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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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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 |
Sumario: | 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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