Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92607, USA.
Erik Jonsson School of Engineering and Computer Science, Dallas, TX 75201, USA.
Sensors (Basel). 2020 Apr 4;20(7):2027. doi: 10.3390/s20072027.
Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios.
由于创伤性脑损伤（TBI）的定量评估存在困难和复杂性，并且其在当今世界的相关性日益增加，因此对 TBI 的稳健检测变得比以往任何时候都更加重要。在这项工作中，我们研究了几种机器学习方法，以评估它们在分类创伤性脑损伤（TBI）的脑电图（EEG）数据中的性能。基于其在不同时间段的清醒阶段将轻度 TBI（mTBI）数据与对照组数据进行分类的性能，分析了决策树（DT）、随机森林（RF）、神经网络（NN）、支持向量机（SVM）、K 最近邻（KNN）和卷积神经网络（CNN）等算法。脑电图中的不同频带的平均功率和α：θ功率比被用作机器学习方法的输入特征。该小鼠模型的结果很有前景，表明类似的方法可能适用于在实际情况下检测人类 TBI。
