{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T11:04:36Z","timestamp":1780398276630,"version":"3.54.1"},"reference-count":36,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:00:00Z","timestamp":1758931200000},"content-version":"vor","delay-in-days":26,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81891004"],"award-info":[{"award-number":["81891004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CNS Neurosci Ther"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to develop a deep learning model for the early and accurate detection of hyper\u2010acute large vessel occlusion (LVO) stroke using EEG data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A pMCAO mouse model was used to simulate LVO stroke, with high\u2010resolution EEG data collected during the hyper\u2010acute phase.\n                      <jats:italic>EEGNet<\/jats:italic>\n                      , a specialized deep learning architecture, was employed to develop a model based on EEG signals for the detection of hyper\u2010acute LVO strokes. Seven\u2010fold cross\u2010validation was conducted to evaluate the model's performance across multiple metrics, including accuracy, AUC, precision, recall, and F1 score.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model achieved an overall accuracy of 97.9% and an AUC of 0.977, demonstrating excellent diagnostic performance across the hyper\u2010acute phase. Stroke detection was reliable within 1.5\u2009h post\u2010onset, with classification accuracies exceeding 95% in all five time intervals segmented by hour. t\u2010SNE analysis confirmed effective feature extraction, and comparisons with sham\u2010operated mice validated the model's specificity for stroke\u2010related EEG changes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The EEG\u2010based deep learning model showed robust performance in hyper\u2010acute 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\u2010time, non\u2010invasive monitoring in clinical and prehospital settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/cns.70592","type":"journal-article","created":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T07:46:01Z","timestamp":1758959161000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["<scp>EEG<\/scp>\n                    \u2010Based Deep Learning Model for Hyper\u2010Acute Large Vessel Occlusion Stroke Detection in Mice"],"prefix":"10.1111","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6702-7255","authenticated-orcid":false,"given":"Tan","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Neurosurgery The Second Affiliated Hospital of Soochow University  Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolin","family":"Li","sequence":"additional","affiliation":[{"name":"Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences  Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxin","family":"Hu","sequence":"additional","affiliation":[{"name":"Changchun University of Science and Technology  Changchun China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences  Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingchun","family":"Mu","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery The Second Affiliated Hospital of Soochow University  Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5450-6531","authenticated-orcid":false,"given":"Xiaoke","family":"Chai","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Beijing Tiantan Hospital Capital Medical University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Lan","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery The Second Affiliated Hospital of Soochow University  Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7304-0255","authenticated-orcid":false,"given":"Jizong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery The Second Affiliated Hospital of Soochow University  Suzhou China"},{"name":"Department of Neurosurgery, Beijing Tiantan Hospital Capital Medical University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,9,27]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(24)00642-1"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.118.022253"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(24)01410-7"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaneurol.2024.2500"},{"issue":"3","key":"e_1_2_11_6_1","first-page":"e111","article-title":"Accuracy of Prediction Instruments for Diagnosing Large Vessel Occlusion in Individuals With Suspected Stroke: A Systematic Review for the 2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke","volume":"49","author":"Smith E. 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