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Design and simulation of AI-based low-cost mechanical ventilator: An approach

In this situation of COVID 19, many people are being exposed to coronavirus, resulting in difficulty in breathing and a drop in oxygen percentage of blood. A mechanical ventilator is playing a vital role in tackling this situation but the ventilation process is neither readily available nor affordab...

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Detalles Bibliográficos
Autores principales: Giri, Jayant, Kshirsagar, Niraj, Wanjari, Aishwary
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8160574/
https://www.ncbi.nlm.nih.gov/pubmed/34075333
http://dx.doi.org/10.1016/j.matpr.2021.04.369
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author Giri, Jayant
Kshirsagar, Niraj
Wanjari, Aishwary
author_facet Giri, Jayant
Kshirsagar, Niraj
Wanjari, Aishwary
author_sort Giri, Jayant
collection PubMed
description In this situation of COVID 19, many people are being exposed to coronavirus, resulting in difficulty in breathing and a drop in oxygen percentage of blood. A mechanical ventilator is playing a vital role in tackling this situation but the ventilation process is neither readily available nor affordable. The idea behind this work is to propose a simplified design of a mechanical ventilator to reduce the cost and automate the Mechanical ventilation process. The simplified design, it's working, and required components are elaborated in this paper. The simulation of the proposed design is made in MATLAB/Simulink platform which is also discussed below. Taking into account the work done in the area of cost reduction of the mechanical ventilation process, the mechanical ventilator with a simplified design comprising of compressed air and oxygen source is being considered. The parameters considered for mechanical ventilation are positive end-expiratory pressure (PEEP), pressure wave, respiratory rate (RR), tidal volume, etc. These parameters of oxygen and air mixture are to be controlled with the help of electronic devices which are pressure regulator, solenoid valve, flow sensor, proportional valve, microprocessor, etc depending upon the condition of patient and type of disease. Simulation results are promising and precise which allows the study on ventilator model without jeopardizing the life of human subjects as in clinical approach and hides the complexity of computational models from the user. Furthermore, advancements in this model are done by the machine learning approach.
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spelling pubmed-81605742021-05-28 Design and simulation of AI-based low-cost mechanical ventilator: An approach Giri, Jayant Kshirsagar, Niraj Wanjari, Aishwary Mater Today Proc Article In this situation of COVID 19, many people are being exposed to coronavirus, resulting in difficulty in breathing and a drop in oxygen percentage of blood. A mechanical ventilator is playing a vital role in tackling this situation but the ventilation process is neither readily available nor affordable. The idea behind this work is to propose a simplified design of a mechanical ventilator to reduce the cost and automate the Mechanical ventilation process. The simplified design, it's working, and required components are elaborated in this paper. The simulation of the proposed design is made in MATLAB/Simulink platform which is also discussed below. Taking into account the work done in the area of cost reduction of the mechanical ventilation process, the mechanical ventilator with a simplified design comprising of compressed air and oxygen source is being considered. The parameters considered for mechanical ventilation are positive end-expiratory pressure (PEEP), pressure wave, respiratory rate (RR), tidal volume, etc. These parameters of oxygen and air mixture are to be controlled with the help of electronic devices which are pressure regulator, solenoid valve, flow sensor, proportional valve, microprocessor, etc depending upon the condition of patient and type of disease. Simulation results are promising and precise which allows the study on ventilator model without jeopardizing the life of human subjects as in clinical approach and hides the complexity of computational models from the user. Furthermore, advancements in this model are done by the machine learning approach. Elsevier Ltd. 2021 2021-05-28 /pmc/articles/PMC8160574/ /pubmed/34075333 http://dx.doi.org/10.1016/j.matpr.2021.04.369 Text en © 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Technology Innovation in Mechanical Engineering-2021. 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
Giri, Jayant
Kshirsagar, Niraj
Wanjari, Aishwary
Design and simulation of AI-based low-cost mechanical ventilator: An approach
title Design and simulation of AI-based low-cost mechanical ventilator: An approach
title_full Design and simulation of AI-based low-cost mechanical ventilator: An approach
title_fullStr Design and simulation of AI-based low-cost mechanical ventilator: An approach
title_full_unstemmed Design and simulation of AI-based low-cost mechanical ventilator: An approach
title_short Design and simulation of AI-based low-cost mechanical ventilator: An approach
title_sort design and simulation of ai-based low-cost mechanical ventilator: an approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8160574/
https://www.ncbi.nlm.nih.gov/pubmed/34075333
http://dx.doi.org/10.1016/j.matpr.2021.04.369
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