通过子序列聚类精准定位癫痫发作起始时刻，提升检测准确率。
Long-Term EEG Partitioning for Seizure Onset Detection
- 将脑电图序列分段聚类，识别正常与发作状态的转变。
- 在三个数据集上比基线模型分类准确率提升5%~11%。
- 适合癫痫监测、临床辅助诊断等需要精确起始时间的场景。
深度学习模型在利用脑电图（EEG）分类癫痫患者方面已取得显著进展。然而，基于分类的方法缺乏可靠的发作起始检测机制。本文提出两阶段框架SODor，通过新颖的子序列聚类任务建模发作起始。给定一段EEG序列，该框架首先在标签监督下学习二级嵌入表示；随后采用基于模型的聚类方法，显式捕捉长期时间依赖性并识别有意义的子序列。子序列内的时程共享同一聚类标签（正常或发作），聚类状态的转换即代表成功检测到发作起始。在三个数据集上的大量实验表明，该方法可纠正误分类，相比其他基线模型分类准确率提升5%~11%，并能准确检测发作起始。
原文摘要 · Abstract (English)
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.
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