Reducing bias in fractional order impedance estimation for lung function evaluation. (January 2018)

• Record Type: Journal Article
• Authors: Copot, Dana; De Keyser, Robin; Derom, Eric; Ortigueira, Manuel; Ionescu, Clara M.
• Is Part Of: Biomedical signal processing and control. Volume 39(2018)
• Page Start: 74
• Page End: 80
• Publication Date: 2018-01
• Subjects: Fractional order impedance -- Respiratory function -- Empirical mode decomposition -- Lung function test -- Filtering -- Parametric model -- Signal processing -- Forced oscillation technique -- Biomedical signal processing; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.07.009
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Respiratory impedance measurements at low frequencies. The use of EMD to reduce bias due to interference between breathing signal excitation signal. EMD proved to be a suitable method to overcome the problem bias. 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.