MultiEpilepsyNet: An EEG and MRI data based multimodal seizure detection model using hybrid deep learning model
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.MultiEpilepsyNet offers accurate, privacy-preserving epilepsy diagnosis using federated learning and hybrid deep learning. This multimodal framework enhances early detection and management of neurological disorders.
Area Of Science
- Neurology
- Artificial Intelligence
- Medical Imaging
Background
- Epilepsy diagnosis requires accurate, privacy-preserving solutions.
- Current methods struggle with centralized data, generalizability, feature extraction, and noise.
Purpose Of The Study
- To introduce MultiEpilepsyNet, a multimodal seizure detection framework.
- To enhance privacy, accuracy, and generalizability in epilepsy diagnostics.
Main Methods
- Federated learning (SeizureFed-Net) for privacy-preserving collaborative learning from EEG and MRI data.
- Hybrid deep learning (SeizureShieldNet) fusing BBIDNet and FD-TMS for robust seizure detection.
- Jackal-Wolf Hybrid Optimizer (JWHO) for optimal feature selection.
- EpiSkullNet++ for enhanced MRI brain segmentation.
Main Results
- Achieved 99.36% accuracy on CHB-MIT EEG dataset and 99.38% on an epilepsy MRI dataset.
- Demonstrated improved robustness to missing modalities and reduced training overhead.
- Showcased enhanced privacy preservation compared to centralized models.
Conclusions
- MultiEpilepsyNet effectively addresses critical barriers in clinical epilepsy diagnosis.
- The framework offers a scalable, privacy-preserving, and clinically viable solution.
- Highlights the potential of multimodal federated learning in neurological disorder management.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
