Automatic arrhythmia detection using support vector machine based on discrete wavelet transform / Ibrahim Hamed Ibrahim ; Supervised Mohamed Emad Mousa Rasmy , Abd Allah Sayed Ahmed , Mohamed Ibrahim Ismail Owis

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Ibrahim Hamed Ibrahim

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TextLanguage: English Publication details: Cairo : Ibrahim Hamed Ibrahim , 2014Description: 99 P. : charts , facsimiles ; 30cmOther title:

الكشف الأتوماتيكى عن عدم إنتظام ضربات القلب بإستخدام مصنف آلى محدد مبنى على التحويل المتقطع المويجى [Added title page title]

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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering Summary: Arrhythmia is abnormality in the way electricity moves through the heart. The symptoms of arrhythmia are not present all the time; several examination hours of ECG records are needed. Even so, there is a high percentage of missing vital information. Automated arrhythmia detection of normal sinus rhythm and three types of arrhythmia (AF, VF, and SVT) was introduced by extracting the main features of the signal through DWT followed by PCA. These features were reduced through statistical analysis to be used as input to SVM that resulted in overall accuracy of 96.89%. The aim is to minimize the risk of missing vital information and to give physicians the confidence of making sound decisions with indistinct symptoms

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Item type	Current library	Home library	Call number	Copy number	Status	Date due	Barcode
Thesis	قاعة الرسائل الجامعية - الدور الاول	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.03.M.Sc.2014.Ib.A (Browse shelf(Opens below))		Not for loan		01010110065910000
CD - Rom	مخـــزن الرســائل الجـــامعية - البدروم	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.03.M.Sc.2014.Ib.A (Browse shelf(Opens below))	65910.CD	Not for loan		01020110065910000

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Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering

Arrhythmia is abnormality in the way electricity moves through the heart. The symptoms of arrhythmia are not present all the time; several examination hours of ECG records are needed. Even so, there is a high percentage of missing vital information. Automated arrhythmia detection of normal sinus rhythm and three types of arrhythmia (AF, VF, and SVT) was introduced by extracting the main features of the signal through DWT followed by PCA. These features were reduced through statistical analysis to be used as input to SVM that resulted in overall accuracy of 96.89%. The aim is to minimize the risk of missing vital information and to give physicians the confidence of making sound decisions with indistinct symptoms

Issued also as CD

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