Evidence map›Paper›PMID 32289073›Full record

ArticleInformatics in medicine unlocked2019

A non-contact infection screening system using medical radar and Linux-embedded FPGA: Implementation and preliminary validation.

Cuong V Nguyen, Truong Le Quang, Trung Nguyen Vu, Hoi Le Thi, Kinh Nguyen Van, Thanh Han Trong, Tuan Do Trong, Guanghao Sun, Koichiro Ishibashi

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Informatics in medicine unlocked, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 37% 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
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

9 authors at 3 institutions in 2 countries.

Cuong V NguyenSchool of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam.
Truong Le QuangSchool of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam.
Trung Nguyen VuNational Hospital of Tropical Diseases, Hanoi, Viet Nam.
Hoi Le ThiNational Hospital of Tropical Diseases, Hanoi, Viet Nam.
Kinh Nguyen VanNational Hospital of Tropical Diseases, Hanoi, Viet Nam.
Thanh Han TrongSchool of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam.
Tuan Do TrongSchool of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam.
Guanghao SunGraduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, 182-8585, Japan.
Koichiro IshibashiGraduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, 182-8585, Japan.
Hanoi University of Science and Technology · VNHospital for Tropical Diseases · VNUniversity of Electro-Communications · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesIn this study, an infection screening system was developed to detect patients suffering from infectious diseases. In addition, the system was also designed to deal with the variability in age and gender, which would affect the accuracy of the detection. Furthermore, to enable a low-cost, non-contact and embedded system, multiple vital signs from a medical radar were measured and all algorithms were implemented on a Field Programmable Gate Array, named PYNQ-Z1.

methodsThe system consisted of two main stages: digital signal processing and data classification. In the former stage, Butterworth filters, with flexible cut-off frequencies depending on age and gender, and a time-domain peak detection algorithm were deployed to compute three vital signs, namely heart rate, respiratory rate, and standard deviation of heart beat-to-beat interval. For the classification problem, two machine learning models, Support Vector Machine and Quadratic Discriminant Analysis, were implemented.

resultsThe Student's

conclusionThis study outlines the implementation of an infection screening system, which achieved competent performance. The system might be beneficial for fast screening of infected patients at public health centers in underdeveloped areas, where people have little access to healthcare.

Indexed as

Digital signal processingEmbedded systemField programmable gate arrayInfection screeningMachine learning

Identifiers

PMID32289073
PMCPMC7103934
OpenAlexW2967192909

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.