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Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence
Today COVID-19 pandemic articulates high stress on clinical resources around the world. At present, physical and viral tests are slowly emerging, and there is a need for robust pandemic detection that biomedical sensors can aid. The utility of biomedical sensors is correlated with the medical instru...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8957369/ https://www.ncbi.nlm.nih.gov/pubmed/35368881 http://dx.doi.org/10.1016/j.measurement.2022.111054 |
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author | Hemamalini, V. Anand, L. Nachiyappan, S. Geeitha, S. Ramana Motupalli, Venkata Kumar, R. Ahilan, A. Rajesh, M. |
author_facet | Hemamalini, V. Anand, L. Nachiyappan, S. Geeitha, S. Ramana Motupalli, Venkata Kumar, R. Ahilan, A. Rajesh, M. |
author_sort | Hemamalini, V. |
collection | PubMed |
description | Today COVID-19 pandemic articulates high stress on clinical resources around the world. At present, physical and viral tests are slowly emerging, and there is a need for robust pandemic detection that biomedical sensors can aid. The utility of biomedical sensors is correlated with the medical instruments with physiological metrics. These Biomedical sensors are integrated with the systematic device to track the target analytes with a biomedical component. The COVID-19 patients' samples are collected, and biomarkers are detected using four sensors: blood pressure sensor, G-FET based biosensor, electrochemical sensor, and potentiometric sensor with different quantifiable measures. The imputed data is then profiled with chest X-ray images from the Covid-19 patients.Multi-Layer Perceptron (MLP), an AI model, is deployed to identify the hidden signatures with biomarkers. The performance of the biosensor is measured with three parameters such as sensitivity, specificity and detection limit by generating the calibration plots that accurately fits the model. |
format | Online Article Text |
id | pubmed-8957369 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89573692022-03-28 Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence Hemamalini, V. Anand, L. Nachiyappan, S. Geeitha, S. Ramana Motupalli, Venkata Kumar, R. Ahilan, A. Rajesh, M. Measurement (Lond) Article Today COVID-19 pandemic articulates high stress on clinical resources around the world. At present, physical and viral tests are slowly emerging, and there is a need for robust pandemic detection that biomedical sensors can aid. The utility of biomedical sensors is correlated with the medical instruments with physiological metrics. These Biomedical sensors are integrated with the systematic device to track the target analytes with a biomedical component. The COVID-19 patients' samples are collected, and biomarkers are detected using four sensors: blood pressure sensor, G-FET based biosensor, electrochemical sensor, and potentiometric sensor with different quantifiable measures. The imputed data is then profiled with chest X-ray images from the Covid-19 patients.Multi-Layer Perceptron (MLP), an AI model, is deployed to identify the hidden signatures with biomarkers. The performance of the biosensor is measured with three parameters such as sensitivity, specificity and detection limit by generating the calibration plots that accurately fits the model. Elsevier Ltd. 2022-05-15 2022-03-26 /pmc/articles/PMC8957369/ /pubmed/35368881 http://dx.doi.org/10.1016/j.measurement.2022.111054 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Hemamalini, V. Anand, L. Nachiyappan, S. Geeitha, S. Ramana Motupalli, Venkata Kumar, R. Ahilan, A. Rajesh, M. Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence |
title | Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence |
title_full | Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence |
title_fullStr | Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence |
title_full_unstemmed | Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence |
title_short | Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence |
title_sort | integrating bio medical sensors in detecting hidden signatures of covid-19 with artificial intelligence |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8957369/ https://www.ncbi.nlm.nih.gov/pubmed/35368881 http://dx.doi.org/10.1016/j.measurement.2022.111054 |
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