ConvMambaNet: A Hybrid CNN-Mamba State Space Architecture for Accurate and Real-Time EEG Seizure Detection

2026-01-19

AI Summary

This work addresses the challenges of temporal complexity and severe class imbalance in electroencephalogram (EEG)-based seizure detection by proposing a hybrid architecture that integrates convolutional neural networks (CNNs) with the Mamba state space model (SSM). For the first time, Mamba is embedded within a CNN framework to jointly capture local spatial features and long-range temporal dependencies. This design enhances the model’s robustness to class imbalance while enabling high-accuracy, real-time seizure detection. Evaluated on the CHB-MIT dataset, the proposed model achieves 99% accuracy, demonstrating superior performance and strong potential for clinical deployment.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• EEG seizure detection
• epilepsy
• temporal complexity
• automated analysis
• real-time monitoring

Innovation

• ConvMambaNet
• Mamba SSM
• CNN
• EEG seizure detection
• state space model