Machine learning approach for non-invasive detection of blood glucose concentration using microwave sensor

People suffering from hyperglycaemia or diabetes mellitus are increasing day by day. The only commercial devices available to measure blood glucose levels are based on invasive methods, such as collecting blood samples from an individual and testing it. However, for a person, whose blood glucose...

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Main Author: Rakesh Reddy, Yeddula Nagabhushigari
Other Authors: Muhammad Faeyz Karim
Format: Theses and Dissertations
Language:English
Published: 2018
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Online Access:http://hdl.handle.net/10356/75954
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-759542023-07-04T15:56:16Z Machine learning approach for non-invasive detection of blood glucose concentration using microwave sensor Rakesh Reddy, Yeddula Nagabhushigari Muhammad Faeyz Karim School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering People suffering from hyperglycaemia or diabetes mellitus are increasing day by day. The only commercial devices available to measure blood glucose levels are based on invasive methods, such as collecting blood samples from an individual and testing it. However, for a person, whose blood glucose levels have to be monitored at a regular interval, the conventional invasive methods are painful, sore and thus not preferred. In order to overcome these problems, non-invasive methods have to replace the conventional forms. The non-invasive methods have not taken a commercial form yet. This project is an attempt to develop a non-invasive glucose measurement method. A non-invasive blood glucose measuring method based on Microwave transmission and then applying Machine-learning technique for the data obtained is proposed for monitoring the patients' blood glucose level. With this method, a non-invasive measurement of the blood glucose determination of the earlobe portion can be realized by analysing the received microwave signals. In this project, the coefficients of the third order Cole-Cole equation are derived to model the dielectric properties of human tissues. 'Particle swarm optimization' technique is used to determine the coefficients for the glucose concentration dependent equations. With these estimated dielectric values of human tissues, Human ear lobe portion is modelled in a simulation setup and tested over a wide range of frequencies to check for the region of linearity. The proposed method is validated by applying it to a solution prepared, which is impersonating the dielectric properties of blood plasma and it is observed that the region of linearity exists from 6 - 8 GHz. The proposed method of detecting blood glucose concentration is very convenient and is harmless to the patients. Master of Science (Signal Processing) 2018-09-10T07:44:43Z 2018-09-10T07:44:43Z 2018 Thesis http://hdl.handle.net/10356/75954 en 65 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Rakesh Reddy, Yeddula Nagabhushigari
Machine learning approach for non-invasive detection of blood glucose concentration using microwave sensor
description People suffering from hyperglycaemia or diabetes mellitus are increasing day by day. The only commercial devices available to measure blood glucose levels are based on invasive methods, such as collecting blood samples from an individual and testing it. However, for a person, whose blood glucose levels have to be monitored at a regular interval, the conventional invasive methods are painful, sore and thus not preferred. In order to overcome these problems, non-invasive methods have to replace the conventional forms. The non-invasive methods have not taken a commercial form yet. This project is an attempt to develop a non-invasive glucose measurement method. A non-invasive blood glucose measuring method based on Microwave transmission and then applying Machine-learning technique for the data obtained is proposed for monitoring the patients' blood glucose level. With this method, a non-invasive measurement of the blood glucose determination of the earlobe portion can be realized by analysing the received microwave signals. In this project, the coefficients of the third order Cole-Cole equation are derived to model the dielectric properties of human tissues. 'Particle swarm optimization' technique is used to determine the coefficients for the glucose concentration dependent equations. With these estimated dielectric values of human tissues, Human ear lobe portion is modelled in a simulation setup and tested over a wide range of frequencies to check for the region of linearity. The proposed method is validated by applying it to a solution prepared, which is impersonating the dielectric properties of blood plasma and it is observed that the region of linearity exists from 6 - 8 GHz. The proposed method of detecting blood glucose concentration is very convenient and is harmless to the patients.
author2 Muhammad Faeyz Karim
author_facet Muhammad Faeyz Karim
Rakesh Reddy, Yeddula Nagabhushigari
format Theses and Dissertations
author Rakesh Reddy, Yeddula Nagabhushigari
author_sort Rakesh Reddy, Yeddula Nagabhushigari
title Machine learning approach for non-invasive detection of blood glucose concentration using microwave sensor
title_short Machine learning approach for non-invasive detection of blood glucose concentration using microwave sensor
title_full Machine learning approach for non-invasive detection of blood glucose concentration using microwave sensor
title_fullStr Machine learning approach for non-invasive detection of blood glucose concentration using microwave sensor
title_full_unstemmed Machine learning approach for non-invasive detection of blood glucose concentration using microwave sensor
title_sort machine learning approach for non-invasive detection of blood glucose concentration using microwave sensor
publishDate 2018
url http://hdl.handle.net/10356/75954
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