Estimation of pulmonary artery occlusion pressure by an artificial neural network
| Autor: | Royce W. Johnson, Michael G. Levitzky, Bennett P. deBoisblanc, Espisito Mcclarty, Michael S. Champagne, Andrew A. Pellett, Gundeep Dhillon |
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| Rok vydání: | 2003 |
| Předmět: |
business.industry
Hemodynamics Blood Pressure Determination Signal Processing Computer-Assisted Critical Care and Intensive Care Medicine law.invention Dogs Blood pressure Pressure measurement law Intensive care Anesthesia medicine.artery Occlusion Pulmonary artery Circulatory system Linear Models Animals Medicine Diagnosis Computer-Assisted Neural Networks Computer Pulmonary Wedge Pressure business Pulmonary wedge pressure |
| Zdroj: | Critical Care Medicine. 31:261-266 |
| ISSN: | 0090-3493 |
| DOI: | 10.1097/00003246-200301000-00041 |
| Popis: | Objective: We hypothesized that an artificial neural network, interconnected computer elements capable of adaptation and learning, could accurately estimate pulmonary artery occlusion pressure from the pulsatile pulmonary artery waveform. Setting: University medical center. Subjects: Nineteen closed-chest dogs. Interventions: Pulmonary artery waveforms were digitally sampled before conventional measurements of pulmonary artery occlusion pressure under control conditions, during infusions of serotonin or histamine, or during volume loading. Individual beats were parsed or separated out. Pulmonary artery pressure, its first time derivative, and the beat duration were used as neural inputs. The neural network was trained by using 80% of all samples and tested on the remaining 20%. For comparison, the regression between pulmonary artery diastolic pressure and pulmonary artery occlusion pressure was developed and tested using the same data sets. As a final test of generalizability, the neural network was trained on data obtained from 18 dogs and tested on data from the remaining dog in a round-robin fashion. Measurements and Main Results: The correlation coefficient between the pulmonary artery diastolic pressure estimate of pulmonary artery occlusion pressure and measured pulmonary artery occlusion pressure was.75, whereas that for the neural network estimate of pulmonary artery occlusion pressure was.97 (p |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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