Waser Markus, Garn Heinrich, Jennum Poul J, Sorensen Helge B D
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:6010-6013. doi: 10.1109/EMBC.2018.8513619.
Electroencephalogram (EEG) is a common tool in sleep medicine, but it is often compromised by non-neural artifacts. Excluding visually identified artifacts is time-consuming and removes relevant EEG information. Blind source separation (BSS) techniques, on the other hand, are capable of separating "brain" from "artifact source components". Existing algorithms for automated component labeling require either a priori morphological information or adaptation to individual recordings. We present a method for the automated identification of artifact components based on their autocorrelation and spectral properties. It requires no tuning for individual recordings. The method was tested on 100 one-minute EEG segments during rapid eye movement sleep. EEG source components were estimated by second order blind source identification and, as reference, manually labeled as "brain" or "artifact component". The algorithm identified electro-cardiogram components by autocorrelation peaks between 0.5-1.5 seconds and -oculogram components by linear discriminant analysis of spectral band-power. Using 5-fold cross-validation, we observed 97% accuracy (95% sensitivity, 98% specificity), as well as minimized correlation of artifacts and the EEG. The approach has demonstrated its potential as promising tool for a broad range of sleep medical applications.
脑电图（EEG）是睡眠医学中的常用工具，但它常常受到非神经伪迹的影响。排除肉眼识别的伪迹既耗时又会去除相关的脑电图信息。另一方面，盲源分离（BSS）技术能够将“大脑”与“伪迹源成分”分离。现有的自动成分标记算法要么需要先验形态学信息，要么需要针对个体记录进行调整。我们提出了一种基于自相关和频谱特性自动识别伪迹成分的方法。它无需针对个体记录进行调整。该方法在100个快速眼动睡眠期间的一分钟脑电图片段上进行了测试。通过二阶盲源识别估计脑电图源成分，并作为参考手动标记为“大脑”或“伪迹成分”。该算法通过0.5 - 1.5秒之间的自相关峰值识别心电图成分，并通过频谱带功率的线性判别分析识别眼电图成分。使用五折交叉验证，我们观察到准确率为97%（灵敏度为95%，特异性为98%），以及伪迹与脑电图之间的相关性最小化。该方法已证明其作为广泛睡眠医学应用中有前景的工具的潜力。
