Work

Ventilator pressure prediction using recurrent neural network

Year: 2024

Type: article

Abstract: This paper presents a recurrent neural network approach to simulating mechanical ventilator pressure. The traditional mechanical ventilator has a control pressure that is monitored by a medical practitioner and can behave incorrectly if the proper pressure is not applied. This paper takes advantage of recent research and develops a simulator based on a deep sequence model to predict airway pressure in the respiratory circuit during the inspiratory phase of a breath given a time series of control parameters and lung attributes. This method demonstrates the effectiveness of neural network-based controllers in tracking pressure wave forms significantly better than the current industry standard and provides insights into the development of effective and robust pressure-controlled mechanical ventilators. The paper will measure as the mean absolute error between the predicted and actual pressures during the inspiratory phase of each breath.

Source: Revista Latinoamericana de Hipertensión/Revista Latinoamericana de Hipertensiòn

DOI: 10.5281/zenodo.14277317

Authors: Su Diao, Changsong Wei, Junyu Wang, Yizhou Li

Institutions: Auburn University, University of California, Los Angeles, Case Western Reserve University

Language: English

Topic: Respiratory Support and Mechanisms

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W6911746331

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