论文标题
在头皮脑电图中用于自动伪影检测的变压器卷积神经网络
Transformer Convolutional Neural Networks for Automated Artifact Detection in Scalp EEG
论文作者
论文摘要
众所周知，脑电图（EEG）通常由于肌肉活动，眼睛眨眼和其他各种原因而包含伪影。检测这种伪影是朝着正确解释脑电图的重要第一步。尽管已大量精力致力于在脑电图中进行半自动化和自动化的伪影检测，但伪像检测的问题仍然具有挑战性。在本文中，我们提出了一种通过使用信念匹配（BM）损失来增强的卷积神经网络（CNN），以自动检测五种类型的伪影：咀嚼，电极流行，眼动，肌肉和颤抖。具体而言，我们在各个脑电图通道上应用这五个检测器，以区分伪像和背景脑电图。接下来，对于这五种类型的工件中的每一种，我们结合了这些通道检测器的输出，以检测多渠道脑电图中的伪影。这些分段级分类器可以以0.947、0.735、0.826、0.857和0.655的均衡精度（BAC）检测特定的伪影，分别用于咀嚼，电极流行，眼动，肌肉，肌肉和shiver伪影。最后，我们将五个段级检测器的输出组合在一起，以执行组合的二进制分类（任何伪像与背景）。最终的检测器的敏感性（SEN）为60.4％，51.8％和35.5％，特异性（SPE）分别为95％，97％和99％。该伪影检测模块可以拒绝伪影段，而仅删除一小部分背景脑电图，从而导致更清洁的脑电图进行进一步分析。
It is well known that electroencephalograms (EEGs) often contain artifacts due to muscle activity, eye blinks, and various other causes. Detecting such artifacts is an essential first step toward a correct interpretation of EEGs. Although much effort has been devoted to semi-automated and automated artifact detection in EEG, the problem of artifact detection remains challenging. In this paper, we propose a convolutional neural network (CNN) enhanced by transformers using belief matching (BM) loss for automated detection of five types of artifacts: chewing, electrode pop, eye movement, muscle, and shiver. Specifically, we apply these five detectors at individual EEG channels to distinguish artifacts from background EEG. Next, for each of these five types of artifacts, we combine the output of these channel-wise detectors to detect artifacts in multi-channel EEG segments. These segment-level classifiers can detect specific artifacts with a balanced accuracy (BAC) of 0.947, 0.735, 0.826, 0.857, and 0.655 for chewing, electrode pop, eye movement, muscle, and shiver artifacts, respectively. Finally, we combine the outputs of the five segment-level detectors to perform a combined binary classification (any artifact vs. background). The resulting detector achieves a sensitivity (SEN) of 60.4%, 51.8%, and 35.5%, at a specificity (SPE) of 95%, 97%, and 99%, respectively. This artifact detection module can reject artifact segments while only removing a small fraction of the background EEG, leading to a cleaner EEG for further analysis.
