The manual labeling of events in electroencephalography (EEG) records is time-consuming. This is especially true when EEG recordings are taken continuously over weeks to months. Therefore, a method to automatically label pertinent EEG events reduces the manual workload. Spike wave discharges (SWD), which are the electrographic hallmark of absence seizures, are EEG events that are often labeled manually. While some previous studies have utilized machine learning to automatically segment and classify EEG signals like SWDs, they can be improved. Here we compare the performance of 14 machine learning classifiers on our own manually annotated dataset of 961 hours of EEG recordings from C3H/HeJ mice, including 22,637 labeled SWDs. We find that a 1D UNet performs best for labeling SWDs in this dataset. We also improve the 1D UNet by augmenting our training data and determine that scaling showed the greatest benefit of all augmentation procedures applied. We then compare the 1D UNet with data augmentation, AugUNet1D, against a recently published time- and frequency-based algorithmic approach called "Twin Peaks". AugUNet1D showed superior performance and detected events with more similar features to the SWDs labeled manually. AugUNet1D, pretrained on our manually annotated data or untrained, is made public for others users.

翻译：脑电图记录中事件的手动标注耗时费力，尤其是在连续数周至数月的脑电图记录中更是如此。因此，一种自动标注相关脑电图事件的方法能够减轻人工工作量。棘波放电作为失神发作的脑电标志，是常需手动标注的脑电图事件。尽管先前已有研究利用机器学习对棘波放电等脑电信号进行自动分割与分类，但其性能仍有提升空间。本研究基于我们手动标注的数据集（包含C3H/HeJ小鼠961小时脑电图记录及22,637个标注棘波放电），比较了14种机器学习分类器的性能。结果表明，一维UNet在该数据集上对棘波放电的标注性能最优。我们通过增强训练数据改进了该模型，并发现尺度缩放在所有增强策略中效益最为显著。进一步将增强后的一维UNet（AugUNet1D）与近期发表的时频域算法"Twin Peaks"进行对比，AugUNet1D展现出更优性能，其检测事件与手动标注棘波放电的特征相似度更高。本研究将基于手动标注数据预训练及未训练的AugUNet1D模型公开供其他研究者使用。