Evidence map›Paper›PMID 32938996›Full record

ArticleScientific reports2020

Low-cost portable microwave sensor for non-invasive monitoring of blood glucose level: novel design utilizing a four-cell CSRR hexagonal configuration.

Ala Eldin Omer, George Shaker, Safieddin Safavi-Naeini, Hamid Kokabi, Georges Alquié, Frédérique Deshours, Raed M Shubair

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed
13.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

55 citing papers in PubMed, 254 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 4 countries.

Ala Eldin OmerDepartment of Electrical and Computer Engineering, Centre for Intelligent Antenna and Radio Systems (CIARS), University of Waterloo, Waterloo, ON, Canada. aeomomer@uwaterloo.ca.
George ShakerDepartment of Electrical and Computer Engineering, Centre for Intelligent Antenna and Radio Systems (CIARS), University of Waterloo, Waterloo, ON, Canada.
Safieddin Safavi-NaeiniDepartment of Electrical and Computer Engineering, Centre for Intelligent Antenna and Radio Systems (CIARS), University of Waterloo, Waterloo, ON, Canada.
Hamid KokabiGroup of Electrical Engineering Paris (GeePs), UMR CNRS-CentraleSupelec - University Paris-Saclay - Sorbonne University, Paris, France.
Georges AlquiéGroup of Electrical Engineering Paris (GeePs), UMR CNRS-CentraleSupelec - University Paris-Saclay - Sorbonne University, Paris, France.
Frédérique DeshoursGroup of Electrical Engineering Paris (GeePs), UMR CNRS-CentraleSupelec - University Paris-Saclay - Sorbonne University, Paris, France.
Raed M ShubairDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA.
Laboratoire de Génie Électrique et Électronique de Paris · FRUniversity of Waterloo · CAMassachusetts Institute of Technology · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article presents a novel design of portable planar microwave sensor for fast, accurate, and non-invasive monitoring of the blood glucose level as an effective technique for diabetes control and prevention. The proposed sensor design incorporates four cells of hexagonal-shaped complementary split ring resonators (CSRRs), arranged in a honey-cell configuration, and fabricated on a thin sheet of an FR4 dielectric substrate.The CSRR sensing elements are coupled via a planar microstrip-line to a radar board operating in the ISM band 2.4-2.5 GHz. The integrated sensor shows an impressive detection capability and a remarkable sensitivity of blood glucose levels (BGLs). The superior detection capability is attributed to the enhanced design of the CSRR sensing elements that expose the glucose samples to an intense interaction with the electromagnetic fields highly concentrated around the sensing region at the induced resonances. This feature enables the developed sensor to detect extremely delicate variations in the electromagnetic properties that characterize the varying-level glucose samples. The desired performance of the fabricated sensor is practically validated through in-vitro measurements using a convenient setup of Vector Network Analyzer (VNA) that records notable traces of frequency-shift responses when the sensor is loaded with samples of 70-120 mg/dL glucose concentrations. This is also demonstrated in the radar-driven prototype where the raw data collected at the radar receiving channel shows obvious patterns that reflect glucose-level variations. Furthermore, the differences in the sensor responses for tested glucose samples are quantified by applying the Principal Component Analysis (PCA) machine learning algorithm. The proposed sensor, beside its impressive detection capability of the diabetes-spectrum glucose levels, has several other favorable attributes including compact size, simple fabrication, affordable cost, non-ionizing nature, and minimum health risk or impact. Such attractive features promote the proposed sensor as a possible candidate for non-invasive glucose levels monitoring for diabetes as evidenced by the preliminary results from a proof-of-concept in-vivo experiment of tracking an individual's BGL by placing his fingertip onto the sensor. The presented system is a developmental platform towards radar-driven wearable continuous BGL monitors.

Indexed as

Blood GlucoseDiabetes Mellitus, Type 2Electromagnetic PhenomenaEquipment DesignHumansMicrowavesMonitoring, PhysiologicBlood Glucose

Identifiers

PMID32938996
PMCPMC7494924
OpenAlexW3087183557

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.