Nocturnal hypoglycemic alarm based on near-infrared spectroscopy: In vitro simulation studies

Abstract

An alarm algorithm for detecting episodes of nocturnal hypoglycemia is demonstrated in simulation studies that incorporate the use of a tissue phantom. Based on transmission spectra collected in the near-infrared combination region of 4000–5000 cm−1, pattern recognition methods are used to classify spectra into alarm and non-alarm data classes on the basis of whether or not they signify a glucose excursion below a user-defined hypoglycemic alarm threshold. A reference spectrum and corresponding glucose concentration are acquired at the start of the monitoring period, and absorbance values of subsequent spectra are computed relative to this reference. The resulting differential spectra reflect differential glucose concentrations that correspond to the differences in concentration between each spectrum and the reference. Given the alarm threshold, a database of calibration differential spectra are partitioned into those above and below the threshold. Piecewise linear discriminant analysis is then used to compute a classification model that can be applied to differential spectra collected during the monitoring period in order to identify spectra that signal glucose concentrations in the hypoglycemic range. This alarm algorithm is demonstrated in two multiple-day dynamic studies that incorporate a tissue phantom composed of films of keratin and collagen that approximate the thicknesses of the corresponding proteins found in human tissue.