City Research Online

Abstract

Current carotid atherosclerosis diagnostic protocols do not feature techniques that would allow for early or frequent medical examinations, leaving a significant number of asymptomatic carotid stenosis cases undetected and often leading to strokes. The key challenge is that current diagnostics are highly operator-dependent. In this work we used idealised biological models to demonstrate a new rapid, potentially inexpensive and operator-independent diagnostic method, aimed at detecting whether a stenosis exists, rather than seeking to be accurately quantifying or localising it. An array of electrodes was used to obtain sequential bioimpedance values over the skin, through a novel scanning technique, covering an area over the artery of interest. FEM simulations, verified through in-vitro experiments on gelatine phantoms, were used to validate the method. The final results, obtained through image processing algorithms, were in the form of planar bio-impedance maps and were successful both in identifying arterial features and detecting the presence of stenoses of different sizes. The results could also be used to indicate the artery’s relative orientation to the sensor, eliminating the need for manual alignment by a specialist operator. Therefore, this method shows promise for routine medical examination, either in primary care, or even at home, to indicate whether a patient would require further, more detailed examinations at a specialist clinic.

Publication Type:	Article
Additional Information:	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information.
Publisher Keywords:	Bioimpedance sensing, Cardiovascular Diagnostics, Carotid artery screening, FEM, modelling, Image processing, Impedance scanning, Planar bioimpedance mapping, Stenosis detection
Subjects:	R Medicine > RC Internal medicine T Technology > T Technology (General)
Departments:	School of Science & Technology School of Science & Technology > Engineering
SWORD Depositor:	Symplectic Administrator

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