الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Many physicians for a long time have been depending on heart sounds auscultation to diagnose different cardiac diseases. Although the electrocardiograph was later developed, there are still some diseases can’t be diagnosed. David S. Gerbarg, thirty years ago, took advantage of the time relations of the signal components to separate them based on the signal itself without a reference to ECG using a set of normal recordings.
The purpose of this study is to develop an algorithm for heart sound segmentation and diagnosis, which uses the heart sound signals as the sole source. Based on the algorithm, every cycle of the Phonocardiograph signals is separated into four parts: the first heart sound, the systolic period, the second heart sound and the diastolic period. The locations and intervals of the first heart sound and the second heart sound are computed first. Then, the intervals of the systolic and diastolic period are obtained consequently. Both normal and abnormal heart sound recordings are investigated.
In the intoduction, the thesis introduces the stethoscope, its construction, different heart sounds and the auscultation methods including best positions to auscultate the heart sounds.
Many methods of spectral estimation are used for heart sound analysis. The study is concentrated on the commonly used parametric analysis methods, that deals with the Autoregressive models and filter order using the Akaike criteria determination. On the other hand, the modern transform methods are briefly summerized and finally, it was concluded that the most suitable transform is the wavelet. Also, it was shown how to denoise the heart sound signal by using the wavelets.