SHERP

Classification of glucose concentration in diluted urine using the low-resolution Raman spectroscopy and kernel optimization methods

Cited 0 time in webofscience Cited 13 time in scopus
Authors
Park, CheolSoo; Kim, KoKeun; Choi, JongMin; Park, KwangSuk
Issue Date
2007-05-02
Publisher
Institute of Physics
Citation
Physiol Meas. 2007 May;28(5):583-93. Epub 2007 Apr 30.
Keywords
AlgorithmsGlycosuria/*diagnosisHumansMonitoring, Physiologic/*methodsSpectrum Analysis, RamanToilet Facilities
Abstract
In order to detect minute amounts of glucose in diluted urine, we applied the Raman spectroscopy method. To simulate abnormal diluted urine in a toilet bowl, we diluted normal urine ten-fold with water and added glucose up to 8 mg dl(-1). Data were collected using a low-resolution Raman spectrometer that was preprocessed with the optimizing kernel method. We also applied the neural network algorithm to classify abnormal and normal urine samples according to their glucose concentrations. The kernel optimizing method was very effective in the classification of the tested subjects as it increased the accuracy of classification by 92%. This method suggests the possibility of caring for patients by daily monitoring their urine components in a manner non-invasive to ordinary life.
ISSN
0967-3334 (Print)
Language
English
URI
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17470990

http://hdl.handle.net/10371/15813
DOI
https://doi.org/10.1088/0967-3334/28/5/011
Files in This Item:
There are no files associated with this item.
Appears in Collections:
College of Medicine/School of Medicine (의과대학/대학원)Biomedical Engineering (의공학전공)Journal Papers (저널논문_의공학전공)
  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse