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MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection
Wei Tong1, Weiqi Yue2, Fangni Chen1
1School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Sensors (Basel, Switzerland)
|July 13, 2024
Summary
This study introduces a deep learning model using electroencephalography (EEG) signals for rapid ischemic stroke detection. The novel method achieves high accuracy, aiding in faster diagnosis and treatment of stroke patients.
Area of Science:
- Neuroscience
- Medical Technology
- Artificial Intelligence
Background:
- Ischemic stroke, caused by disrupted brain blood flow, necessitates rapid diagnosis for effective treatment to prevent disability or death.
- Current diagnostic methods may not always provide the speed required for critical early intervention in ischemic stroke.
- Electroencephalography (EEG) signals offer a potential avenue for non-invasive brain activity monitoring.
Purpose of the Study:
- To develop and validate a deep learning model for the rapid detection of ischemic stroke using electroencephalography (EEG) signals.
- To explore novel feature extraction techniques from EEG data for improved stroke identification.
- To assess the performance and efficiency of the proposed deep learning model compared to existing methods.
Main Methods:
- Collected EEG signals from 20 acute ischemic stroke patients and 19 healthy controls.
- Developed a novel feature, correlation-weighted Phase Lag Index (cwPLI), to analyze EEG channel synchronization and functional connectivity.
- Fused spatio-temporal (cwPLI matrix) and nonlinear (Sample Entropy) features, then classified using a novel MSE-VGG deep learning network.
Main Results:
- The proposed deep learning model achieved high diagnostic performance: 90.17% accuracy, 89.86% sensitivity, and 90.44% specificity.
- The method demonstrated superior time efficiency compared to other state-of-the-art examinations.
- Feature fusion combining cwPLI and Sample Entropy enhanced the model's discriminative capability.
Conclusions:
- The developed deep learning model shows significant potential for the rapid and accurate detection of ischemic stroke.
- This study highlights the value of EEG signal analysis combined with advanced machine learning for neurological disorder diagnosis.
- The findings support the efficacy of deep learning in identifying ischemic stroke, paving the way for improved clinical tools.
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