Iterative integral parameter identification of a respiratory mechanics model
<p>Abstract</p> <p>Background</p> <p>Patient-specific respiratory mechanics models can support the evaluation of optimal lung protective ventilator settings during ventilation therapy. Clinical application requires that the individual’s model parameter values must be id...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2012-07-01
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| Series: | BioMedical Engineering OnLine |
| Subjects: | |
| Online Access: | http://www.biomedical-engineering-online.com/content/11/1/38 |
| _version_ | 1819155347072352256 |
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| author | Schranz Christoph Docherty Paul D Chiew Yeong Möller Knut Chase J |
| author_facet | Schranz Christoph Docherty Paul D Chiew Yeong Möller Knut Chase J |
| author_sort | Schranz Christoph |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Patient-specific respiratory mechanics models can support the evaluation of optimal lung protective ventilator settings during ventilation therapy. Clinical application requires that the individual’s model parameter values must be identified with information available at the bedside. Multiple linear regression or gradient-based parameter identification methods are highly sensitive to noise and initial parameter estimates. Thus, they are difficult to apply at the bedside to support therapeutic decisions.</p> <p>Methods</p> <p>An iterative integral parameter identification method is applied to a second order respiratory mechanics model. The method is compared to the commonly used regression methods and error-mapping approaches using simulated and clinical data. The clinical potential of the method was evaluated on data from 13 Acute Respiratory Distress Syndrome (ARDS) patients.</p> <p>Results</p> <p>The iterative integral method converged to error minima 350 times faster than the Simplex Search Method using simulation data sets and 50 times faster using clinical data sets. Established regression methods reported erroneous results due to sensitivity to noise. In contrast, the iterative integral method was effective independent of initial parameter estimations, and converged successfully in each case tested.</p> <p>Conclusion</p> <p>These investigations reveal that the iterative integral method is beneficial with respect to computing time, operator independence and robustness, and thus applicable at the bedside for this clinical application.</p> |
| first_indexed | 2024-12-22T15:35:32Z |
| format | Article |
| id | doaj.art-348d535f16c64de0804f564a8449f9cc |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-12-22T15:35:32Z |
| publishDate | 2012-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-348d535f16c64de0804f564a8449f9cc2022-12-21T18:21:16ZengBMCBioMedical Engineering OnLine1475-925X2012-07-011113810.1186/1475-925X-11-38Iterative integral parameter identification of a respiratory mechanics modelSchranz ChristophDocherty Paul DChiew YeongMöller KnutChase J<p>Abstract</p> <p>Background</p> <p>Patient-specific respiratory mechanics models can support the evaluation of optimal lung protective ventilator settings during ventilation therapy. Clinical application requires that the individual’s model parameter values must be identified with information available at the bedside. Multiple linear regression or gradient-based parameter identification methods are highly sensitive to noise and initial parameter estimates. Thus, they are difficult to apply at the bedside to support therapeutic decisions.</p> <p>Methods</p> <p>An iterative integral parameter identification method is applied to a second order respiratory mechanics model. The method is compared to the commonly used regression methods and error-mapping approaches using simulated and clinical data. The clinical potential of the method was evaluated on data from 13 Acute Respiratory Distress Syndrome (ARDS) patients.</p> <p>Results</p> <p>The iterative integral method converged to error minima 350 times faster than the Simplex Search Method using simulation data sets and 50 times faster using clinical data sets. Established regression methods reported erroneous results due to sensitivity to noise. In contrast, the iterative integral method was effective independent of initial parameter estimations, and converged successfully in each case tested.</p> <p>Conclusion</p> <p>These investigations reveal that the iterative integral method is beneficial with respect to computing time, operator independence and robustness, and thus applicable at the bedside for this clinical application.</p>http://www.biomedical-engineering-online.com/content/11/1/38Parameter identificationRespiratory mechanicsViscoelastic modelGlobal minimumRobustness |
| spellingShingle | Schranz Christoph Docherty Paul D Chiew Yeong Möller Knut Chase J Iterative integral parameter identification of a respiratory mechanics model BioMedical Engineering OnLine Parameter identification Respiratory mechanics Viscoelastic model Global minimum Robustness |
| title | Iterative integral parameter identification of a respiratory mechanics model |
| title_full | Iterative integral parameter identification of a respiratory mechanics model |
| title_fullStr | Iterative integral parameter identification of a respiratory mechanics model |
| title_full_unstemmed | Iterative integral parameter identification of a respiratory mechanics model |
| title_short | Iterative integral parameter identification of a respiratory mechanics model |
| title_sort | iterative integral parameter identification of a respiratory mechanics model |
| topic | Parameter identification Respiratory mechanics Viscoelastic model Global minimum Robustness |
| url | http://www.biomedical-engineering-online.com/content/11/1/38 |
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