Detail publikace

COVID-19 Diagnosis at early stage Based on smartwatches and machine learning Techniques

SKIBIŃSKA, J. BURGET, R. CHANNA, A. POPESCU, N. KOUCHERYAVY, Y.

Originální název

COVID-19 Diagnosis at early stage Based on smartwatches and machine learning Techniques

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

Early detection of COVID-19 positive people are now extremely needed and considered to be one of the most effective ways how to limit spreading the infection. Commonly used screening methods are reverse transcription polymerase chain reaction (RT-PCR) or antigen tests, which need to be periodically repeated. This paper proposes a methodology for detecting the disease in non-invasive way using wearable devices and for the analysis of bio-markers using artificial intelligence. This paper have reused a publicly available dataset containing COVID-19, influenza, and Healthy control data. In total 27 COVID-19 positive and 27 healthy control were pre-selected for the experiment, and several feature extraction methods were applied to the data. This paper have experimented with several machine learning methods, such as XGBoost, k-nearest neighbour k-NN, support vector machine, logistic regression, decision tree, and random forest, and statistically evaluated their perfomance using various metrics, including accuracy, sensitivity and specificity. The proposed experiment reached 78% accuracy using the k-NN algorithm which is significantly higher than reported for state-of-the-art methods. For the cohort containing influenza, the accuracy was 73 % for k-NN. Additionally, we identified the most relevant features that could indicate the changes between the healthy and infected state. The proposed methodology can complement the existing RT-PCR or antigen screening tests, and it can help to limit the spreading of the viral diseases, not only COVID-19, in the non-invasive way.

Klíčová slova

artificial intelligence, COVID, singal processing

Autoři

SKIBIŃSKA, J.; BURGET, R.; CHANNA, A.; POPESCU, N.; KOUCHERYAVY, Y.

Vydáno

7. 8. 2021

Nakladatel

IEEE

ISSN

2169-3536

Periodikum

IEEE Access

Ročník

9

Číslo

1

Stát

Spojené státy americké

Strany od

119476

Strany do

119491

Strany počet

16

URL

https://ieeexplore.ieee.org/document/9517046

Plný text v Digitální knihovně

http://hdl.handle.net/11012/203104

BibTex

@article{BUT172256,
  author="Justyna {Skibińska} and Radim {Burget} and Asma {Channa} and Nirvana {Popescu} and Yevgeni {Koucheryavy}",
  title="COVID-19 Diagnosis at early stage Based on smartwatches and machine learning Techniques",
  journal="IEEE Access",
  year="2021",
  volume="9",
  number="1",
  pages="119476--119491",
  doi="10.1109/ACCESS.2021.3106255",
  issn="2169-3536",
  url="https://ieeexplore.ieee.org/document/9517046"
}