Vectorgastrogram: dynamic trajectory and recurrence quantification analysis to assess slow wave vector movement in...

Area of Science:

• Gastroenterology
• Biomedical Engineering
• Signal Processing

Background:

• Functional gastric disorders present diagnostic challenges due to lack of structural abnormalities.
• Electrogastrography (EGG) is a potential diagnostic tool but faces interference issues.
• Accurate identification of gastric slow waves (SW) is crucial for diagnosis.

Purpose of the Study:

• To robustly identify gastric SW components using multivariate variational mode decomposition (MVMD) on multichannel EGG.
• To develop and analyze a 2D SW vectorgastrogram (VGG_SW) for assessing SW dynamics.
• To evaluate VGG_SW's potential in diagnosing gastric pathologies.

Main Methods:

• Application of MVMD to multichannel EGG data.
• Creation of a T-shaped 4-electrode VGG_SW.
• Analysis of VGG_SW dynamic trajectory and recurrence quantification analysis (RQA).

Main Results:

• MVMD achieved >91% detection rates for gastric SW with <5.3% frequency instability.
• Postprandial analysis showed increased SW amplitude, frequency, and vertical displacement in VGG_SW.
• RQA metrics indicated more intense, coordinated, and periodic SW activity after ingestion.

Conclusions:

• MVMD reliably identifies gastric SW, overcoming EGG interference.
• VGG_SW provides valuable spatiotemporal information on gastric SW propagation.
• VGG_SW is a promising, simple technique for diagnosing functional gastric disorders.