A Method for Detection and Classification of Diabetes Noninvasively

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 490)

Abstract

Diabetes a common ailment affecting the vast population of people requires continues monitoring of blood glucose levels so as to control this disorder. Presently, the common technique used to monitor these levels is through an invasive process of drawing blood. Although this technique achieves high accuracy, it encompasses all disadvantages associated with an invasive method. This inconvenience is felt more accurately in patients who frequently examine these levels through the day. Hence, there is a need for a noninvasive technique for predicting the glucose levels. This paper aims at analyzing the breath as a noninvasive technique to predict diabetes.

References

  1. 1.
    Alberti KGMM, Zimmet PZ (1998) Denition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO consultation. Diabet Med 15(7):539–553CrossRefGoogle Scholar
  2. 2.
    Makaram P, Owens D, Aceros J (2014) Trends in nanomaterial-based non-invasive diabetes sensing technologies. Diagnostics 4(2):27–46CrossRefGoogle Scholar
  3. 3.
    Wang P, Tan Y, Xie H, Shen F (1997) A novel method for diabetes diagnosis based on electronic nose. Biosens Bioelectron 12(9):10311036Google Scholar
  4. 4.
    Deng C, Zhang J, Yu X, Zhang X, Zhang X (2004) Determination of acetone in human breath by gas chromatography mass spectrometry and solid-phase microextraction with on-fiber derivatization. J Chromatogr 810:269–275Google Scholar
  5. 5.
    Moorhead K, Lee D, Chase JG, Moot A, Ledingham K, Scotter J, Allardyce R, Senthilmohan S, Endre Z (2007) Classification algorithms for SIFT-MS medical diagnosis. In: Proceedings of the 29th annual international conference of the IEEE EMBS, Cit Internationale, Lyon, France, 23–26 AugustGoogle Scholar
  6. 6.
    Wang C, Mbi A, Shepherd M (2010) A study on breath acetone in diabetic patients using a cavity ringdown breath analyzer: exploring correlations of breath acetone with blood glucose and glycohemoglobin A1C. IEEE Sens J 10(1):54–63 CrossRefGoogle Scholar
  7. 7.
    Guo D, Zhang D, Li N, Zhang L, Yang J (2010) A novel breath analysis system based on electronic olfaction. IEEE Trans Biomed Eng 57(11):2753–2763CrossRefGoogle Scholar
  8. 8.
    Lee DS et al (2003) GaN thin films as gas sensors. Sens Actuators B: Chem 89(3):305–310CrossRefGoogle Scholar
  9. 9.
    Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):127CrossRefGoogle Scholar
  10. 10.
    Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167CrossRefGoogle Scholar
  11. 11.
    Muthuvel K, Suresh LP, Veni SK, Kannan KB (2014) ECG signal feature extraction and classification using harr wavelet transform and neural network. In: 2014 International Conference on circuit, power and computing technologies (ICCPCT), 20 Mar 2014, pp 1396–1399, IEEEGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Electronics EngineeringVIT UniversityChennaiIndia

Personalised recommendations