000			02126cam a2200325 a 4500
003			EG-GiCUC
008			150625s2014 ua dh f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.13.03.M.Sc.2014.Ib.A
100	0		_aIbrahim Hamed Ibrahim
245	1	0	_aAutomatic arrhythmia detection using support vector machine based on discrete wavelet transform / _cIbrahim Hamed Ibrahim ; Supervised Mohamed Emad Mousa Rasmy , Abd Allah Sayed Ahmed , Mohamed Ibrahim Ismail Owis
246	1	5	_aالكشف الأتوماتيكى عن عدم إنتظام ضربات القلب بإستخدام مصنف آلى محدد مبنى على التحويل المتقطع المويجى
260			_aCairo : _bIbrahim Hamed Ibrahim , _c2014
300			_a99 P. : _bcharts , facsimiles ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering
520			_aArrhythmia 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
530			_aIssued also as CD
653		4	_aArrhythmia detection
653		4	_aPrincipal component analysis
653		4	_aWavelet
700	0		_aAbdallah Sayed Ahmed , _eSupervisor-Dead
700	0		_aMohamed Emad Mousa Rasmy , _eSupervisor
700	0		_aMohamed Ibrahim Ismail Owis , _eSupervisor
905			_aEnas _eCataloger
905			_aNazla _eRevisor
942			_2ddc _cTH
999			_c51430 _d51430