Subcutaneous Glucose Biosensor Failure – A Fuzzy Fault Tree Analysis Approach

Subcutaneous Glucose Biosensor Failure – A Fuzzy Fault Tree Analysis Approach

C.G. Siontorou F.A. Batzias

Department of Industrial Management & Technology, University of Piraeus, Greece

Page: 
149-164
|
DOI: 
https://doi.org/10.2495/DNE-V9-N2-149-164
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

Diabetes mellitus is a disease of major global importance, increasing in frequency at almost epidemic rates. In order to optimize diabetes management and, therefore, reduce hyper- or hypo-glycaemia complications, continuous and reliable in vivo (subcutaneous or intravenous) glucose monitoring is indispensable. To that end, subcutaneous sensors have progressed more rapidly as they can be linked to state-of-the-art signal transmission modes (transdermal, IR, etc.). Yet, most of the devices currently at clinical trials have not demonstrated a stable and clinically useful sensor performance. In this work, the causes of subcutaneous glucose biosensor sensitivity drift have been investigated by means of fault tree analysis relying on fuzzy reasoning to account for uncertainty. Using the methodology proposed herein, all ultimate causes or combination of causes attributed to the device components, the surrounding tissue and their intra/inter-relations that are responsible for or contribute to the top event have been recognized and quantified based on (a) measurements for the deterministic contributors and (b) experience for the stochastic contributors. The tree structure has been designed by combining deduction and induction, top-down and bottom-up techniques, thus establishing a dialectic tradeoff which brings this method closer to scientific logic, permitting the introduction of deeper knowledge into the surface or experiential knowledge level characterizing FTA. The proposed methodology has been implemented in the investigation of the causes responsible for sensor fouling by thrombus formation and has proven to be an efficient tool for internal diagnostics and fault compensation. The suggested approach may contribute significantly to the self-optimization of the measuring equipment from one generation to the next as it supports the flexible, ad hoc, and tailor-made development, thus potentiating the progress of epidemics from statistics to individualization.

Keywords: 

biosensors, fault tree analysis, fuzzy logic, knowledge processing, knowledge representation, sensitivity decrease, subcutaneous monitoring

  References

[1] Vashist, S.K., Zheng, D., Al-Rubeaan, K., Luong, J.H.T. & Sheu, F-S., Technology behind commercial devices for blood glucose monitoring in diabetes management: a review. Analytica Chimica Acta, 703, pp. 124–136, 2011. doi: http://dx.doi.org/10.1016/j.aca.2011.07.024 

[2] Shaw, J.E., Sicree, R.A. & Zimmet, P.Z., Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Research and Clinical Practice, 87, pp. 4–14. 2010.doi: http://dx.doi. org/10.1016/j.diabres.2009.10.007

[3] Yoo, E-H. & Lee, S-Y., Glucose biosensors: an overview of use in clinical practice. Sensors 10, pp. 4558–4576, 2010. doi: http://dx.doi.org/10.3390/s100504558

[4] D′Orazio, P., Biosensors in clinical chemistry – 2011 update. Clinica Chimica Acta, 412, pp. 1749–1761, 2011. doi: http://dx.doi.org/10.1016/j.cca.2011.06.025

[5] Gerritsen, M., Jansen, J.A. & Lutterman, J.A., Performance of subcutaneously implanted  glucose sensors for continuous monitoring. The Netherlands Journal of Medicine, 54, pp. 167–179, 1999. doi: http://dx.doi.org/10.1016/S0300-2977(99)00006-6

[6] Klueh, U., Dorsky, D.I. & Kreutzer, D.L., Enhancement of implantable glucose sensor function in vivo using gene transfer-induced neovascularization. Biomaterials, 26, pp. 1155–1163, 2005. doi: http://dx.doi.org/10.1016/j.biomaterials.2004.04.017

[7] Cobelli, C. & Ruggeri, A., Evaluation of portal/peripheral route and of algorithms for insulin delivery in the closed-loop control of glucose in diabetes – a modeling study. IEEE Transactions on Biomedical Engineering, 30, pp. 93–103, 1983. doi: http://dx.doi.org/10.1109/ TBME.1983.325203

[8] Klonoff, D.C., The benefi ts of implanted glucose sensors. Journal of Diabetes Science and Technology, 1(6), pp. 797–800, 2007. doi: http://dx.doi.org/10.1177/193229680700100601

[9] Renard, E., Place, J., Cantwell, M., Chevassus, H. & Palerm, C.C., Closed-loop insulin delivery using a subcutaneous glucose sensor and intraperitoneal insulin delivery. Diabetes Care, 33(1), pp. 121–127, 2010. doi: http://dx.doi.org/10.2337/dc09-1080

[10] Wickramasinghe, Y., Yang, Y. & Spencer, S.A., Current problems and potential techniques in in vivo glucose monitoring. Journal of Fluorescence, 14, pp. 513–520, 2004. doi: http://dx.doi. org/10.1023/B:JOFL.0000039339.36839.19

[11] Nablo, B.J., Prichard, H.L., Butler, R.D., Klitzman, B. & Schoenfi sch, M.H., Inhibition of implant-associated infections via nitric oxide release. Biomaterials, 26, pp. 6984–6990, 2005. doi: http://dx.doi.org/10.1016/j.biomaterials.2005.05.017

[12] Sieminski, A.L. & Gooch, K.J., Biomaterial-microvascalature interactions. Biomaterials, 21, pp. 2233–2241, 2000. doi: http://dx.doi.org/10.1016/S0142-9612(00)00149-6

[13] Faucheux, N., Tzovena, R., Nagel, M.-D. & Groth, T., The dependence of fi brillar adhesions in human fi broblasts on substratum chemistry. Biomaterials, 27, pp. 234–245, 2006. doi: http:// dx.doi.org/10.1016/j.biomaterials.2005.05.076

[14] Wisniewski, N. & Reichert, M., Methods for reducing biosensor membrane biofouling.  Colloids and Surfaces B: Biointerfaces, 18, pp. 197–219, 2000. doi: http://dx.doi.org/10.1016/ S0927-7765(99)00148-4

[15] Kasemo, B., Biological surface science. Current Opinion in Solid State and Materials Science, 3, pp. 451–459, 1998. doi: http://dx.doi.org/10.1016/S1359-0286(98)80006-5

[16] Batzias, F.A. & Siontorou, C.G., Investigating the causes of biosensor SNR decrease by means of fault tree analysis. IEEE Transactions on Instrumentation and Measurement, 54, pp. 1395–1406, 2005. doi: http://dx.doi.org/10.1109/TIM.2005.851056

[17] Abrahamsson, P. & Winsö, O., An assessment of calibration and performance of the microdialysis system. Journal of Pharmaceutical and Biomedical Analysis, 39, pp. 730–734, 2005. doi: http://dx.doi.org/10.1016/j.jpba.2005.04.036

[18] Jeong, R.-A., Hwang, J.Y., Joo, S., Chung, T.D., Park, S., Kang, S.K., Lee, W.-Y. & Kim, H.C., In vivo calibration of the subcutaneous amperometric glucose sensors using a nonenzyme electrode. Biosensors and Bioelectronics, 19, pp. 131–139, 2003. doi: http://dx.doi. org/10.1016/S0956-5663(03)00219-7

[19] Banerjee, R., Nag, S. & Fraser, H.L., A novel combinatorial approach to the development of beta titanium alloys for orthopaedic implants. Materials Science and Engineering C, 25 pp. 282–289, 2005. doi: http://dx.doi.org/10.1016/j.msec.2004.12.010

[20] Bellazzi, R., Guglielmann, R. & Ironi, L., Learning from biomedical time series through the integration of qualitative models and fuzzy systems. Artifi cial Intelligence in Medicine, 21, pp. 215–220, 2001. doi: http://dx.doi.org/10.1016/S0933-3657(00)00088-9

[21] Siontorou, C.G., Batzias, F.A. & Tsakiri, V., A knowledge-based approach to online fault  diagnosis of FET biosensors. IEEE Transactions on Instrumentation and Measurement, 59, pp. 2345–2364, 2010. doi: http://dx.doi.org/10.1109/TIM.2009.2036464

[22] Siontorou, C.G. & Batzias, F.A., Error identifi cation/propagation/remediation in biomonitoring surveys – a knowledge-based approach towards standardization via fault tree analysis. Ecological Indicators, 11, pp. 564–581, 2011. doi: http://dx.doi.org/10.1016/j.ecolind.2010.07.013

[23] Siontorou, C.G. & Batzias, F.A., Carbohydrate detection failure analysis via biosensoring. IEEE Transactions on Instrumentation and Measurement, 57, pp. 2856–2867, 2011. doi: http://dx.doi.org/10.1109/TIM.2008.926051

[24] Thévenot, D.R., Toth, K., Durst, R.A. & Wilson, G.S., Electrochemical biosensors: recommended defi nition and classifi cation. Pure and Applied Chemistry, 71, pp. 2333–2348, 

1999. doi: http://dx.doi.org/10.1351/pac199971122333

[25] Usher, M.J., Sensors and Transducers. Macmillan: London, UK, 1985.

[26] Wisniewski, N., Rajamand, N., Adamsson, U., Lins, P.E., Reichert, W.M., Klitzman, B. &  Ungerstedt, U., Analyte fl ux through chronically implanted subcutaneous polyamide membranes differs in humans and rats. American Journal of Physiology, Endocrinology and  Metabolism, 282, pp. E1316–E1323, 2002.

[27] Berglin, M., Andersson, M., Sellborn, A. & Elwing, H., The effect of substrate molecular  mobility on surface induced immune complement activation and blood plasma coagulation. Biomaterials, 25, pp. 4581–4590, 2004. doi: http://dx.doi.org/10.1016/j.biomaterials.2003.11.050

[28] Theoret, C.L., Update on would repair. Clinical Techniques in Equine Practice, 3(2), pp. 110–122, 2004. doi: http://dx.doi.org/10.1053/j.ctep.2004.08.009

[29] Weisenberg, B.A. & Mooradian, D.L., Hemocompatibility of materials used in microelectromechanical systems: platelet adhesion and morphology in vitro. Journal of Biomedical Materials Research, 60(2), pp. 283–291, 2002. doi: http://dx.doi.org/10.1002/jbm.10076

[30] Choi, J., Hammer, L.W. & Hester, R.L., Calcium-dependent synthesis of prostacyclin in ATPstimulated venous endothelial cells. Hypertension, 39, pp. 581–585, 2002. doi: http://dx.doi. org/10.1161/hy0202.103289

[31] Deng, T., Yu, L., Ge, Y., Zhang, L. & Zheng, X., Intracellular-free calcium dynamics and  F-actin alteration in the formation of macrophage foam cells. Biochemical and Biophysical Research Communications, 338, pp. 748–756, 2005. doi: http://dx.doi.org/10.1016/j.bbrc.2005.10.010

[32] Beetens, J.R., Coene, M.C., Verheyen, A., Zonnekeyn, L. & Herman, A.G., Vitamin C increases the prostacyclin production and decreases the vascular lesions in experimental atherosclerosis in rabbits. Prostagladins, 32(3), pp. 335–352, 1986.

[33] Brattsand, R., Actions of vitamins A and E and some nicotinic acid derivatives on plasma lipids and on lipid infi ltration of aorta in cholesterol-fed rabbits. Atherosclerosis, 22, pp. 47–61, 1975. doi: http://dx.doi.org/10.1016/0021-9150(75)90067-2

[34] Knobler, H., Savion, N., Shenkman, B., Kotev-Emeth, S. & Varon, D., Shear-induced platelet adhesion and aggregation on subendothelium are increased in diabetic patients. Thrombosis Research, 90, pp. 181–190, 1998. doi: http://dx.doi.org/10.1016/S0049-3848(98)00050-4

[35] Zimering, M.B. & Thakker-Varia, S., Increased fi broblast growth factor-like autoantibodies in serum from a subset of patients with cancer-associated hypercalcemia. Life Sciences, 71 pp. 2939–2959, 2002. doi: http://dx.doi.org/10.1016/S0024-3205(02)02160-4