Manual annotation of spike-wave discharges (SWDs), the electrographic hallmark of absence seizures, is labor-intensive for long-term electroencephalography (EEG) monitoring studies. While machine learning approaches show promise for automated detection, they often struggle with cross-subject generalization due to high inter-individual variability in seizure morphology and signal characteristics. In this study we compare the performance of 15 machine learning classifiers on our own manually annotated dataset of 961 hours of EEG recordings from C3H/HeJ mice, including 22,637 labeled SWDs and find that a 1D U-Net performs the best. We then improve its performance by employing residual connections and data augmentation strategies combining amplitude scaling, Gaussian noise injection, and signal inversion during training to enhance cross-subject generalization. We also compare our method, named AugUNet1D, to a recently published time- and frequency-based algorithmic approach called "Twin Peaks" and show that AugUNet1D performs better on our dataset. AugUNet1D, pretrained on our manually annotated data or untrained, is made public for other users.

翻译：棘慢波放电（SWDs）作为失神发作的脑电图标志，其人工标注在长期脑电图监测研究中极为耗时。尽管机器学习方法在自动检测方面展现出潜力，但由于发作形态与信号特征存在显著的个体间差异，这类方法在跨被试泛化方面往往面临挑战。本研究基于我们自行标注的数据集（包含C3H/HeJ小鼠961小时脑电记录及22,637个标记SWDs），比较了15种机器学习分类器的性能，发现一维U-Net表现最佳。随后，我们通过引入残差连接及结合幅度缩放、高斯噪声注入与信号反转的数据增强策略来提升其跨被试泛化能力。我们将该方法命名为AugUNet1D，并与近期发表的时频域算法"Twin Peaks"进行对比，结果表明AugUNet1D在我们的数据集上表现更优。我们将基于标注数据预训练及未训练的AugUNet1D模型公开，以供其他研究者使用。