Avilov Oleksii, Rimbert Sebastien, Popov Anton, Bougrain Laurent
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:142-145. doi: 10.1109/EMBC44109.2020.9176228.
Every year, millions of patients regain conscious- ness during surgery and can potentially suffer from post-traumatic disorders. We recently showed that the detection of motor activity during a median nerve stimulation from electroencephalographic (EEG) signals could be used to alert the medical staff that a patient is waking up and trying to move under general anesthesia [1], [2]. In this work, we measure the accuracy and false positive rate in detecting motor imagery of several deep learning models (EEGNet, deep convolutional network and shallow convolutional network) directly trained on filtered EEG data. We compare them with efficient non-deep approaches, namely, a linear discriminant analysis based on common spatial patterns, the minimum distance to Riemannian mean algorithm applied to covariance matrices, a logistic regression based on a tangent space projection of covariance matrices (TS+LR). The EEGNet improves significantly the classification performance comparing to other classifiers (p- value <; 0.01); moreover it outperforms the best non-deep classifier (TS+LR) for 7.2% of accuracy. This approach promises to improve intraoperative awareness detection during general anesthesia.
每年，数百万患者在手术过程中恢复意识，并可能患上创伤后疾病。我们最近表明，通过脑电图（EEG）信号检测正中神经刺激期间的运动活动，可用于提醒医务人员患者在全身麻醉下正在苏醒并试图移动[1,2]。在这项工作中，我们测量了直接在滤波后的EEG数据上训练的几种深度学习模型（EEGNet、深度卷积网络和浅卷积网络）在检测运动想象时的准确率和误报率。我们将它们与高效的非深度方法进行比较，即基于共同空间模式的线性判别分析、应用于协方差矩阵的到黎曼均值的最小距离算法、基于协方差矩阵切线空间投影的逻辑回归（TS+LR）。与其他分类器相比，EEGNet显著提高了分类性能（p值<0.01）；此外，它在准确率方面比最佳非深度分类器（TS+LR）高出7.2%。这种方法有望改善全身麻醉期间的术中意识检测。
