Data-driven Method for Generating Synthetic Electrogastrogram Time Series

Objective: A new method for generating realistic electrogastrogram (EGG) time series is presented and evaluated. Methods: We used EGG data from an existing open database to set model parameters and Monte Carlo simulation to evaluate a new model based on the hypothesis that EGG dominant frequency should be statistically significantly different between fasting and postprandial states. Additionally, we illustrated method customization for generating artificial EGG alterations caused by the simulator sickness. Results: The user can specify the following input parameters of developed data-driven model: (1) duration of the generated sequence, (2) sampling frequency, (3) recording state (postprandial or fasting state), (3) breathing artifact contamination, (4) a flag whether the output would produce plots, (5) seed for the sake of reproducibility, (6) pauses in the gastric rhythm (arrhythmia occurrence), and (7) overall noise contamination to produce proper variability in EGG signals. The simulated EGG provided expected results of Monte Carlo simulation while features obtained from the synthetic EGG signal resembling simulator sickness occurrence displayed expected trends. Conclusion: The code for generation of synthetic EGG time series is freely available and can be further customized to assess robustness of the signal processing algorithms to noises and especially to movement artifacts, as well as to simulate alterations of gastric electrical activity. Significance: The proposed approach is customized for EGG data synthesis, but it can be further utilized to other biosignals with similar nature such as electroencephalogram.