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MNL-Network: A Multi-Scale Non-local Network for Epilepsy Detection From EEG Signals
Guokai Zhang1, Le Yang2, Boyang Li2
1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Frontiers in Neuroscience
|December 7, 2020
Summary
This study introduces a novel Multi-Scale Non-Local (MNL) network for automated epilepsy detection using electroencephalogram (EEG) signals. The MNL-Network improves classification accuracy by analyzing multi-scale features and their correlations, offering a more efficient diagnostic tool.
Area of Science:
- Neurology
- Artificial Intelligence
- Biomedical Signal Processing
Background:
- Epilepsy is a widespread neurological disorder impacting global health.
- Electroencephalogram (EEG) is the primary method for epilepsy detection.
- Manual EEG analysis is time-consuming and prone to errors due to inter-physician variability.
Purpose of the Study:
- To develop an automated system for epilepsy detection from EEG signals.
- To propose a novel Multi-Scale Non-Local (MNL) network for enhanced EEG classification.
- To address the limitations of manual EEG interpretation in epilepsy diagnosis.
Main Methods:
- Development of a 1D Convolutional Neural Network (CNN) architecture.
- Incorporation of a signal pooling layer with multiple 1D max-pooling sizes for multi-scale feature extraction.
- Integration of a multi-scale non-local layer to capture correlations between extracted features.
Main Results:
- The proposed MNL-Network demonstrated competitive performance in EEG classification.
- The signal pooling and multi-scale non-local layers effectively enhanced feature learning.
- Experimental validation was conducted using the standard Bonn dataset.
Conclusions:
- The MNL-Network offers a promising automated solution for epilepsy detection from EEG.
- The multi-scale feature extraction and correlation analysis contribute to improved classification accuracy.
- This approach has the potential to streamline the epilepsy diagnosis process.
