Signal Processing Techniques used for Detection of Pulmonary Edema

This research work enumerates signal processing techniques on acoustic signals of the heart and lung that help measure the changes in the Congestive Heart Failure(CHF) patient's pulmonary edema from decompensated to compensated and vice versa. CHF is one of the leading causes of death worldwide resulting in nearly 287,000 deaths annually. This work 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 work 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 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.