Fractional-order dynamic errors for analyzing residual arteriovenous access stenosis

Wei Ling Chen, Yi Chen Mai, Chia Hung Lin, Chung Dann Kan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In clinical examinations, acoustic methods are non-invasive and inexpensive diagnostic tools for the assessment of aortic and vascular stenosis or occlusion. To ensure the early detection of a dysfunctional arteriovenous access (AVA), we developed a monitoring interface by using phonoangiography (PCG) techniques to examine stenosis among hemodialysis patients. AVAs are vital lines for hemodialysis patients. Clinically, when the AVA lumen is reduced to less than 50% of normal lumen, percutaneous transluminal angioplasty or surgical intervention must be performed to restore normal AVA function. A method based on the Burg AR model combined with the fractional-order dynamic error (FODE) was proposed for evaluating the relationship between the power spectral density and vascular access DOS. However, in a clinical trial of 42 patients, the coefficients of determination was 0.3842 at the V-site anastomosis. For this project, the biophysical model describes an appropriate AVG model for describing the pumping action of the heart, and presents the experimental results. In addition, an arteriovenous graft biophysical model was created for comparing the spectral differences among the different stenotic ratio situations and elucidating the effects of other interference factors.

Original languageEnglish
Title of host publicationProceedings of 2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016
PublisherIEEE Computer Society
Pages280-284
Number of pages5
ISBN (Electronic)9781509003891
DOIs
Publication statusPublished - 2016 Jul 2
Event2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016 - Jeju Island, Korea, Republic of
Duration: 2016 Jul 102016 Jul 13

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume1
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Other

Other2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016
CountryKorea, Republic of
CityJeju Island
Period16-07-1016-07-13

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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  • Cite this

    Chen, W. L., Mai, Y. C., Lin, C. H., & Kan, C. D. (2016). Fractional-order dynamic errors for analyzing residual arteriovenous access stenosis. In Proceedings of 2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016 (pp. 280-284). [7860914] (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2016.7860914