DocumentCode :
2930664
Title :
Assessment of human instantaneous arterial diameter using B-mode ultrasound imaging and artificial neural networks: Determination of wall mechanical properties
Author :
Pessana, F. ; Venialgo, E. ; Rubstein, J. ; Furfaro, A.
Author_Institution :
Electron. Dept., Nat. Technol. Univ., Buenos Aires, Argentina
fYear :
2010
fDate :
Aug. 31 2010-Sept. 4 2010
Firstpage :
1409
Lastpage :
1412
Abstract :
Wall artery viscoelastic properties (WAVP) are correlated with structural and functional state of the arterial system. An accurate estimation of these properties is achieved measuring wall instantaneous diameter and pressure signals. The aim of this work was to evaluate a new non invasive estimation method of the instantaneous arterial diameter (D), and consequently, WAVP. Ten common carotid arteries of hypertensive men were evaluated. D was calculated by using B-mode ultrasonic imaging and specialized software designed with Artificial Neural Networks. Instantaneous arterial pressure of all subjects was measured by piezoelectric tonometry. Arterial wall properties were evaluated using a linear autoregressive with exogenous input model. The new method, which determinates the arterial diameter, was compared respect to a specialized and previously validated method. Results showed no significant differences in all parameters derived of D (Bland & Altman test) and no differences in all the wall arterial mechanic indexes (p>0.05). For these reasons, the developed software based on Artificial Neural Networks was successful in determining the parameters associated with arterial diameters and it opens up the possibility of real time calculations of arterial wall mechanical properties because of its simplicity.
Keywords :
biomechanics; biomedical ultrasonics; blood vessels; medical computing; neural nets; viscoelasticity; B-mode ultrasound imaging; artificial neural networks; exogenous input model; human instantaneous arterial diameter; linear autoregressive model; piezoelectric tonometry; wall arterial mechanic index; wall artery viscoelastic properties; wall pressure signal; Arteries; Artificial neural networks; Image edge detection; Mechanical factors; Pressure measurement; Ultrasonic imaging; Ultrasonic variables measurement; Algorithms; Carotid Arteries; Computer Systems; Elastic Modulus; Elasticity Imaging Techniques; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Male; Neural Networks (Computer); Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Shear Strength; Viscosity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Conference_Location :
Buenos Aires
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4123-5
Type :
conf
DOI :
10.1109/IEMBS.2010.5626719
Filename :
5626719
Link To Document :
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