Anastasiadou Maria, Christodoulakis Manolis, Papathanasiou Eleftherios S, Papacostas Savvas S, Mitsis Georgios D
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:1946-50. doi: 10.1109/EMBC.2015.7318765.
Automatic detection and removal of muscle artifacts plays an important role in long-term scalp electroencephalography (EEG) monitoring, and muscle artifact detection algorithms have been intensively investigated. This paper proposes an algorithm for automatic muscle artifacts detection and removal using canonical correlation analysis (CCA) and wavelet transform (WT) in epochs from long-term EEG recordings. The proposed method first performs CCA analysis and then conducts wavelet decomposition on the canonical components within a specific frequency range and selects a subset of the wavelet coefficients for subsequent processing. A set of features, including the mean of wavelet coefficients and the canonical component autocorrelation values, are extracted from the above analysis and subsequently used as input in a random forest (RF) classifier. The RF classifier produces a similarity measure between observations and selects a subset of the most important features by comparing the original data with a set of synthetic data that is constructed based on the latter. The RF predictor output is finally used in combination with unsupervised clustering algorithms to discriminate between contaminated and non-contaminated EEG epochs. The proposed method is evaluated in epochs of 30 min from scalp EEG recordings obtained from three patients with epilepsy and yields a sensitivity of 71% and 80%, as well as a specificity of 81% and 85% for k-means and spectral clustering, respectively.
在长期头皮脑电图（EEG）监测中，自动检测和去除肌肉伪迹起着重要作用，并且对肌肉伪迹检测算法已经进行了深入研究。本文提出了一种在长期EEG记录的时间段中使用典型相关分析（CCA）和小波变换（WT）进行自动肌肉伪迹检测和去除的算法。所提出的方法首先进行CCA分析，然后对特定频率范围内的典型成分进行小波分解，并选择小波系数的一个子集用于后续处理。从上述分析中提取一组特征，包括小波系数的均值和典型成分自相关值，随后将其用作随机森林（RF）分类器的输入。RF分类器产生观测值之间的相似性度量，并通过将原始数据与基于后者构建的一组合成数据进行比较来选择最重要特征的一个子集。RF预测器的输出最终与无监督聚类算法结合使用，以区分受污染和未受污染的EEG时间段。在所提出的方法在从三名癫痫患者获得的头皮EEG记录的30分钟时间段中进行评估，对于k均值聚类和谱聚类，分别产生71%和80%的灵敏度以及81%和85%的特异性。
