Method and apparatus for predicting analyte concentration

Abstract

A method for predicting a concentration of an in vivo analyte includes obtaining a plurality of in vivo spectra of the in vivo analyte, determining a learning section for a concentration predicting algorithm for the analyte based on an unchanged section, during which the concentration of the analyte is not substantially changed, and a plurality of the in vivo spectra, and predicting the concentration of the in vivo analyte by using the concentration predicting algorithm based on a learned result of the learning section and an intrinsic spectrum of the in vivo analyte.

What is claimed is: 
     
         1 . A method for predicting a concentration of an in vivo analyte, the method comprising:
 obtaining a plurality of in vivo spectra of the in vivo analyte;   determining a learning section of a concentration predicting algorithm for the in vivo analyte based on an unchanged section, during which a concentration of the in vivo analyte is not substantially changed, and the in vivo spectra; and   predicting the concentration of the in vivo analyte by using the concentration predicting algorithm based on a learned result of the learning section and an intrinsic spectrum of the in vivo analyte.   
     
     
         2 . The method of  claim 1 , wherein the in vivo analyte is at least one of glucose, urea, lactate, triglyceride, protein, cholesterol, and ethanol. 
     
     
         3 . The method of  claim 1 , wherein,
 the in vivo analyte is glucose, and   the unchanged section, during which the concentration of the in vivo analyte is not substantially changed, is a fasting section.   
     
     
         4 . The method of  claim 1 , wherein the in vivo spectrum comprise at least one of an absorption spectrum or a reflection spectrum of an infra-red ray. 
     
     
         5 . The method of  claim 1 , wherein the in vivo spectrum comprise a dispersion spectrum of a single wavelength electromagnetic wave. 
     
     
         6 . The method of  claim 1 , wherein the obtaining the in vivo spectra of the in vivo analyte comprises obtaining the in vivo spectra continually at a predetermined time interval. 
     
     
         7 . The method of  claim 1 , wherein the concentration predicting algorithm comprises a net analyte signal algorithm. 
     
     
         8 . The method of  claim 1 , wherein the determining the learning section comprises:
 calculating a similarity between the in vivo spectra;   determining a section having a high similarity as a similar section; and   determining a section, during which the unchanged section and the similar section overlap each other, as the learning section.   
     
     
         9 . The method of  claim 8 , wherein the calculating the similarity between the in vivo spectra comprises:
 aligning baselines of at least two spectra for calculating similarities thereof among the in vivo spectra; and   calculating a difference between the at least two in vivo spectra, the baselines of which are aligned.   
     
     
         10 . The method of  claim 1 , wherein the predicting the concentration of the in vivo analyte comprises predicting the concentration of the in vivo analyte in the similar section including the learning section when a length of the learning section is longer than a predetermined section length. 
     
     
         11 . The method of  claim 1 , wherein the predicting the concentration of the in vivo analyte comprises re-determining the learning section in the similar section when a length of the learning section is shorter than a predetermined length. 
     
     
         12 . The method of  claim 1 , wherein the predicting the concentration of the in vivo analyte comprises displaying a message to inform a user that a concentration prediction is unavailable when a length of the learning section is shorter than a predetermined length. 
     
     
         13 . The method of  claim 1 , wherein the in vivo analyte is included in a human body, an animal, a mammal, a non-mammal or a microorganism.