Data Augmentation for Generating Synthetic Electrogastrogram Time Series

Objective: To address an emerging need for large amount of diverse datasets for proper training of artificial intelligence (AI) algorithms and for rigor evaluation of signal processing techniques, we developed and evaluated a new method for generating synthetic electrogastrogram (EGG) time series. Methods: We used EGG data from an open database to set model parameters and statistical tests to evaluate synthesized data. Additionally, we illustrated method customization for generating artificial EGG alterations caused by the simulator sickness. Results: Proposed data augmentation method generates synthetic EGG with specified duration, sampling frequency, recording state (postprandial or fasting state), overall noise and breathing artifact injection, and pauses in the gastric rhythm (arrhythmia occurrence) with statistically significant difference between postprandial and fasting states in >70% cases while not accounting for individual differences. 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 signal processing algorithms or to increase diversity in datasets used to train AI algorithms. The proposed approach is customized for EGG data synthesis, but can be easily utilized for other biosignals with similar nature such as electroencephalogram.Comment: three figures and two table