Noninvasive Blood Glucose Measurement Based on NIR Spectrums and Double ANN Analysis

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Noninvasive Blood Glucose Measurement Based on NIR Spectrums and Double ANN Analysis [iCBBE 2015]
DOI: 10.4236/jbm.2015.36007 PP.42 - 48
Author(s)
D. X. Guo, Y. Z. Shang, R. Peng, S. S. Yong, X. A. Wang
ABSTRACT
This paper presents a new noninvasive blood glucose monitoring method based on four near infrared spectrums and double artificial neural network analysis. We choose four near infrared wavelengths, 820 nm, 875 nm, 945 nm, 1050 nm, as transmission spectrums, and capture four fingers transmission PPG signals simultaneously. The wavelet transform algorithm is used to remove baseline drift, smooth signals and extract eight eigenvalues of each PPG signal. The eigenvalues are the input parameters of double artificial neural network analysis model. Double artificial neural network regression combines the classification recognition algorithm with prediction algorithm to improve the accuracy of measurement. Experiments show that the root mean square error of the prediction is between 0.97 mg/dL - 6.69 mg/dL, the average of root mean square error is 3.80 mg/dL.
KEYWORDS
Noninvasive, Blood Glucose, NIR, ANN
References
[1]
Garcia-Compean, D., Jaquez-Quintana, J.O., Gonzalez-Gonzalez, J.A. and Maldonado-Garza, H. (2009) Liver Cirrhosis and Diabetes: Risk Factors, Pathophysiology, Clinical Implications and Management. World Journal of Gastroenterology, 15, 280-288.
http://dx.doi.org/10.3748/wjg.15.280
[2]
Mitrovic, M., Popovic, D.S., Naglic, D.T., Paro, J.N., Ilic, T. and Zavisic, B.K. (2014) Markers of Inflammation and Microvascular Complications in Type 1 Diabetes. Central European Journal of Medicine, 9, 748-753.
http://dx.doi.org/10.2478/s11536-013-0335-6
[3]
Ramachandran, A., Snehalatha, C., Shetty, A.S. and Nanditha, A. (2012) Trends in Prevalence of Diabetes in Asian Countries. World journal of Diabetes, 3, 110.
http://dx.doi.org/10.4239/wjd.v3.i6.110
[4]
Ferrante Do Amaral, C.E. and Wolf, B. (2008) Current Development in Non-Invasive Glucose Monitoring. Medical Engineering & Physics, 30, 541-549.
http://dx.doi.org/10.1016/j.medengphy.2007.06.003
[5]
Vashist, S.K. (2012) Non-Invasive Glucose Monitoring Technology in Diabetes Management: A Review. Analytica Chimica Acta, 750, 16-27.
http://dx.doi.org/10.1016/j.aca.2012.03.043
[6]
Unnikrishna Menon, K.A., Hemachandran, D. and Abhishek, T.K. (2013) A Survey on Non-Invasive Blood Glucose Monitoring Using NIR. 2013 International Conference on Communications and Signal Processing (ICCSP), 1069- 1072.
http://dx.doi.org/10.1109/iccsp.2013.6577220
[7]
Lam, S.C.H., Chung, J.W.Y., Fan, K.L. and Wong, T.K.S. (2010) Non-Invasive Blood Glucose Measurement by Near Infrared Spectroscopy: Machine Drift, Time Drift and Physiological Effect. Spectroscopy, 24.
http://dx.doi.org/10.1155/2010/929506
[8]
Maruo, K., Tsurugi, M., Chin, J., Ota, T., Arimoto, H., Yamada, Y., Ta-mura, M., Ishii, M. and Ozaki, Y. (2003) Noninvasive Blood Glucose Assay Using a Newly Developed Near-Infrared System. IEEE Journal of Selected Topics in Quantum Electronics, 9, 322-330.
http://dx.doi.org/10.1109/JSTQE.2003.811283
[9]
Malin, S.F., Ruchti, T.L., Blank, T.B., Thennadil, S.N. and Monfre, S.L. (1999) Noninvasive Prediction of Glucose by Near-Infrared Diffuse Reflectance Spectroscopy. Clinical Chemistry, 45, 1651-1658.
[10]
Elgendi, M. (2012) On the Analysis of Fingertip Photoplethysmogram Signals. Current Cardiology Reviews, 8, 14.
http://dx.doi.org/10.2174/157340312801215782
[11]
Ubeyli, E.D., Cvetkovic, D. and Cosic, I. (2007) Eigenvector Methods for Analysis of Human PPG, ECG and EEG Signals. 29th Annual International Conference of the Engineering in Medicine and Biology Society, 3304-3307.
http://dx.doi.org/10.1109/iembs.2007.4353036
[12]
Du, Y.P., Liang, Y.Z., Kasemsumran, S., Maruo, K. and Ozaki, Y. (2004) Removal of Interference Signals Due to Water from in Vivo Near-Infrared (NIR) Spectra of Blood Glucose by Region Orthogonal Signal Correction (ROSC). Analytical Sciences, 20, 1339-1345.
http://dx.doi.org/10.2116/analsci.20.1339
[13]
Ramasahayam, S., Haindavi, K.S., Kavala, B. and Chowdhury, S.R. (2013) Non Invasive Estimation of Blood Glucose Using Near Infra Red Spectroscopy and Double Regression Analysis. 2013 Seventh International Conference on Sensing Technology (ICST), 627-631.
http://dx.doi.org/10.1109/ICSensT.2013.6727729
[14]
Yadav, J., Rani, A., Singh, V. and Murari, B.M. (2014) Near-Infrared LED Based Non-Invasive Blood Glucose Sensor. 2014 International Conference on Signal Processing and Integrated Networks (SPIN), 591-594.
http://dx.doi.org/10.1109/SPIN.2014.6777023
[15]
Barman, I., Kong, C.-R., Dingari, N.C., Dasari, R.R. and Feld, M.S. (2010) Development of Robust Calibration Models Using Support Vector Machines for Spectroscopic Monitoring of Blood Glucose. Analytical Chemistry, 82, 9719- 9726.
http://dx.doi.org/10.1021/ac101754n
[16]
Ping, Z., Yingchun, L., Jie, M. and Siliang, M. (2005) Analysis of Noninvasive Measurement of Human Blood Glucose with ANN-NIR Spectroscopy. International Conference on Neural Networks and Brain, 1350-1353.
http://dx.doi.org/10.1109/icnnb.2005.1614881
[17]
Kovatchev, B.P., Gonder-Frederick, L.A., Cox, D.J. and Clarke, W.L. (2004) Evaluating the Accuracy of Continuous Glucose-Monitoring Sensors Continuous Glucose-Error Grid Analysis Illustrated by Therasense Freestyle Navigator Data. Diabetes Care, 27, 1922-1928.
http://dx.doi.org/10.2337/diacare.27.8.1922

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