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Cardiac arrhythmias classification using Support Vector Machines

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dc.contributor.author Mohamed, Tamador ElkhansaJaber
dc.contributor.author Supervisor - Megdi B. M. Amien
dc.date.accessioned 2014-12-15T11:58:15Z
dc.date.available 2014-12-15T11:58:15Z
dc.date.issued 2014-02-10
dc.identifier.citation Mohamed,Tamador ElkhansaJaber .Cardiac arrhythmias classification using Support Vector Machines/Tamador ElkhansaJaber Mohamed;Megdi B. M. Amien.-khartoum:Sudan University of Science and Technology,College of Engineering,2014.-85p:ill;28cm.-M.Sc. en_US
dc.identifier.uri http://repository.sustech.edu/handle/123456789/8898
dc.description thesis en_US
dc.description.abstract Heart diseases (HD) are the number one cause of death globally, more people die annually from HDs than from any other cause, according to World-Health-Organization (WHO) 7.3 million were died due to coronary heart disease in 2008. Electrocardiogram (ECG) interpretation is most widely used to detect the abnormality of the heart. A reliable computer programs could lead to enhanced visual interpretation, and significant-increase of diagnosis-efficiency. This study introduced a novel method for ECG classification; fifteen different records of five rhythms from “MIT-BIH” Arrhythmia Database have been used to evaluate the implemented algorithms. The proposed approach consists of three distinct stages. In the first stage a preprocessing of different-steps is done to remove the baseline wander, power line interference and to enhance morphological properties.Secondly Daubechies are chosen and implemented as mother-wavelet-function to extract ten features of ECG signals, in the final stage Support-Vector-Machines (SVM), has been used as Multi-class classifier and decision making algorithm. The performance of the proposed method has been evaluated in terms of accuracy, and specific accuracy. The experimental results have shown that the proposed system achieves validity as competitive results quality-wise, and the accuracy-rate of classification of Normal sinus Rhythm (N), Bundle Branch Block (RBBB), Atrial Premature Beat (APB), 3Premature Ventricular Contraction (PVC), Fusion Heart Beats (F), and Unclassified Heart Beats (P) were 90.0%, 100%, 66.6%, 100%, 100%, and 100%, respectively. en_US
dc.description.sponsorship Sudan University of Science and Technology en_US
dc.language.iso en en_US
dc.publisher Sudan University of Science and Technology en_US
dc.subject biomedical engineering en_US
dc.subject Heart disease en_US
dc.subject Mechanisms supporting Vector en_US
dc.title Cardiac arrhythmias classification using Support Vector Machines en_US
dc.title.alternative تصنيف امراض القلب باستخدام اليات المتجهات الداعمة en_US
dc.type Thesis en_US


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