融合CNN与Mamba模型，实现高精度实时脑电癫痫发作检测
ConvMambaNet: A Hybrid CNN-Mamba State Space Architecture for Accurate and Real-Time EEG Seizure Detection
- 用CNN提取空间特征，Mamba-SSM捕捉长时序动态
- 在CHB-MIT数据集上达99%准确率，极端不平衡下仍稳定
- 适合临床实时癫痫监测系统开发人员参考
癫痫是一种慢性神经系统疾病，以反复发作的癫痫发作为特征，严重影响生活质量。脑电图（EEG）仍是监测神经活动和检测发作的主要工具，但自动化分析因脑电信号的时间复杂性而面临挑战。本文提出ConvMambaNet，一种将卷积神经网络（CNN）与结构化状态空间模型（Mamba-SSM）结合的混合深度学习模型，以增强时间特征提取能力。通过在CNN框架中嵌入Mamba-SSM模块，模型有效捕捉了空间信息和长程时间动态。在CHB-MIT头皮脑电数据集上的评估显示，该模型达到了99%的准确率，并在严重类别不平衡条件下表现出稳健性能。结果表明，该模型在精确且高效地检测癫痫发作方面具有潜力，为临床环境中实现实时、自动化的癫痫监测提供了可行路径。
原文摘要 · Abstract (English)
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.
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