Ventilator pressure prediction using recurrent neural network
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909342995841024 |
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| author | Diao, Su Wei, Changsong Wang, Junyu Li, Yizhou |
| author_facet | Diao, Su Wei, Changsong Wang, Junyu Li, Yizhou |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06552 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ventilator pressure prediction using recurrent neural network Diao, Su Wei, Changsong Wang, Junyu Li, Yizhou Distributed, Parallel, and Cluster Computing 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. |
| title | Ventilator pressure prediction using recurrent neural network |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2410.06552 |