Building Hybrid Deep Learning Models for EEG-Based Seizure Detection

Introduction

Epilepsy is a neurological disorder characterized by recurrent seizures, affecting millions of people worldwide. Electroencephalography (EEG) is widely used to monitor brain activity and detect seizure patterns. However, manual analysis of EEG signals is time-consuming and prone to error, making automated detection systems a critical area of research.

In this project, I developed a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with the Mamba Structured State Space Model (SSM) to improve temporal and spatial feature extraction from EEG signals.

Problem Statement

Traditional deep learning models face several challenges in EEG signal analysis:

• High temporal complexity and noise in EEG signals
• Long-range temporal dependencies that CNNs struggle to capture
• Class imbalance between seizure and non-seizure data
• Generalization issues across patients

The goal was to design a model capable of capturing both local spatial features and long-term temporal patterns while maintaining computational efficiency.

Model Architecture

The proposed architecture consists of three main components:

1. Signal Preprocessing
   • Noise filtering and normalization of EEG signals
   • Segmentation of continuous signals into fixed-length windows
2. CNN Feature Extractor
   • Convolutional layers for extracting spatial and local temporal features
   • Batch normalization and dropout for regularization
3. Mamba SSM Module
   • Structured State Space Model blocks for modeling long-range temporal dependencies
   • Integration with CNN outputs to enhance sequential representation

This hybrid design allows the model to leverage the strengths of both CNNs and state space models.

Implementation Details

• Frameworks: PyTorch, TensorFlow
• Dataset: CHB-MIT Scalp EEG Dataset
• Evaluation Metrics: Accuracy, Precision, Recall, F1-score
• Training Strategy: Handling class imbalance using weighted loss and data augmentation

Results and Insights

The hybrid model demonstrated strong performance in seizure detection tasks:

• Improved temporal pattern recognition compared to baseline CNN models
• Robust performance under class imbalance conditions
• Better generalization across patient datasets

Beyond accuracy, the model showed potential for real-time deployment in clinical environments due to its efficient architecture.

Real-World Implications

Automated EEG analysis systems can significantly reduce the workload of clinicians and improve diagnostic accuracy. Hybrid architectures like CNN + Mamba SSM open new possibilities for building intelligent healthcare systems capable of analyzing complex biomedical signals.

This project highlights the importance of combining modern deep learning architectures with domain-specific knowledge to solve real-world problems.

Conclusion

Hybrid deep learning models represent a promising direction for EEG-based seizure detection. By integrating CNNs with structured state space models, it is possible to capture both spatial and temporal dynamics effectively. Future work includes optimizing the model for real-time inference and extending the approach to other biomedical signal processing tasks.