用于生成合成胃电图时间序列的数据增强
Data Augmentation for Generating Synthetic Electrogastrogram Time Series
AI总结：
提出一种可配置的合成胃电图时间序列数据增强方法，能模拟餐后/禁食、噪声、呼吸伪影、心律失常及模拟器疾病特征，并提供可复用代码以评估信号处理和训练AI算法。
摘要：
为满足对大量多样化数据集以严格评估信号处理技术的新兴需求，我们开发并评估了一种生成合成胃电图时间序列的新方法。我们使用来自开放数据库的胃电图（EGG）数据来设置模型参数，并使用统计检验来评估合成数据。此外，我们展示了针对模拟器疾病引起的人工EGG时间序列变化的方法定制。所提出的数据增强方法可生成具有指定持续时间、采样频率、记录状态（餐后或禁食状态）、整体噪声和呼吸伪影注入，以及胃节律暂停（心律失常发生）的合成EGG数据；在不考虑个体差异的情况下，餐后与禁食状态之间在超过70%的情况下存在统计学显著差异。从类似模拟器疾病发生的合成EGG信号中获得的特征呈现出预期趋势。用于生成合成EGG时间序列的代码不仅可免费获取并可进一步定制以评估信号处理算法，还可用于增加训练人工智能（AI）算法的数据多样性。所提出的方法是为EGG数据合成定制的，但也可方便地用于其他具有相似性质的生物信号，例如脑电图。
英文摘要：
To address an emerging need for large number of diverse datasets for rigor evaluation of signal processing techniques, we developed and evaluated a new method for generating synthetic electrogastrogram time series. We used electrogastrography (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 time series alterations caused by the simulator sickness. Proposed data augmentation method generates synthetic EGG data 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. The code for generation of synthetic EGG time series is not only freely available and can be further customized to assess signal processing algorithms but also may be used to increase data diversity for training artificial intelligence (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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
