ArticleCNS neuroscience & therapeutics2025
EEG-Based Deep Learning Model for Hyper-Acute Large Vessel Occlusion Stroke Detection in Mice.
Article in CNS neuroscience & therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- EEG-Based Deep Learning Model for Hyper-Acute Large Vessel Occlusion Stroke Detection in Mice.CNS neuroscience & therapeutics · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
objectiveThis study aims to develop a deep learning model for the early and accurate detection of hyper-acute large vessel occlusion (LVO) stroke using EEG data.
methodsA pMCAO mouse model was used to simulate LVO stroke, with high-resolution EEG data collected during the hyper-acute phase. EEGNet, a specialized deep learning architecture, was employed to develop a model based on EEG signals for the detection of hyper-acute LVO strokes. Seven-fold cross-validation was conducted to evaluate the model's performance across multiple metrics, including accuracy, AUC, precision, recall, and F1 score.
resultsThe model achieved an overall accuracy of 97.9% and an AUC of 0.977, demonstrating excellent diagnostic performance across the hyper-acute phase. Stroke detection was reliable within 1.5 h post-onset, with classification accuracies exceeding 95% in all five time intervals segmented by hour. t-SNE analysis confirmed effective feature extraction, and comparisons with sham-operated mice validated the model's specificity for stroke-related EEG changes.
conclusionThe EEG-based deep learning model showed robust performance in hyper-acute LVO stroke detection, achieving high accuracy and specificity. These results highlight its potential as a biomarker for early stroke diagnosis and as a foundation for real-time, non-invasive monitoring in clinical and prehospital settings.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.