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

has been cited by the following article:

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

  AUTHORS: D. X. Guo, Y. Z. Shang, R. Peng, S. S. Yong, X. A. Wang

  KEYWORDS: Noninvasive, Blood Glucose, NIR, ANN

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.3 No.6, June 13, 2015

  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.