Fractional-order dynamic errors for analyzing residual arteriovenous access stenosis

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

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Proceedings of 2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016
發行者IEEE Computer Society
頁面280-284
頁數5
ISBN(電子)9781509003891
DOIs
出版狀態Published - 2016 七月 2
事件2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016 - Jeju Island, Korea, Republic of
持續時間: 2016 七月 102016 七月 13

出版系列

名字Proceedings - International Conference on Machine Learning and Cybernetics
1
ISSN(列印)2160-133X
ISSN(電子)2160-1348

Other

Other2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016
國家Korea, Republic of
城市Jeju Island
期間16-07-1016-07-13

指紋

DOS
Power spectral density
Grafts
Acoustics
Monitoring

All Science Journal Classification (ASJC) codes

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

引用此文

Chen, W. L., Mai, Y. C., Lin, C. H., & Kan, C-D. (2016). Fractional-order dynamic errors for analyzing residual arteriovenous access stenosis. 於 Proceedings of 2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016 (頁 280-284). [7860914] (Proceedings - International Conference on Machine Learning and Cybernetics; 卷 1). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2016.7860914
Chen, Wei Ling ; Mai, Yi Chen ; Lin, Chia Hung ; Kan, Chung-Dann. / Fractional-order dynamic errors for analyzing residual arteriovenous access stenosis. Proceedings of 2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016. IEEE Computer Society, 2016. 頁 280-284 (Proceedings - International Conference on Machine Learning and Cybernetics).
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Chen, WL, Mai, YC, Lin, CH & Kan, C-D 2016, Fractional-order dynamic errors for analyzing residual arteriovenous access stenosis. 於 Proceedings of 2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016., 7860914, Proceedings - International Conference on Machine Learning and Cybernetics, 卷 1, IEEE Computer Society, 頁 280-284, 2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016, Jeju Island, Korea, Republic of, 16-07-10. https://doi.org/10.1109/ICMLC.2016.7860914

Fractional-order dynamic errors for analyzing residual arteriovenous access stenosis. / Chen, Wei Ling; Mai, Yi Chen; Lin, Chia Hung; Kan, Chung-Dann.

Proceedings of 2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016. IEEE Computer Society, 2016. p. 280-284 7860914 (Proceedings - International Conference on Machine Learning and Cybernetics; 卷 1).

研究成果: Conference contribution

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Chen WL, Mai YC, Lin CH, Kan C-D. Fractional-order dynamic errors for analyzing residual arteriovenous access stenosis. 於 Proceedings of 2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016. IEEE Computer Society. 2016. p. 280-284. 7860914. (Proceedings - International Conference on Machine Learning and Cybernetics). https://doi.org/10.1109/ICMLC.2016.7860914