Li Qi, Cao Wei, Zhang Anyuan
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
Jilin Provincial International Joint Research Center of Brain Informatics and Intelligence Science, Changchun, 130022, China.
J Transl Med. 2025 Aug 6;23(1):871. doi: 10.1186/s12967-025-06862-z.
Automated seizure detection based on scalp electroencephalography (EEG) can significantly accelerate the epilepsy diagnosis process. However, most existing deep learning-based epilepsy detection methods are deficient in mining the local features and global time series dependence of EEG signals, limiting the performance enhancement of the models in seizure detection.
Our study proposes an epilepsy detection model, CMFViT, based on a Multi-Stream Feature Fusion (MSFF) strategy that fuses a Convolutional Neural Network (CNN) with a Vision Transformer (ViT). The model converts EEG signals into time-frequency domain images using the Tunable Q-factor Wavelet Transform (TQWT), and then utilizes the CNN module and the ViT module to capture local features and global time-series correlations, respectively. It fuses different feature representations through the MSFF strategy to enhance its discriminative ability, and finally completes the classification task through the average pooling layer and the fully connected layer.
The effectiveness of the model was validated by experimental evaluations on the publicly available CHB-MIT dataset and the Kaggle 121 people epilepsy dataset. The model achieved 98.85% classification accuracy and other excellent metrics in single-subject experiments on the CHB-MIT dataset, and also demonstrated strong performance in cross-subject experiments on the Kaggle dataset. Ablation experiments demonstrate the complementary roles of the CNN and ViT modules, and their integration significantly improves detection accuracy and generalization. Comparisons with other methods highlight the advantages of the CMFViT model.
The CMFViT model provides an efficient, accurate, and innovative solution for complex EEG signal analysis and seizure detection tasks for single and cross-subjects while laying the foundation for developing real-time, accurate seizure detection systems.
基于头皮脑电图（EEG）的自动癫痫发作检测可显著加速癫痫诊断过程。然而，大多数现有的基于深度学习的癫痫检测方法在挖掘EEG信号的局部特征和全局时间序列依赖性方面存在不足，限制了模型在癫痫发作检测中的性能提升。
我们的研究提出了一种基于多流特征融合（MSFF）策略的癫痫检测模型CMFViT，该策略将卷积神经网络（CNN）与视觉Transformer（ViT）相融合。该模型使用可调Q因子小波变换（TQWT）将EEG信号转换为时频域图像，然后分别利用CNN模块和ViT模块来捕获局部特征和全局时间序列相关性。它通过MSFF策略融合不同的特征表示以增强其判别能力，最后通过平均池化层和全连接层完成分类任务。
通过对公开可用的CHB - MIT数据集和Kaggle 121人癫痫数据集进行实验评估，验证了该模型的有效性。该模型在CHB - MIT数据集的单受试者实验中实现了98.85%的分类准确率和其他优异指标，并且在Kaggle数据集的跨受试者实验中也表现出强大的性能。消融实验证明了CNN和ViT模块的互补作用，它们的集成显著提高了检测准确率和泛化能力。与其他方法的比较突出了CMFViT模型的优势。
CMFViT模型为单受试者和跨受试者的复杂EEG信号分析和癫痫发作检测任务提供了一种高效、准确且创新的解决方案，同时为开发实时、准确的癫痫发作检测系统奠定了基础。
