Epilepsy is a chronic neurological disorder marked by recurrent seizures that can severely impact quality of life. Electroencephalography (EEG) remains the primary tool for monitoring neural activity and detecting seizures, yet automated analysis remains challenging due to the temporal complexity of EEG signals. This study introduces ConvMambaNet, a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with the Mamba Structured State Space Model (SSM) to enhance temporal feature extraction. By embedding the Mamba-SSM block within a CNN framework, the model effectively captures both spatial and long-range temporal dynamics. Evaluated on the CHB-MIT Scalp EEG dataset, ConvMambaNet achieved a 99% accuracy and demonstrated robust performance under severe class imbalance. These results underscore the model's potential for precise and efficient seizure detection, offering a viable path toward real-time, automated epilepsy monitoring in clinical environments.

翻译：癫痫是一种以反复发作为特征的慢性神经系统疾病，严重影响患者生活质量。脑电图（EEG）仍然是监测神经活动和检测癫痫发作的主要工具，但由于EEG信号的时间复杂性，其自动化分析仍面临挑战。本研究提出ConvMambaNet，一种混合深度学习模型，它将卷积神经网络（CNNs）与Mamba结构化状态空间模型（SSM）相结合，以增强时序特征提取。通过将Mamba-SSM模块嵌入CNN框架，该模型能有效捕获空间特征和长程时序动态。在CHB-MIT头皮EEG数据集上的评估表明，ConvMambaNet实现了99%的准确率，并在严重的类别不平衡情况下表现出鲁棒性能。这些结果凸显了该模型在精确高效癫痫发作检测方面的潜力，为临床环境中实现实时自动化癫痫监测提供了可行路径。