Determination of Blood Glucose Concentration by Using Wavelet Transform and Neural Networks

Abstract

Background: Early and non-invasive determination of blood glucose level is of great importance. We aimed to present a new technique to accurately infer the blood glucose concentration in peripheral blood flow using non-invasive optical monitoring system.Methods: The data for the research were obtained from 900 individuals. Of them, 750 people had diabetes mellitus (DM). The system was designed using a helium neon laser source of 632.8 nm wavelength with 5mW power, photo detectors and digital storage oscilloscope. The laser beam was directed through a single optical fiber to the index finger and the scattered beams were collected by the photo detectors placed circumferentially to the transmitting fiber. The received signals were filtered using band pass filter and finally sent to a digital storage oscilloscope. These signals were then decomposed into approximation and detail coefficients using modified Haar Wavelet Transform. Back propagation neural and radial basis functions were employed for the prediction of blood glucose concentration.Results: The data of 450 patients were randomly used for training, 225 for testing and the rest for validation. The data showed that outputs from radial basis function were nearer to the clinical value. Significant variations could be seen from signals obtained from patients with DM and those without DM.Conclusion: The proposed non-invasive optical glucose monitoring system is able to predict the glucose concentration by proving that there is a definite variation in hematological distribution between patients with DM and those without DM.

Keywords

• Diabetes mellitus
• Noninvasive
• Neural networks