School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300, Nibong Tebal, Penang, Malaysia.
Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Kota Bharu, Kelantan, Malaysia.
Comput Intell Neurosci. 2020 Mar 11;2020:8923906. doi: 10.1155/2020/8923906. eCollection 2020.
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.
创伤性脑损伤 (TBI) 是一种可能导致严重后果的损伤，如果延迟就医则后果更为严重。通常情况下，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。然而，由于现在 TBI 患者数量的增加，对每个潜在患者进行 CT 扫描或 MRI 扫描不仅昂贵，而且耗时。因此，在本文中，我们研究了使用脑电图 (EEG) 和计算智能作为替代方法来检测中度 TBI 患者严重程度的可能性。EEG 程序比 CT 或 MRI 便宜得多。虽然 EEG 的空间分辨率不如 CT 和 MRI 高，但它具有更高的时间分辨率。使用传统的计算智能方法对 EEG 进行中度 TBI 的分析和预测非常繁琐，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息状态闭眼 EEG，无需进行预处理和特征选择。该 EEG 数据集包括 15 名健康志愿者和 15 名中度 TBI 患者，这些数据是在马来西亚吉兰丹州的马来西亚理科大学医院获得的。与其他四种现有方法相比，所提出方法的性能已经进行了比较。所提出方法的平均分类准确率为 72.46%，优于其他四种方法。这一结果表明，该方法有可能作为中度 TBI 的初步筛查，用于选择进一步诊断和治疗计划的患者。
