Abstract
A multisensors information fusion model (MIFM) based on the Mixture of Experts (ME) neural networks was designed to fuse the multi-sensors signals for infrared noninvasive blood glucose detection. ME algorithm greatly improved the precision of noninvasive blood glucose measurement with multisensors. The principle of ME, design and implementation of MIFM were described in details. The standard deviation of the error of predication (SO) was 0.88 mmol/l from blood and 0.65 mmol/l from water-glucose. The correlation coefficient (CC) to training data from blood analysis was 0. 9.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Arnold, M.A., Burmeister, J.J., Small, G.W.: Phantom Glucose Calibration Models from Simulated Noninvasive Human Near-infrared Spectra. Anal. Chem. 70, 1773–1781 (1998)
Klonoff, D.C.: Noninvasive Blood Glucose Monitoring. Diabetes Care 20, 433–437 (1997)
Zheng, P., Kramer, C.E., Barnes, C.W., Braig, J.R., Sterling, B.B.: Noninvasive Glucose Determination by Oscillating Thermal Gradient Spectrometry. Diabetes Technol. Ther. 2, 17–25 (2000)
Christison, G.B., MacKenzie, H.A.: Laser Photoacoustic Determination of Physiological Glucose Concentrations in Human Whole Blood. Med. Biol. Eng. Comput. 31, 284–290 (1993)
Robinovitch, N.: Noninvasive Glucose Determination by Reverse Iontophresis Fluid Extraction from Skin. Diabetic Care 5, 259–265 (1982)
Tenhunen, J., Kopola, H., Myllylä, R.: Non-invasive Glucose Measurement Based on Selective Near Infrared Absorption; Requirements on Instrumentation and Spectral Range. Measurement 24, 173–177 (1998)
Pan, S., Chung, H., Arnold, M., Small, G.W.: Near-infrared Spectroscopic Measurement of Physiological Glucose Levels in Variable Matrices of Protein and Triglycerides. Anal. Chem. 68, 112–1135 (1996)
Muller, U.A., Mertes, B., Fischbacher, C., Jageman, K.U., Danzer, K.: Noninvasive Blood Glucose Monitoring by Means of Near Infrared Methods for Improving the Reliability of the Calibration Models. Int. J. Artif. Organs 20, 285–290 (1997)
Gabriely, I., Wozniak, R., Mevorach, M., Kaplan, J., Aharon, Y., Shamoon, H.: Transcutaneous Glucose Measurement Using Near-Infrared Spectroscopy During Hypoglycemia. Diabets Care 22, 2026–2032 (1999)
Jacobs, R.A., Jordan, M.: Adaptive Mixtures of Local-Experts. Neural Computation 3, 79–87 (1991)
Kurnik, R.T., Oliver, J.J., Waterhouse, S.R., Dunn, T.: Application of the Mixtures of Experts Algorithm for Signal Processing in A Noninvasive Glucose Monitoring System. Sensors and Actuators b60, 19–26 (1999)
Malin, S.F., Ruchti, T.L., Blank, T.B., Thennadil, S.N., Monfre, S.L.: Noninvasive Prediction of Glucose by Near-infrared Diffuse Reflectance Spectroscopy. Clin Chem. 45, 1651–1658 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, W., Yan, L., Liu, B., Zhang, H. (2005). Multisensors Information Fusion with Neural Networks for Noninvasive Blood Glucose Detection. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_121
Download citation
DOI: https://doi.org/10.1007/11427469_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
eBook Packages: Computer ScienceComputer Science (R0)