Deep learning models have recently shown great success in classifying epileptic patients using EEG recordings. Unfortunately, classification-based methods lack a sound mechanism to detect the onset of seizure events. In this work, we propose a two-stage framework, SODor, that explicitly models seizure onset through a novel task formulation of subsequence clustering. Given an EEG sequence, the framework first learns a set of second-level embeddings with label supervision. It then employs model-based clustering to explicitly capture long-term temporal dependencies in EEG sequences and identify meaningful subsequences. Epochs within a subsequence share a common cluster assignment (normal or seizure), with cluster or state transitions representing successful onset detections. Extensive experiments on three datasets demonstrate that our method can correct misclassifications, achieving 5\%-11\% classification improvements over other baselines and accurately detecting seizure onsets.

翻译：深度学习模型近期在利用脑电图记录对癫痫患者进行分类方面取得了显著成功。遗憾的是，基于分类的方法缺乏可靠的机制来检测癫痫发作事件的起始。在本研究中，我们提出了一个两阶段框架SODor，该框架通过一种新颖的子序列聚类任务形式化来显式建模癫痫发作起始。给定一段脑电图序列，该框架首先在标签监督下学习一组秒级嵌入表示，随后采用基于模型的聚类方法，以显式捕捉脑电图序列中的长期时间依赖性并识别有意义的子序列。同一子序列内的时段共享共同的聚类分配（正常或发作），而聚类或状态间的转换即代表成功的起始检测。在三个数据集上的大量实验表明，我们的方法能够纠正误分类，相比其他基线模型实现了5%-11%的分类性能提升，并能准确检测癫痫发作起始。