A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component...

Area of Science:

• Biomedical Engineering
• Signal Processing
• Gastroenterology

Background:

• Electrogastrogram (EGG) signals are crucial for assessing gastric motility.
• EGG signals are often contaminated by various artifacts, hindering accurate analysis.
• Existing artifact removal methods have limitations in effectiveness and signal distortion.

Purpose of the Study:

• To develop and evaluate a novel artifact removal technique for multichannel EGG data.
• To combine Independent Component Analysis (ICA) and Ensemble Empirical Mode Decomposition (EEMD) for enhanced signal denoising.
• To improve the accuracy of gastric slow-wave frequency analysis in different physiological states.

Main Methods:

• Utilized a multichannel EGG system with 16 electrodes on the upper abdomen.
• Applied a combined ICA-EEMD methodology to denoise gastric signals.
• Analyzed instantaneous frequencies of intrinsic mode functions to identify and remove artifacts.
• Compared the ICA-EEMD method against ICA-EMD and conventional filtering techniques.

Main Results:

• The proposed ICA-EEMD method demonstrated superior artifact attenuation and lower signal distortion compared to ICA-EMD and conventional filters.
• Characteristic changes in gastric slow-wave frequencies were successfully identified across preprandial and postprandial states.
• The denoised signals enabled reliable determination of gastric condition-specific frequency alterations.

Conclusions:

• The ICA-EEMD technique offers a robust and effective solution for denoising EGG signals.
• This method holds significant potential for improving the clinical utility of EGG in diagnosing gastric disorders.
• The findings support the adoption of EEMD-based denoising for gastric signal analysis in clinical practice.