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      Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor

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          Abstract

          This paper proposes a noninvasive dual optical photoplethysmography (PPG) sensor to classify the degree of arteriovenous fistula (AVF) stenosis in hemodialysis (HD) patients. Dual PPG measurement node (DPMN) becomes the primary tool in this work for detecting abnormal narrowing vessel simultaneously in multi-beds monitoring patients. The mean and variance of Rising Slope (RS) and Falling Slope (FS) values between before and after HD treatment was used as the major features to classify AVF stenosis. Multilayer perceptron neural networks (MLPN) training algorithms are implemented for this analysis, which are the Levenberg-Marquardt, Scaled Conjugate Gradient, and Resilient Back-propagation, to identify the degree of HD patient stenosis. Eleven patients were recruited with mean age of 77 ± 10.8 years for analysis. The experimental results indicated that the variance of RS in the HD hand between before and after treatment was significant difference statistically to stenosis ( p < 0.05). Levenberg-Marquardt algorithm (LMA) was significantly outperforms the other training algorithm. The classification accuracy and precision reached 94.82% and 92.22% respectively, thus this technique has a potential contribution to the early identification of stenosis for a medical diagnostic support system.

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          Most cited references 34

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          A scaled conjugate gradient algorithm for fast supervised learning

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            A direct adaptive method for faster backpropagation learning: the RPROP algorithm

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              Photoplethysmographic assessment of pulse wave reflection: blunted response to endothelium-dependent beta2-adrenergic vasodilation in type II diabetes mellitus.

              We sought to determine whether a simple index of pressure wave reflection may be derived from the digital volume pulse (DVP) and used to examine endothelium-dependent vasodilation in patients with type II diabetes mellitus. The DVP exhibits a characteristic notch or inflection point that can be expressed as percent maximal DVP amplitude (IP(DVP)). Nitrates lower IP(DVP), possibly by reducing pressure wave reflection. Response of IP(DVP) to endothelium-dependent vasodilators may provide a measure of endothelial function. The DVP was recorded by photoplethysmography. Albuterol (salbutamol) and glyceryl trinitrate (GTN) were administered locally by brachial artery infusion or systemically. Aortic pulse wave transit time from the root of the subclavian artery to aortic bifurcation (T(Ao)) was measured by simultaneous Doppler velocimetry. Brachial artery infusion of drugs producing a greater than threefold increase in forearm blood flow within the infused limb was without effect on IP(DVP), whereas systemic administration of albuterol and GTN produced dose-dependent reductions in IP(DVP). The time between the first and second peak of the DVP correlated with T(Ao) (r = 0.75, n = 20, p < 0.0001). The effects of albuterol but not GTN on IP(DVP) were attenuated by N(G)-monomethyl-L-arginine. The IP(DVP) response to albuterol (400 microg by inhalation) was blunted in patients with type II diabetes mellitus as compared with control subjects (fall 5.9 +/- 1.8% vs. 11.8 +/- 1.8%, n = 20, p < 0.02), but that to GTN (500 microg sublingually) was preserved (fall 18.3 +/- 1.2% vs. 18.6 +/- 1.9%, p = 0.88). The IP(DVP) is influenced by pressure wave reflection. The effects of albuterol on IP(DVP) are mediated in part through the nitric oxide pathway and are impaired in patients with type II diabetes.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                17 July 2018
                July 2018
                : 18
                : 7
                Affiliations
                Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan; stevylib@ 123456yahoo.com
                Author notes
                [* ]Correspondence: terrydu@ 123456stust.edu.tw ; Tel.: +886-253-3131 (ext. 3321)
                Article
                sensors-18-02322
                10.3390/s18072322
                6068649
                30018275
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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