Ionescu Clara M, De Keyser Robin
Department of Electrical Energy, Systems and Automation, Ghent University, Gent 9000, Belgium.
IEEE Trans Biomed Eng. 2009 Apr;56(4):978-87. doi: 10.1109/TBME.2008.2004966. Epub 2008 Nov 7.
In this study, changes in respiratory mechanics from healthy and chronic obstructive pulmonary disease (COPD) diagnosed patients are observed from identified fractional-order (FO) model parameters. The noninvasive forced oscillation technique is employed for lung function testing. Parameters on tissue damping and elastance are analyzed with respect to lung pathology and additional indexes developed from the identified model. The observations show that the proposed model may be used to detect changes in respiratory mechanics and offers a clear-cut separation between the healthy and COPD subject groups. Our conclusion is that an FO model is able to capture changes in viscoelasticity of the soft tissue in lungs with disease. Apart from this, nonlinear effects present in the measured signals were observed and analyzed via signal processing techniques and led to supporting evidence in relation to the expected phenomena from lung pathology in healthy and COPD patients.
在本研究中,从确定的分数阶(FO)模型参数观察健康人和慢性阻塞性肺疾病(COPD)确诊患者呼吸力学的变化。采用无创强迫振荡技术进行肺功能测试。针对肺病理学以及从识别模型得出的其他指标,分析组织阻尼和弹性的参数。观察结果表明,所提出的模型可用于检测呼吸力学的变化,并在健康受试者组和COPD受试者组之间提供明确区分。我们的结论是,分数阶模型能够捕捉患病肺部软组织粘弹性的变化。除此之外,通过信号处理技术观察和分析了测量信号中存在的非线性效应,并为健康人和COPD患者肺部病理学预期现象提供了支持证据。