Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Information Technology

This study built a classi{uFB01}cation model using hidden markov model (HMM) to recognize heart murmur and chest sounds. Diagnosis of congenital heart and chest defects is challenging, with some being diagnosed during pregnancy while others are diagnosed after birth or later on. Prompt diagnosis allows early intervention and best prognosis. Contemporary diagnosis relies upon the clinical examination, pulse oxime- tery, chest X-ray, electrocardiogram (ECG), echocardiography (ECHO), computed tomography (CT) and cardiac catheterization. These diagnostic modalities reliable upon recording electrical activity, sound waves or upon radiation. Yet, some of congenital heart and pulmonary diseases are still misdiagnosed be- cause of level of physician expertise. In an attempt to improve recognition of heart and chest sounds, we built a classi{uFB01}cation model for heart murmur and chest sounds recognition using hidden markov model (HMM). This study used mel frequency cepes- tral coe{uFB03}cient (MFCC) as a feature and 13 MFCC coe{uFB03}cients

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