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.

中文翻译：

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通过小鼠脑电图评估对创伤性脑损伤分类的机器学习方法的研究。

由于创伤性脑损伤 (TBI) 定量评估的困难和并发症及其在当今世界的日益相关性，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，以评估它们在小鼠模型中对 TBI 脑电图 (EEG) 数据进行分类的性能。对决策树 (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) 等算法的分类性能进行了分析来自对照组在不同时期长度的唤醒阶段的轻度 TBI (mTBI) 数据。不同频率子带的平均功率和 EEG 中的 alpha:theta 功率比被用作机器学习方法的输入特征。