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.
Highlights
Diabetes has become a common disease in modern society
The blood glucose is too high or too low both will cause a significant impact on health, and the complications of diabetes are very serious, such as liver cirrhosis and neuropathy (Garcia-Compean et al 2009, Mitrović et al 2014) [1] [2]
This study proposes using four-channel 820 nm, 875 nm, 945 nm and 1050 nm infrared light as the transmission wavelengths
Summary
Diabetes has become a common disease in modern society. The blood glucose is too high or too low both will cause a significant impact on health, and the complications of diabetes are very serious, such as liver cirrhosis and neuropathy (Garcia-Compean et al 2009, Mitrović et al 2014) [1] [2]. The current method to measure blood glucose is mainly direct drawing blood from patients, which is a electrochemical way. Using invasive way to measure blood glucose for a long-term, the patients suffer from great physical pains, and the risk of infection increases. All kinds of testing strips of invasive blood glucose are expensive disposable consumables. These factors are not conducive to patients to facilitate and timely understand their blood glucose condition (Ramachandran et al 2012) [3]. (2015) Noninvasive Blood Glucose Measurement Based on NIR Spectrums and Double ANN Analysis. The researches of using near infrared spectroscopy for noninvasive blood glucose measurement are increasing recently (Unnikrishna Menon et al 2013, Ramasahayam et al 2013) [3] [6]. After extracting the eigenvalues of each PPG signal with the wavelet transform algorithm, the estimation model is developed with double artificial neural network
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