@article{(Open Science Index):https://publications.waset.org/pdf/13058,
	  title     = {Arterial Stiffness Detection Depending on Neural Network Classification of the Multi- Input Parameters},
	  author    = {Firas Salih and  Luban Hameed and  Afaf Kamil and  Armin Bolz},
	  country	= {},
	  institution	= {},
	  abstract     = {Diagnostic and detection of the arterial stiffness is
very important; which gives indication of the associated increased risk of cardiovascular diseases. To make a cheap and easy method for general screening technique to avoid the future cardiovascular
complexes , due to the rising of the arterial stiffness ; a proposed algorithm depending on photoplethysmogram to be used. The
photoplethysmograph signals would be processed in MATLAB. The
signal will be filtered, baseline wandering removed, peaks and
valleys detected and normalization of the signals should be achieved
.The area under the catacrotic phase of the photoplethysmogram
pulse curve is calculated using trapezoidal algorithm ; then will used
in cooperation with other parameters such as age, height, blood
pressure in neural network for arterial stiffness detection. The Neural
network were implemented with sensitivity of 80%, accuracy 85%
and specificity of 90% were got from the patients data. It is
concluded that neural network can detect the arterial STIFFNESS
depending on risk factor parameters.},
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {6},
	  number    = {10},
	  year      = {2012},
	  pages     = {1221 - 1224},
	  ee        = {https://publications.waset.org/pdf/13058},
	  url   	= {https://publications.waset.org/vol/70},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 70, 2012},
	}