Kuang Zhejun, Guo Liming, Wang Jingrui, Zhao Jian, Wang Liu, Geng Kangwei
College of Computer Science and Technology, Changchun University, Changchun 130022, China.
Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Ministry of Education, Changchun 130022, China.
Brain Sci. 2024 Oct 29;14(11):1090. doi: 10.3390/brainsci14111090.
The localization of seizure onset zones (SOZs) is a critical step before the surgical treatment of epilepsy.
In this paper, we propose an SOZ detection method based on convolutional neural networks and EEG signals. This method aims to locate SOZs through the seizure status of each channel in multi-channel EEG signals. First, we preprocess the data with filtering, segmentation, resampling, and standardization to ensure their quality and consistency. Then, the single-channel UCI epilepsy seizure recognition dataset is used to train and test the convolutional neural network (CNN) model, achieving an accuracy of 98.70%, a sensitivity of 97.53%, and a specificity of 98.98%. Next, the multi-channel clinical EEG dataset collected by a hospital is divided into 21 single-channel site datasets and input into the model for detection, and then the seizure results of 21 sites per second are obtained. Finally, the seizure sites are visualized through the international 10-20 system electrode distribution map, diagrams of the change process of the seizure sites during seizures are drawn, and patients' SOZs are located.
Our proposed method well classifies seizure and non-seizure data and successfully locates SOZs by detecting the seizure results of 21 sites through a single-channel model. This study can effectively assist doctors in locating the SOZs of patients and provide help for the diagnosis and treatment of epilepsy.
癫痫发作起始区（SOZs）的定位是癫痫外科治疗前的关键步骤。
本文提出一种基于卷积神经网络和脑电图（EEG）信号的SOZ检测方法。该方法旨在通过多通道EEG信号中每个通道的癫痫发作状态来定位SOZs。首先，我们通过滤波、分割、重采样和标准化对数据进行预处理，以确保其质量和一致性。然后，使用单通道UCI癫痫发作识别数据集训练和测试卷积神经网络（CNN）模型，准确率达到98.70%，灵敏度为97.53%，特异性为98.98%。接下来，将某医院收集的多通道临床EEG数据集划分为21个单通道部位数据集并输入模型进行检测，进而获得每秒21个部位的癫痫发作结果。最后，通过国际10 - 20系统电极分布图对癫痫发作部位进行可视化，绘制癫痫发作期间发作部位的变化过程图，并定位患者的SOZs。
我们提出的方法能够很好地对癫痫发作和非癫痫发作数据进行分类，并通过单通道模型检测21个部位的癫痫发作结果成功定位SOZs。本研究能够有效协助医生定位患者的SOZs，为癫痫的诊断和治疗提供帮助。
