Empirical Mode Decomposition for Bias Reduction in Fractional Order Impedance Evaluation during Forced Oscillation Technique

Abstract

The Forced Oscillation Technique (FOT) is a non-invasive and computationally efficient method widely used in clinical practice for evaluating lung function through fractional order impedance. While FOT has been effective for assessing respiratory properties at high frequencies, challenges arise when measuring at low frequencies due to interference between imposed pressure oscillations and the subject's breathing signal. Existing filtering techniques have failed to successfully separate this disturbance signal, leading to biased correlates in impedance estimation. To address this issue, we investigate the potential of empirical mode decomposition techniques in eliminating the bias introduced by the breathing signal. We analyze respiratory data from patients diagnosed with chronic obstructive pulmonary disease (COPD) and demonstrate that by employing decomposed signals for estimating fractional order impedance, we can significantly reduce bias in respiratory impedance evaluation. Our preliminary results highlight the promising role of empirical mode decomposition in improving the accuracy of impedance estimation during the Forced Oscillation Technique.