Waser Markus, Garn Heinrich, Benke Thomas, Dal-Bianco Peter, Ransmayr Gerhard, Schmidt Reinhold, Jennum Poul J, Sorensen Helge B D
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:3793-3796. doi: 10.1109/EMBC.2017.8037683.
Electroencephalogram (EEG) signal quality is often compromised by artifacts that corrupt quantitative EEG measurements used in clinical applications and EEG-related studies. Techniques such as filtering, regression analysis and blind source separation are often used to remove these artifacts. However, these preprocessing steps do not allow for complete artifact correction. We propose a method for the automated offline-detection of remaining artifacts after preprocessing in multi-channel EEG recordings. In contrast to existing methods it requires neither adaptive parameters varying between recordings nor a topography template. It is suited for short EEG segments and is flexible with regard to target applications. The algorithm was developed and tested on 60 clinical EEG samples of 20 seconds each that were recorded both in resting state and during cognitive activation to gain a realistic artifact set. Five EEG features were used to quantify temporal and spatial signal variations. Two distance measures for the single-channel and multi-channel variations of these features were defined. The global thresholds were determined by three-fold cross-validation and Youden's J statistic in conjunction with receiver operating characteristics (ROC curves). We observed high sensitivity of 95.5%±4.8 and specificity of 88.8%±2.1. The method has thus shown great potential and is promising as a possible tool for both EEG-based clinical applications and EEG-related research.
脑电图（EEG）信号质量常常受到伪迹的影响，这些伪迹会破坏临床应用和与脑电图相关研究中所使用的定量脑电图测量。诸如滤波、回归分析和盲源分离等技术常被用于去除这些伪迹。然而，这些预处理步骤并不能实现对伪迹的完全校正。我们提出了一种用于自动离线检测多通道脑电图记录预处理后剩余伪迹的方法。与现有方法不同，它既不需要在不同记录之间变化的自适应参数，也不需要地形图模板。它适用于短脑电图片段，并且在目标应用方面具有灵活性。该算法是基于20个临床脑电图样本开发和测试的，每个样本时长20秒，这些样本在静息状态和认知激活期间均有记录，以获取一组真实的伪迹。使用了五个脑电图特征来量化时间和空间信号变化。定义了两种用于这些特征的单通道和多通道变化的距离度量。全局阈值通过三倍交叉验证以及约登指数（Youden's J statistic）结合接收者操作特征（ROC曲线）来确定。我们观察到其灵敏度高达95.5%±4.8，特异性为88.8%±2.1。因此，该方法已显示出巨大潜力，有望成为基于脑电图的临床应用和与脑电图相关研究的一种可能工具。
