysis to acoustic signals of the heart and lungs. The power spectra obtained through Fourier transform produced results that differentiated signals from healthy people and CHF patients, while, the short time Fourier transform (STFT) technique didn’t give envisaged results. The most promising results were derived through the wavelet analysis method. Wavelet transforms gives better resolution in time for higher frequencies and better resolution in frequencies for lower frequencies. Using wavelets, a difference between decompensated and compensated conditions are shown in the wavelet coefficients for the same patient that appears correlated with the weight of the patient.

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