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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.