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Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
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Data augmentation for generating synthetic electrogastrogram time series.

Nadica Miljković1,2, Nikola Milenić3, Nenad B Popović3

  • 1University of Belgrade-School of Electrical Engineering, Bulevar Kralja Aleksandra 73, 11000, Belgrade, Serbia. nadica.miljkovic@etf.bg.ac.rs.

Medical & Biological Engineering & Computing
|May 5, 2024
PubMed
Summary
This summary is machine-generated.

Researchers developed a novel method to generate synthetic electrogastrogram (EGG) time series data. This data augmentation technique enhances signal processing algorithm evaluation and artificial intelligence (AI) model training.

Keywords:
ElectrogastrographyGastric rhythmMotion sicknessPower spectral densitySynthetic data

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Computational Biology

Background:

  • Emerging need for diverse datasets in signal processing.
  • Electrogastrography (EGG) data is crucial for understanding gastric dynamics.
  • Current datasets may lack diversity for robust algorithm evaluation.

Purpose of the Study:

  • Develop and evaluate a new method for synthetic electrogastrogram (EGG) time series generation.
  • Provide a customizable tool for signal processing algorithm assessment.
  • Enhance data diversity for training artificial intelligence (AI) algorithms.

Main Methods:

  • Utilized EGG data from an open database to set model parameters.
  • Employed statistical tests to evaluate the synthesized data.
  • Customized the method for simulating simulator sickness effects and arrhythmias.

Main Results:

  • Generated synthetic EGG data with controllable parameters (duration, sampling frequency, state, noise, artifacts, arrhythmia).
  • Achieved statistically significant differences between postprandial and fasting states in over 70% of cases.
  • Synthetic EGG signals mimicking simulator sickness showed expected trends.

Conclusions:

  • The proposed data augmentation method effectively generates diverse and realistic synthetic EGG data.
  • Freely available code allows customization for signal processing and AI algorithm development.
  • The approach is adaptable for other biosignals like electroencephalogram (EEG).