Wavelet analysis of scaling properties of gastric electrical activity

Fractal nature of human gastrointestinal system: Exploring a new era

The morphological complexity of cells and tissues, whether normal or pathological, is characterized by two primary attributes: Irregularity and self-similarity across different scales. When an object exhibits self-similarity, its shape remains unchanged as the scales of measurement vary because any part of it resembles the whole. On the other hand, the size and geometric characteristics of an irregular object vary as the resolution increases, revealing more intricate details. Despite numerous attempts, a reliable and accurate method for quantifying the morphological features of gastrointestinal organs, tissues, cells, their dynamic changes, and pathological disorders has not yet been established. However, fractal geometry, which studies shapes and patterns that exhibit self-similarity, holds promise in providing a quantitative measure of the irregularly shaped morphologies and their underlying self-similar temporal behaviors. In this context, we explore the fractal nature of the gastrointestinal system and the potential of fractal geometry as a robust descriptor of its complex forms and functions. Additionally, we examine the practical applications of fractal geometry in clinical gastroenterology and hepatology practice.

Analysis of gastric myoelectrical activity from the electrogastrogram signals based on wavelet transform and line length feature

Electrogastrogram is used for the abdominal surface measurement of the gastric electrical activity of the human stomach. The electrogastrogram technique has significant value as a clinical tool because careful electrogastrogram signal recordings and analyses play a major role in determining the propagation and coordination of gastric myoelectric abnormalities. The aim of this article is to evaluate electrogastrogram features calculated by line length features based on the discrete wavelet transform method to differentiate healthy control subjects from patients with functional dyspepsia and diabetic gastroparesis. For this analysis, the discrete wavelet transform method was used to extract electrogastrogram signal characteristics. Next, line length features were calculated for each sub-signal, which reflect the waveform dimensionality variations and represent a measure of sensitivity to differences in signal amplitude and frequency. The analysis was carried out using a statistical analysis of variance test. The results obtained from the line length analysis of the electrogastrogram signal prove that there are significant differences among the functional dyspepsia, diabetic gastroparesis, and control groups. The electrogastrogram signals of the control subjects had a significantly higher line length than those of the functional dyspepsia and diabetic gastroparesis patients. In conclusion, this article provides new methods with increased accuracy obtained from electrogastrogram signal analysis. The electrogastrography is an effective and non-stationary method to differentiate diabetic gastroparesis and functional dyspepsia patients from the control group. The proposed method can be considered a key test and an essential computer-aided diagnostic tool for detecting gastric myoelectric abnormalities in diabetic gastroparesis and functional dyspepsia patients.