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العنوان
Improving the recognition of heart murmur /
الناشر
Khaled Waleed Younis Rjoob ,
المؤلف
Khaled Waleed Younis Rjoob
هيئة الاعداد
باحث / Khaled Waleed Younis Rjoob
مشرف / Magd Ahmed Kotb
مشرف / Hesham N. Elmahdy
مشرف / Mohammed Ahmed Ahmed Refaey
تاريخ النشر
2016
عدد الصفحات
66 Leaves :
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
Information Systems
تاريخ الإجازة
17/6/2017
مكان الإجازة
جامعة القاهرة - كلية الحاسبات و المعلومات - Information Technology
الفهرس
Only 14 pages are availabe for public view

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from 83

Abstract

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