Accurate automated analysis of electroencephalography (EEG) would largely help clinicians effectively monitor and diagnose patients with various brain diseases. Compared to supervised learning with labelled disease EEG data which can train a model to analyze specific diseases but would fail to monitor previously unseen statuses, anomaly detection based on only normal EEGs can detect any potential anomaly in new EEGs. Different from existing anomaly detection strategies which do not consider any property of unavailable abnormal data during model development, a task-oriented self-supervised learning approach is proposed here which makes use of available normal EEGs and expert knowledge about abnormal EEGs to train a more effective feature extractor for the subsequent development of anomaly detector. In addition, a specific two branch convolutional neural network with larger kernels is designed as the feature extractor such that it can more easily extract both larger scale and small-scale features which often appear in unavailable abnormal EEGs. The effectively designed and trained feature extractor has shown to be able to extract better feature representations from EEGs for development of anomaly detector based on normal data and future anomaly detection for new EEGs, as demonstrated on three EEG datasets. The code is available at https://github.com/ironing/EEG-AD.
翻译:准确的自动电脑造影分析(EEG)将主要帮助临床医生有效监测和诊断各种脑疾病的病人。与监督地学习带有标签的疾病EEG数据相比,可以培训一种分析特定疾病的模型,但无法监测先前不见的状态,仅以正常的 EEG 为基础的异常探测可以探测新的EEG 中任何潜在的异常现象。与现有的异常探测战略不同,这些战略并不考虑模型开发期间无法找到的异常数据的任何属性。在此建议采用一种面向任务的自我监督学习方法,利用正常的EEGs和关于异常EEEGs的专家知识,为随后开发异常探测器培训更有效的特征提取器。此外,设计了一个具有较大内核的两部分部神经网络,作为特征提取器,这样可以更容易地提取大尺度和小规模特征,而这些特征通常出现在无法获取的异常 EEEGs。有效设计和培训的地物提取器显示,能够从EEGs提取更好的特征描述,用于根据正常数据开发异常探测器以及未来异常现象探测,用于新的 EEG/EGs 3 显示的EG/EGs。在EG/EGs 3上显示的AGSet/EGs。