论文标题
EEG-GCNN：使用域引导的图形卷积神经网络增强基于脑电图的神经疾病诊断
EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network
论文作者
论文摘要
本文提出了一种基于新型的图形卷积神经网络（GCNN）的方法，用于改善使用头皮电脑图（EEGS）改善神经系统疾病的诊断。尽管脑电图是用于神经疾病诊断的主要测试之一，但基于脑电图的专家视觉诊断的敏感性仍为$ \ sim $ 50 \％。这表明在检测异常头皮eegs时，清楚地需要先进的方法来降低假阴性率。在这种情况下，我们着重于区分神经系统疾病患者头皮异常的脑电图的问题，这些患者最初被专家与健康个体的头皮脑电图归类为“正常”。 The contributions of this paper are three-fold: 1) we present EEG-GCNN, a novel GCNN model for EEG data that captures both the spatial and functional connectivity between the scalp electrodes, 2) using EEG-GCNN, we perform the first large-scale evaluation of the aforementioned hypothesis, and 3) using two large scalp-EEG databases, we demonstrate that EEG-GCNN significantly outperforms the human基线和经典机器学习（ML）基线，AUC为0.90。
This paper presents a novel graph convolutional neural network (GCNN)-based approach for improving the diagnosis of neurological diseases using scalp-electroencephalograms (EEGs). Although EEG is one of the main tests used for neurological-disease diagnosis, the sensitivity of EEG-based expert visual diagnosis remains at $\sim$50\%. This indicates a clear need for advanced methodology to reduce the false negative rate in detecting abnormal scalp-EEGs. In that context, we focus on the problem of distinguishing the abnormal scalp EEGs of patients with neurological diseases, which were originally classified as 'normal' by experts, from the scalp EEGs of healthy individuals. The contributions of this paper are three-fold: 1) we present EEG-GCNN, a novel GCNN model for EEG data that captures both the spatial and functional connectivity between the scalp electrodes, 2) using EEG-GCNN, we perform the first large-scale evaluation of the aforementioned hypothesis, and 3) using two large scalp-EEG databases, we demonstrate that EEG-GCNN significantly outperforms the human baseline and classical machine learning (ML) baselines, with an AUC of 0.90.
