标题： 基于自监督蒸馏的遗留规则方法在增强基于EEG的决策中的应用 显示英文标题

标题： Self-Supervised Distillation of Legacy Rule-Based Methods for Enhanced EEG-Based Decision-Making

摘要： 高频振荡（HFOs）在颅内脑电图（iEEG）中是定位癫痫治疗中致痫区的关键生物标志物。然而，传统的基于规则的HFO检测器精度不令人满意，会产生需要耗时人工审查的假阳性结果。监督机器学习方法已被用于分类检测结果，但它们通常依赖于标记的数据集，由于需要专业技能，获取这些数据集很困难。此外，由于不同机构之间评分者间可靠性低和注释实践不一致，准确标记HFOs具有挑战性。对什么是病理HFO缺乏明确共识进一步挑战了监督精炼方法。为了解决这个问题，我们利用了一个见解，即尽管传统检测器的假阳性率相对较高，但它们能够可靠地捕捉临床相关的信号。因此，我们提出了自监督到标签发现（SS2LD）框架，将传统检测器生成的大量候选事件精炼为精确的病理HFOs集合。SS2LD使用变分自动编码器（VAE）进行形态预训练，以学习检测事件的有意义潜在表示。这些表示被聚类以推导出病理事件的弱监督。然后，分类器使用这种监督来精炼检测边界，训练数据包括真实数据和VAE增强数据。在大型多机构间期iEEG数据集上评估，SS2LD优于最先进的方法。SS2LD提供了一种可扩展、标签高效且临床上有效的策略，利用传统检测器识别病理HFOs。 显示英文摘要

摘要： High-frequency oscillations (HFOs) in intracranial Electroencephalography (iEEG) are critical biomarkers for localizing the epileptogenic zone in epilepsy treatment. However, traditional rule-based detectors for HFOs suffer from unsatisfactory precision, producing false positives that require time-consuming manual review. Supervised machine learning approaches have been used to classify the detection results, yet they typically depend on labeled datasets, which are difficult to acquire due to the need for specialized expertise. Moreover, accurate labeling of HFOs is challenging due to low inter-rater reliability and inconsistent annotation practices across institutions. The lack of a clear consensus on what constitutes a pathological HFO further challenges supervised refinement approaches. To address this, we leverage the insight that legacy detectors reliably capture clinically relevant signals despite their relatively high false positive rates. We thus propose the Self-Supervised to Label Discovery (SS2LD) framework to refine the large set of candidate events generated by legacy detectors into a precise set of pathological HFOs. SS2LD employs a variational autoencoder (VAE) for morphological pre-training to learn meaningful latent representation of the detected events. These representations are clustered to derive weak supervision for pathological events. A classifier then uses this supervision to refine detection boundaries, trained on real and VAE-augmented data. Evaluated on large multi-institutional interictal iEEG datasets, SS2LD outperforms state-of-the-art methods. SS2LD offers a scalable, label-efficient, and clinically effective strategy to identify pathological HFOs using legacy detectors.