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

TL;DR

ConvMambaNet is a hybrid CNN-Mamba architecture that significantly improves EEG seizure detection accuracy and robustness, enabling real-time automated epilepsy monitoring by effectively capturing spatial and temporal features.

Contribution

This paper introduces ConvMambaNet, a novel hybrid deep learning model combining CNNs with Mamba-SSM for enhanced temporal feature extraction in EEG analysis.

Findings

Achieved 99% accuracy on CHB-MIT EEG dataset

Robust performance under class imbalance

Effective real-time seizure detection

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.