Windowing is a common technique in EEG machine learning classification and other time series tasks. However, a challenge arises when employing this technique: computational expense inhibits learning global relationships across an entire recording or set of recordings. Furthermore, the labels inherited by windows from their parent recordings may not accurately reflect the content of that window in isolation. To resolve these issues, we introduce a multi-stage model architecture, incorporating meta-learning principles tailored to time-windowed data aggregation. We further tested two distinct strategies to alleviate these issues: lengthening the window and utilizing overlapping to augment data. Our methods, when tested on the Temple University Hospital Abnormal EEG Corpus (TUAB), dramatically boosted the benchmark accuracy from 89.8 percent to 99.0 percent. This breakthrough performance surpasses prior performance projections for this dataset and paves the way for clinical applications of machine learning solutions to EEG interpretation challenges. On a broader and more varied dataset from the Temple University Hospital EEG Corpus (TUEG), we attained an accuracy of 86.7%, nearing the assumed performance ceiling set by variable inter-rater agreement on such datasets.

通过引入多阶段模型架构和元学习原理，结合窗体化的数据聚合方法，我们成功解决了EEG机器学习分类和其他时间序列任务中遇到的窗体计算困难、数据标签不准确等问题，并在Temple大学医院异常EEG数据库（TUAB）上将基准准确率从89.8%大幅提升至99.0%，为机器学习在EEG解读挑战中的临床应用提供了突破性的解决方案。在Temple大学医院EEG数据库（TUEG）上，我们实现了86.7%的准确率，接近变量评价者一致性设定的数据性能上限。

窗口叠加元模型用于临床脑电分类