论文标题
在脑电图图中进行异常通道检测的自学学习：应用于癫痫发作分析
Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis
论文作者
论文摘要
脑电图（EEG）信号是用于癫痫发作分析的有效工具，其中最重要的挑战之一是对癫痫发作或发起的癫痫发作事件和大脑区域的准确检测。但是，所有现有基于机器的基于机器的癫痫发作分析算法都需要访问标记的癫痫发作数据，同时获取标记的数据是非常强大，昂贵的，并且鉴于依赖于脑电图信号的视觉定性解释的主观性质。在本文中，我们建议以自我监督的方式检测癫痫发道和剪辑，在这些方式中不需要访问癫痫发作数据。该提出的方法考虑了通过使用正面和负面的子图，考虑了脑图中嵌入的局部结构和上下文信息。我们通过最大程度地减少对比度和生成性损失来训练我们的方法。由于诸如头骨骨折之类的并发症，访问所有脑电图通道时，不可能使用本地脑电图子图使该算法成为适当的选择。我们对最大的癫痫发作数据集进行了一系列广泛的实验，并证明我们提出的框架在基于EEG的癫痫发作研究中优于最先进的方法。所提出的方法是唯一需要在其训练阶段访问癫痫发作数据的研究，但在该领域建立了新的最新技术，并且胜过所有相关的监督方法。
Electroencephalogram (EEG) signals are effective tools towards seizure analysis where one of the most important challenges is accurate detection of seizure events and brain regions in which seizure happens or initiates. However, all existing machine learning-based algorithms for seizure analysis require access to the labeled seizure data while acquiring labeled data is very labor intensive, expensive, as well as clinicians dependent given the subjective nature of the visual qualitative interpretation of EEG signals. In this paper, we propose to detect seizure channels and clips in a self-supervised manner where no access to the seizure data is needed. The proposed method considers local structural and contextual information embedded in EEG graphs by employing positive and negative sub-graphs. We train our method through minimizing contrastive and generative losses. The employ of local EEG sub-graphs makes the algorithm an appropriate choice when accessing to the all EEG channels is impossible due to complications such as skull fractures. We conduct an extensive set of experiments on the largest seizure dataset and demonstrate that our proposed framework outperforms the state-of-the-art methods in the EEG-based seizure study. The proposed method is the only study that requires no access to the seizure data in its training phase, yet establishes a new state-of-the-art to the field, and outperforms all related supervised methods.
