Combined method for artifact reduction in surface electroenterogram.

Area of Science:

• Biomedical Engineering
• Physiology
• Signal Processing

Background:

• Surface electroenterogram (EEnG) is a non-invasive technique for assessing intestinal motility.
• EEnG signals are frequently corrupted by artifacts, primarily from body movement, impacting data reliability.
• Accurate intestinal motility assessment is crucial for diagnosing and managing gastrointestinal disorders.

Purpose of the Study:

• To develop and validate a combined Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) method for removing movement artifacts from surface EEnG.
• To improve the accuracy and robustness of non-invasive intestinal motility indicators.
• To enhance the clinical utility of EEnG by reducing signal interference.

Main Methods:

• Surface EEnG signals were recorded from a canine model across four channels.
• Empirical Mode Decomposition (EMD) was applied to decompose the EEnG signals into intrinsic mode functions (IMFs).
• Independent Component Analysis (ICA) was utilized to separate artifactual components (e.g., movement) from the IMFs, followed by signal reconstruction.

Main Results:

• The combined EMD-ICA method effectively identified and removed movement-related artifacts from surface EEnG signals.
• Reconstructed EEnG signals exhibited reduced artifactual interference, leading to more stable intestinal motility indexes.
• The technique successfully eliminated irregular peaks in motility indicators caused by artifacts.

Conclusions:

• The proposed EMD-ICA approach provides an effective solution for mitigating movement artifacts in surface EEnG recordings.
• This method enhances the reliability of non-invasive intestinal motility monitoring.
• The technique holds promise for improving diagnostic accuracy and developing more robust clinical indicators for gastrointestinal motility.