Non invasive estimation of blood glucose using near infra red spectroscopy and double regression analysis

Abstract

This paper presents a unique technique for noninvasive estimation of blood glucose concentration using near infra red spectroscopy. The spectroscopy has been performed at the second overtone of glucose which falls in the near infra red region. The near infra red spectroscopy has been performed using transmission photoplethsymography (PPG). The analog front end system has been implemented to get the PPG signal at the near infra red wavelengths of 1070nm, 950nm, 935nm. The PPG signal that has been obtained is processed and double regression analysis is carried out with the artificial neural network for estimating the glucose levels. The root mean square error of the prediction was 5.84mg/dL.