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Related Experiment Video

Updated: Jan 16, 2026

Modeling Stroke in Mice: Transient Middle Cerebral Artery Occlusion via the External Carotid Artery
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EEG-Based Deep Learning Model for Hyper-Acute Large Vessel Occlusion Stroke Detection in Mice.

Tan Zhang1, Xiaolin Li2, Xinxin Hu3

  • 1Department of Neurosurgery, The Second Affiliated Hospital of Soochow University, Suzhou, China.

CNS Neuroscience & Therapeutics
|September 27, 2025
PubMed
Summary

This study developed a deep learning model using electroencephalography (EEG) to detect large vessel occlusion (LVO) stroke in its earliest stages. The model achieved high accuracy, showing potential for rapid, non-invasive stroke diagnosis.

Keywords:
EEGNetEEGacute ischemic strokedeep learninglarge vessel occlusion

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Large vessel occlusion (LVO) stroke requires rapid diagnosis and intervention.
  • Current diagnostic methods can be time-consuming, delaying critical treatment.
  • Early detection of hyper-acute LVO stroke is crucial for improving patient outcomes.

Purpose of the Study:

  • To develop a deep learning model for early and accurate detection of hyper-acute LVO stroke.
  • To utilize electroencephalography (EEG) data for stroke detection.
  • To evaluate the model's performance using various metrics.

Main Methods:

  • A pMCAO mouse model was used to simulate LVO stroke.
  • High-resolution EEG data was collected during the hyper-acute phase.
  • EEGNet architecture was employed for model development and evaluated using seven-fold cross-validation.

Main Results:

  • The deep learning model achieved 97.9% accuracy and 0.977 AUC.
  • Reliable stroke detection was achieved within 1.5 hours post-onset.
  • Classification accuracy exceeded 95% across hourly intervals, demonstrating high specificity.

Conclusions:

  • An EEG-based deep learning model demonstrates robust performance for hyper-acute LVO stroke detection.
  • The model shows potential as a biomarker for early stroke diagnosis.
  • This approach could form the basis for real-time, non-invasive stroke monitoring.