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Improving the recognition of heart murmur / Khaled Waleed Younis Rjoob ; Supervised Magd Ahmed Kotb , Hesham N. Elmahdy , Mohammed Ahmed Ahmed Refaey

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Khaled Waleed Younis Rjoob , 2016Description: 66 Leaves : charts , facsimiles ; 30cmOther title:
  • تحسين التعرف علي أصوات القلب الغير طبيعية [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Information Technology Summary: 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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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.01.M.Sc.2016.Kh.I (Browse shelf(Opens below)) Not for loan 01010110071882000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.01.M.Sc.2016.Kh.I (Browse shelf(Opens below)) 71882.CD Not for loan 01020110071882000

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

Issued also as CD

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