Research Papers

Investigations on Multisensor-Based Noninvasive Blood Glucose Measurement System

[+] Author and Article Information
Jyoti Yadav

Research Lab,
Instrumentation and Control
Engineering Division,
Block-6, Dwarka,
New Delhi 110078, India
e-mail: bmjyoti@gmail.com

Asha Rani

Research Lab,
Instrumentation and Control
Engineering Division,
Block-6, Dwarka,
New Delhi 110078, India
e-mail: ashansit@gmail.com

Vijander Singh

Research Lab,
Instrumentation and Control
Engineering Division,
Block-6, Dwarka,
New Delhi 110078, India
e-mail: vijaydee@gmail.com

Bhaskar Mohan Murari

Department of Sensors and
Biomedical Technology,
VIT University,
Vellore 632014, India
e-mail: bhaskarmurari@vit.ac.in

Manuscript received July 20, 2016; final manuscript received March 13, 2017; published online June 27, 2017. Assoc. Editor: Rafael V. Davalos.

J. Med. Devices 11(3), 031006 (Jun 27, 2017) (7 pages) Paper No: MED-16-1271; doi: 10.1115/1.4036580 History: Received July 20, 2016; Revised March 13, 2017

Noninvasive blood glucose (NIBG) measurement technique has been explored for the last three decades to facilitate diabetes management. Photoplethysmogram (PPG) signal may be used to measure the variations in blood glucose concentration. However, the literature reveals that physiological perturbations such as temperature, skin moisture, and sweat lead to less accurate NIBG measurements. The task of minimizing the effect of these perturbations for accurate measurements is an important research area. Therefore, in the present work, galvanic skin response (GSR) and temperature measurements along with PPG were used to measure blood glucose noninvasively. The data extracted from the sensors were used to estimate blood glucose concentration with the help of two machine learning (ML) techniques, i.e., multiple linear regression (MLR) and artificial neural network (ANN). The accuracy of proposed multisensor system was evaluated by pairing and comparing noninvasive measurements with invasively measured readings. The study was performed on 50 nondiabetic subjects with body mass index (BMI) 27.3 ± 3 kg/m2. The results revealed that multisensor NIBG measurement system significantly improves mean absolute prediction error and correlation coefficient in comparison to the techniques reported in the literature.

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Grahic Jump Location
Fig. 1

(a) Block diagram of NIBG measurement and (b) multiple sensor based NIBG measurement system

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Fig. 2

Detailed ANN structure

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Fig. 3

Regression analysis of neural network based on LM-based ANN model

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Fig. 4

Best validation performance

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Fig. 6

Clark error grid analysis (EGA) for LM



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