Firas Salih and Luban Hameed and Afaf Kamil and Armin Bolz
Arterial Stiffness Detection Depending on Neural Network Classification of the Multi Input Parameters
1221 - 1224
2012
6
10
International Journal of Electrical and Computer Engineering
https://publications.waset.org/pdf/13058
https://publications.waset.org/vol/70
World Academy of Science, Engineering and Technology
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.
Open Science Index 70, 2012