Anastasiadou Maria N, Christodoulakis Manolis, Papathanasiou Eleftherios S, Papacostas Savvas S, Mitsis Georgios D
McGill University, Department of Bioengineering, 817 Sherbrooke St. W., Macdonald Engineering Building, Room 270, Montreal, QC H3A 0C3, Canada.
McGill University, Department of Bioengineering, 817 Sherbrooke St. W., Macdonald Engineering Building, Room 270, Montreal, QC H3A 0C3, Canada.
Clin Neurophysiol. 2017 Sep;128(9):1755-1769. doi: 10.1016/j.clinph.2017.06.247. Epub 2017 Jul 8.
This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF).
The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). The proposed algorithms are evaluated using realistic simulation data as well as 30min epochs of non-invasive EEG recordings obtained from ten patients with epilepsy.
We assessed the performance of the proposed algorithms using classification performance and goodness-of-fit values for noisy and noise-free signal windows. In the simulation study, where the ground truth was known, the proposed algorithms yielded almost perfect performance. In the case of experimental data, where expert marking was performed, the results suggest that both the supervised and unsupervised algorithm versions were able to remove artifacts without affecting noise-free channels considerably, outperforming standard CCA, independent component analysis (ICA) and Lagged Auto-Mutual Information Clustering (LAMIC).
The proposed algorithms achieved excellent performance for both simulation and experimental data. Importantly, for the first time to our knowledge, we were able to perform entirely unsupervised artifact removal, i.e. without using already marked noisy data segments, achieving performance that is comparable to the supervised case.
Overall, the results suggest that the proposed algorithms yield significant future potential for improving EEG signal quality in research or clinical settings without the need for marking by expert neurophysiologists, EMG signal recording and user visual inspection.
本文提出了用于从长期脑电图记录中自动检测和去除肌肉伪迹的监督和无监督算法，该算法将典型相关分析（CCA）、小波与随机森林（RF）相结合。
所提出的算法首先对典型成分进行CCA和连续小波变换，以生成一些特征，包括成分自相关值和小波系数幅值。随后使用随机森林和标记观测值（监督情况）或从原始观测值构建的合成数据（无监督情况）选择最重要特征的一个子集。使用逼真的模拟数据以及从10名癫痫患者获得的30分钟无创脑电图记录片段对所提出的算法进行评估。
我们使用噪声和无噪声信号窗口的分类性能和拟合优度值来评估所提出算法的性能。在已知真实情况的模拟研究中，所提出的算法产生了几乎完美的性能。在进行专家标记的实验数据情况下，结果表明监督和无监督算法版本都能够去除伪迹，而不会对无噪声通道产生太大影响，优于标准CCA、独立成分分析（ICA）和滞后自互信息聚类（LAMIC）。
所提出的算法在模拟和实验数据方面均取得了优异的性能。重要的是，据我们所知，我们首次能够完全无监督地去除伪迹，即无需使用已标记的噪声数据段，实现了与监督情况相当的性能。
总体而言，结果表明所提出的算法在无需专家神经生理学家标记、肌电信号记录和用户目视检查的情况下，在改善研究或临床环境中的脑电图信号质量方面具有巨大的未来潜力。
