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Reducing bias in fractional order impedance estimation for lung function evaluation

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Abstract
Forced oscillation technique (FOT) emerged as a non-invasive, computationally efficient, fast and reliable method used in clinical practice for lung evaluation by means of fractional order impedance. Only recently, FOT has been employed to assess respiratory properties at low frequencies. When measuring at low frequencies interference between the imposed pressure oscillations and the breathing signal of the subject occurs. To deal with these challenges filtering techniques have been proposed to avoid biased correlates in the impedance, but none proved to successfully separate this disturbance signal. Hence, in this paper we are investigating the usefulness of empirical mode decomposition techniques to eliminate the bias introduced by the breathing signal. Respiratory data from patients diagnosed with chronic obstructive pulmonary disease (COPD) were analyzed and the results indicate that the method can successfully fill the gap in reducing the bias in the estimated impedance. The preliminary results show that by using the decomposed signals to estimate the fractional order impedance a bias reduction of respiratory impedance evaluation can be achieved. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords
Fractional order impedance, Respiratory function, Empirical mode decomposition, Lung function test, Filtering, Parametric model, Signal processing, Forced oscillation technique, Biomedical signal processing

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Chicago
Copot, Dana, Robain De Keyser, Eric Derom, Manuel Ortigueira, and Clara-Mihaela Ionescu. 2018. “Reducing Bias in Fractional Order Impedance Estimation for Lung Function Evaluation.” Biomedical Signal Processing and Control 39: 74–80.
APA
Copot, D., De Keyser, R., Derom, E., Ortigueira, M., & Ionescu, C.-M. (2018). Reducing bias in fractional order impedance estimation for lung function evaluation. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 39, 74–80.
Vancouver
1.
Copot D, De Keyser R, Derom E, Ortigueira M, Ionescu C-M. Reducing bias in fractional order impedance estimation for lung function evaluation. BIOMEDICAL SIGNAL PROCESSING AND CONTROL. 2018;39:74–80.
MLA
Copot, Dana, Robain De Keyser, Eric Derom, et al. “Reducing Bias in Fractional Order Impedance Estimation for Lung Function Evaluation.” BIOMEDICAL SIGNAL PROCESSING AND CONTROL 39 (2018): 74–80. Print.
@article{8535333,
  abstract     = {Forced oscillation technique (FOT) emerged as a non-invasive, computationally efficient, fast and reliable method used in clinical practice for lung evaluation by means of fractional order impedance. Only recently, FOT has been employed to assess respiratory properties at low frequencies. When measuring at low frequencies interference between the imposed pressure oscillations and the breathing signal of the subject occurs. To deal with these challenges filtering techniques have been proposed to avoid biased correlates in the impedance, but none proved to successfully separate this disturbance signal. Hence, in this paper we are investigating the usefulness of empirical mode decomposition techniques to eliminate the bias introduced by the breathing signal. Respiratory data from patients diagnosed with chronic obstructive pulmonary disease (COPD) were analyzed and the results indicate that the method can successfully fill the gap in reducing the bias in the estimated impedance. The preliminary results show that by using the decomposed signals to estimate the fractional order impedance a bias reduction of respiratory impedance evaluation can be achieved. (C) 2017 Elsevier Ltd. All rights reserved.},
  author       = {Copot, Dana and De Keyser, Robain and Derom, Eric and Ortigueira, Manuel and Ionescu, Clara-Mihaela},
  issn         = {1746-8094},
  journal      = {BIOMEDICAL SIGNAL PROCESSING AND CONTROL},
  keyword      = {Fractional order impedance,Respiratory function,Empirical mode decomposition,Lung function test,Filtering,Parametric model,Signal processing,Forced oscillation technique,Biomedical signal processing},
  language     = {eng},
  pages        = {74--80},
  title        = {Reducing bias in fractional order impedance estimation for lung function evaluation},
  url          = {http://dx.doi.org/10.1016/j.bspc.2017.07.009},
  volume       = {39},
  year         = {2018},
}

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