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003			EG-GiCUC
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008			191215s2019 ua dh f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aPh.D
099			_aCai01.13.03.Ph.D.2019.Ab.S
100	0		_aAbdelrahman Shaaban Sayed Hassan
245	1	0	_aSignal processing and machine learning for blood pressure classification using only the ECG signal / _cAbdelrahman Shaaban Sayed Hassan ; Supervised Amr Abdelrahman Sharawi
246	1	5	_aمعالجة الإشارة وتعلم الآلة لتصنيف ضغط الدم باستخدام إشارة رسم القلب فقط
260			_aCairo : _bAbdelrahman Shaaban Sayed Hassan , _c2019
300			_a90 P. : _bcharts , facsimiles ; _c30cm
502			_aThesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering
520			_aContinuous reading of vital signs in the Intensive Care Unit is a major role for the physician, which allows him to intervene in a timely manner. Thus, continuous blood pressure measurement remains a difficult task as long as it is based on using a mercury device or other wide varieties of methods. The approach of this research is based on classifying blood pressure records obtained from the analysis of the Electrocardiogram (ECG) solely using signal processing techniques. The analysis starts with Butterworth filtration of the ECG signal. Following that trend removal and normalization of the signal takes place before extracting 27 features. Feature selection methods are applied to reduce the number of features to the most dominant ones, and as a result the number of features was reduced to 10. The final results point to a high accuracy of 98.18% using a support vector machine (SVM) classifier. Other classifiers like artificial neural networks (ANN) and Bayesian naïve (BN) classifiers were also used but gave a less accuracy of 96.5% and 96.08%, respectively
530			_aIssued also as CD
653		4	_aBlood pressure
653		4	_aElectrocardiogram
653		4	_aSVM classifier
700	0		_aAmr Abdelrahman Sharawi , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aAsmaa _eCataloger
905			_aNazla _eRevisor
942			_2ddc _cTH
999			_c75885 _d75885