Cardiogenic oscillations extraction in inductive plethysmography: Ensemble empirical mode decomposition

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiovascular Physiology

Background:

• Inductive plethysmography is a non-invasive method for monitoring respiratory and cardiac activity.
• Accurate measurement of cardiac stroke volume is crucial for assessing cardiovascular health.
• Empirical Mode Decomposition (EMD) has been explored for signal analysis but suffers from mode mixing.

Purpose of the Study:

• To evaluate the efficacy of Ensemble Empirical Mode Decomposition (EEMD) in extracting cardiogenic oscillations.
• To assess the potential of EEMD for accurate cardiac stroke volume measurement using inductive plethysmography.
• To address the mode mixing problem inherent in traditional EMD methods.

Main Methods:

• A cardio-respiratory model was developed to simulate cardiac, respiratory, and combined signals.
• Empirical Mode Decomposition (EMD) was applied to simulated signals to identify limitations.
• Ensemble Empirical Mode Decomposition (EEMD) was then applied to assess its performance in signal extraction and stroke volume measurement.

Main Results:

• Simulated signals demonstrated that mode mixing in EMD significantly impacts cardiogenic oscillation extraction and stroke volume accuracy.
• EEMD successfully mitigated the mode mixing phenomenon observed with EMD.
• The amplitude of extracted cardiogenic oscillations using EEMD correlated well with simulated stroke volume.

Conclusions:

• EEMD is a robust technique for extracting cardiogenic oscillations from inductive plethysmography signals.
• EEMD offers improved accuracy in cardiac stroke volume measurement compared to standard EMD.
• This method holds promise for enhanced non-invasive cardiovascular monitoring.