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Microcontroller-Based Detection of Diabetes and Ketosis State Using Breath Sensors

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Recent Trends in Image and Signal Processing in Computer Vision

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1124))

Abstract

Researchers have demonstrated that breath acetone is an effective biomarker of type 2 diabetes which a habitual form of diabetes. Conventional way for the detection of glucose levels is through invasive technique which involves pricking the finger and collecting blood samples. This is not only painful and blood consuming but also time-consuming and expensive. Therefore, there has been a great demand for the non-invasive techniques of blood glucose determinations in the commercial market. Researchers have been attempting to develop a number of non-invasive techniques where the diabetes is detected by different methods outside the body, without puncturing the skin or without taking the blood sample.

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Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, R V College of Engineering, Bengaluru, 560059, India

    C. H. Renumadhavi, N. Jamuna, A. N. Chandana & Vinay Balamurali

  2. Department of Biotechnology, R V College of Engineering, Bengaluru, 560059, India

    Praveen Kumar Gupta, Ryna Shireen Sheriff & Anushree Vinayak Lokur

  3. Department of Electronics and Communication Engineering, R V College of Engineering, Bengaluru, 560059, India

    Rhutu Kallur & R. Sindhu

Authors
  1. C. H. Renumadhavi
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  2. N. Jamuna
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  4. Vinay Balamurali
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  9. R. Sindhu
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Correspondence to C. H. Renumadhavi .

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Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    Shruti Jain

  2. Department of Biomedical Engineering, North-Eastern Hill University, Shillong, Meghalaya, India

    Sudip Paul

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Renumadhavi, C.H. et al. (2020). Microcontroller-Based Detection of Diabetes and Ketosis State Using Breath Sensors. In: Jain, S., Paul, S. (eds) Recent Trends in Image and Signal Processing in Computer Vision. Advances in Intelligent Systems and Computing, vol 1124. Springer, Singapore. https://doi.org/10.1007/978-981-15-2740-1_1

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