{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zeng Z"],"funding":["Major Projects of Institute of Chinese Materia Medica China Academy of Chinese Medical Sciences","Jiangxi Provincial Education Department Project","Hospital-level project of the Second Affiliated Hospital of Nanchang University","Jiangxi Provincial Science and Technology Department Project"],"pagination":["e0279706"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9794063"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["17(12)"],"pubmed_abstract":["<h4>Objective</h4>Ischemic stroke (IS) with subsequent cerebrocardiac syndrome (CCS) has a poor prognosis. We aimed to investigate electrocardiogram (ECG) changes after IS with artificial intelligence (AI).<h4>Methods</h4>We collected ECGs from a healthy population and patients with IS, and then analyzed participant demographics and ECG parameters to identify abnormal features in post-IS ECGs. Next, we trained the convolutional neural network (CNN), random forest (RF) and support vector machine (SVM) models to automatically detect the changes in the ECGs; Additionally, We compared the CNN scores of good prognosis (mRS ≤ 2) and poor prognosis (mRS > 2) to assess the prognostic value of CNN model. Finally, we used gradient class activation map (Grad-CAM) to localize the key abnormalities.<h4"],"journal":["PloS one"],"pubmed_title":["Assessing electrocardiogram changes after ischemic stroke with artificial intelligence."],"pmcid":["PMC9794063"],"funding_grant_id":["No. 2021efyC02","No. 20208031","No. GJJ200133","No. ZZ15-WT-04"],"pubmed_authors":["Xu L","Yan L","Chen Q","Yi Y","Zeng Z","Lou W","Deng L","Cheng Z","Fan G","Wang Q","Zhang Y","Wang Y","Yu Y"],"additional_accession":[]},"is_claimable":false,"name":"Assessing electrocardiogram changes after ischemic stroke with artificial intelligence.","description":"<h4>Objective</h4>Ischemic stroke (IS) with subsequent cerebrocardiac syndrome (CCS) has a poor prognosis. We aimed to investigate electrocardiogram (ECG) changes after IS with artificial intelligence (AI).<h4>Methods</h4>We collected ECGs from a healthy population and patients with IS, and then analyzed participant demographics and ECG parameters to identify abnormal features in post-IS ECGs. Next, we trained the convolutional neural network (CNN), random forest (RF) and support vector machine (SVM) models to automatically detect the changes in the ECGs; Additionally, We compared the CNN scores of good prognosis (mRS ≤ 2) and poor prognosis (mRS > 2) to assess the prognostic value of CNN model. Finally, we used gradient class activation map (Grad-CAM) to localize the key abnormalities.<h4","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-04T12:09:44.643Z","creation":"2025-04-04T12:09:44.643Z"},"accession":"S-EPMC9794063","cross_references":{"pubmed":["36574427"],"doi":["10.1371/journal.pone.0279706"]}}