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COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology
At present, COVID-19 is spreading widely around the world. It causes many health problems, namely, respiratory failure and acute respiratory distress syndrome. Wearable devices have gained popularity by allowing remote COVID-19 detection, contact tracing, and monitoring. In this study, the correlati...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343670/ https://www.ncbi.nlm.nih.gov/pubmed/35928478 http://dx.doi.org/10.3389/fpubh.2022.920849 |
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author | Nayan, Nazrul Anuar Jie Yi, Choon Suboh, Mohd Zubir Mazlan, Nur-Fadhilah Periyasamy, Petrick Abdul Rahim, Muhammad Yusuf Zawir Shah, Shamsul Azhar |
author_facet | Nayan, Nazrul Anuar Jie Yi, Choon Suboh, Mohd Zubir Mazlan, Nur-Fadhilah Periyasamy, Petrick Abdul Rahim, Muhammad Yusuf Zawir Shah, Shamsul Azhar |
author_sort | Nayan, Nazrul Anuar |
collection | PubMed |
description | At present, COVID-19 is spreading widely around the world. It causes many health problems, namely, respiratory failure and acute respiratory distress syndrome. Wearable devices have gained popularity by allowing remote COVID-19 detection, contact tracing, and monitoring. In this study, the correlation of photoplethysmogram (PPG) morphology between patients with COVID-19 infection and healthy subjects was investigated. Then, machine learning was used to classify the extracted features between 43 cases and 43 control subjects. The PPG data were collected from 86 subjects based on inclusion and exclusion criteria. The systolic-onset amplitude was 3.72% higher for the case group. However, the time interval of systolic-systolic was 7.69% shorter in the case than in control subjects. In addition, 12 out of 20 features exhibited a significant difference. The top three features included dicrotic-systolic time interval, onset-dicrotic amplitude, and systolic-onset time interval. Nine features extracted by heatmap based on the correlation matrix were fed to discriminant analysis, k-nearest neighbor, decision tree, support vector machine, and artificial neural network (ANN). The ANN showed the best performance with 95.45% accuracy, 100% sensitivity, and 90.91% specificity by using six input features. In this study, a COVID-19 prediction model was developed using multiple PPG features extracted using a low-cost pulse oximeter. |
format | Online Article Text |
id | pubmed-9343670 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93436702022-08-03 COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology Nayan, Nazrul Anuar Jie Yi, Choon Suboh, Mohd Zubir Mazlan, Nur-Fadhilah Periyasamy, Petrick Abdul Rahim, Muhammad Yusuf Zawir Shah, Shamsul Azhar Front Public Health Public Health At present, COVID-19 is spreading widely around the world. It causes many health problems, namely, respiratory failure and acute respiratory distress syndrome. Wearable devices have gained popularity by allowing remote COVID-19 detection, contact tracing, and monitoring. In this study, the correlation of photoplethysmogram (PPG) morphology between patients with COVID-19 infection and healthy subjects was investigated. Then, machine learning was used to classify the extracted features between 43 cases and 43 control subjects. The PPG data were collected from 86 subjects based on inclusion and exclusion criteria. The systolic-onset amplitude was 3.72% higher for the case group. However, the time interval of systolic-systolic was 7.69% shorter in the case than in control subjects. In addition, 12 out of 20 features exhibited a significant difference. The top three features included dicrotic-systolic time interval, onset-dicrotic amplitude, and systolic-onset time interval. Nine features extracted by heatmap based on the correlation matrix were fed to discriminant analysis, k-nearest neighbor, decision tree, support vector machine, and artificial neural network (ANN). The ANN showed the best performance with 95.45% accuracy, 100% sensitivity, and 90.91% specificity by using six input features. In this study, a COVID-19 prediction model was developed using multiple PPG features extracted using a low-cost pulse oximeter. Frontiers Media S.A. 2022-07-19 /pmc/articles/PMC9343670/ /pubmed/35928478 http://dx.doi.org/10.3389/fpubh.2022.920849 Text en Copyright © 2022 Nayan, Jie Yi, Suboh, Mazlan, Periyasamy, Abdul Rahim and Shah. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Nayan, Nazrul Anuar Jie Yi, Choon Suboh, Mohd Zubir Mazlan, Nur-Fadhilah Periyasamy, Petrick Abdul Rahim, Muhammad Yusuf Zawir Shah, Shamsul Azhar COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology |
title | COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology |
title_full | COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology |
title_fullStr | COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology |
title_full_unstemmed | COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology |
title_short | COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology |
title_sort | covid-19 prediction with machine learning technique from extracted features of photoplethysmogram morphology |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343670/ https://www.ncbi.nlm.nih.gov/pubmed/35928478 http://dx.doi.org/10.3389/fpubh.2022.920849 |
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