Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of computed tomography (CT) or magnetic resonance imaging (MRI) is required to determine the severity of a moderate TBI patient. However, due to the rising number of TBI patients these days, employing the CT scan or MRI scan to every potential patient is not only expensive, but also time consuming. Therefore, in this paper, we investigate the possibility of using electroencephalography (EEG) with computational intelligence as an alternative approach to detect the severity of moderate TBI patients. EEG procedure is much cheaper than CT or MRI. Although EEG does not have high spatial resolutions as compared with CT and MRI, it has high temporal resolutions. The analysis and prediction of moderate TBI from EEG using conventional computational intelligence approaches are tedious as they normally involve complex preprocessing, feature extraction, or feature selection of the signal. Thus, we propose an approach that uses convolutional neural network (CNN) to automatically classify healthy subjects and moderate TBI patients. The input to this computational intelligence system is the resting-state eye-closed EEG, without undergoing preprocessing and feature selection. The EEG dataset used includes 15 healthy volunteers and 15 moderate TBI patients, which is acquired at the Hospital Universiti Sains Malaysia, Kelantan, Malaysia. The performance of the proposed method has been compared with four other existing methods. With the average classification accuracy of 72.46%, the proposed method outperforms the other four methods. This result indicates that the proposed method has the potential to be used as a preliminary screening for moderate TBI, for selection of the patients for further diagnosis and treatment planning.

中文翻译：

---

从静止状态闭眼式脑电图检测中度创伤性脑损伤

创伤性脑损伤（TBI）是如果延迟就医会带来严重后果的伤害之一。通常，需要对计算机断层扫描（CT）或磁共振成像（MRI）进行分析以确定中度TBI患者的严重程度。但是，由于近来TBI患者数量的增加，对每位潜在患者进行CT扫描或MRI扫描不仅昂贵，而且耗时。因此，在本文中，我们研究了使用具有计算智能的脑电图（EEG）作为检测中度TBI患者严重程度的替代方法的可能性。脑电图程序比CT或MRI便宜得多。尽管与CT和MRI相比，脑电图没有很高的空间分辨率，但它有很高的时间分辨率。使用常规的计算智能方法从脑电图分析和预测中度TBI繁琐，因为它们通常涉及信号的复杂预处理，特征提取或特征选择。因此，我们提出了一种使用卷积神经网络（CNN）自动对健康受试者和中度TBI患者进行分类的方法。该计算智能系统的输入是静止状态的闭眼式EEG，无需进行预处理和特征选择。使用的EEG数据集包括15名健康志愿者和15名中度TBI患者，这些数据是从马来西亚吉兰丹大学医学院获得的。所提出的方法的性能已与其他四种现有方法进行了比较。平均分类准确率为72.46％，所提出的方法优于其他四种方法。该结果表明，所提出的方法有可能被用作中度TBI的初步筛选，以选择患者进行进一步的诊断和治疗计划。