Instantaneous Respiratory Estimation from Thoracic Impedance by Empirical Mode Decomposition

Area of Science:

• Biomedical Engineering
• Physiological Monitoring
• Signal Processing

Background:

• Impedance plethysmography (IP) measures respiration by detecting thoracic impedance changes.
• Empirical Mode Decomposition (EMD) can extract respiratory signals but is sensitive to motion artifacts.
• Existing methods struggle to remove motion artifacts effectively, hindering accurate respiratory analysis.

Purpose of the Study:

• To develop a robust method for motion artifact detection and removal in IP signals.
• To propose a novel index for identifying respiration-related intrinsic mode functions (IMFs).
• To improve the accuracy of instantaneous respiratory rate estimation from IP signals.

Main Methods:

• Motion artifacts are detected and replaced using mirrored data from adjacent signal segments.
• A novel intrinsic respiratory reconstruction index is introduced to select relevant IMFs.
• Empirical Mode Decomposition (EMD) is applied to preprocessed IP signals.

Main Results:

• The proposed method effectively handles motion artifacts, enabling EMD to decompose respiratory components.
• The novel index accurately identifies respiration-related IMFs for signal reconstruction.
• Higher cross-correlations were observed between estimated and reference respiratory frequencies compared to Fourier transform methods, particularly with small window sizes.

Conclusions:

• The developed technique offers improved accuracy and robustness for respiratory monitoring using IP, even during physical activity.
• This method enhances the utility of IP for long-term and ambulatory respiratory monitoring.
• The proposed approach provides a reliable way to estimate instantaneous respiratory rates from thoracic impedance data.