2022 Volume 37 Issue 2 Pages S23-S29
Spectral analysis methods are commonly used for the Electrogastrography. However, spectral analysis is not sufficient to evaluate an electrogastrogram, and a certain size of time sequences is required for the analysis. Therefore, it is difficult to analyze time-series data over a relatively short period of time. The purpose of this study was to apply an analysis method using deep learning to the electrogastrograms of healthy young subjects in the seated posture during meal loading and in the supine posture after meal loading. The results of the analysis of the electrograms embedded in a low-dimensional feature space using an autoencoder showed that it was possible to estimate the state of the stomach before and after a meal in a short period of time.
