{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang Y"],"funding":["Fundamental Research Funds for the Central Universities","National Natural Science Foundation of China","BUPT Excellent Ph.D. Students Foundation","State Key Program of the National Natural Science Foundation of China"],"pagination":["769"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9774098"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(12)"],"pubmed_abstract":["Successful surgery on drug-resistant epilepsy patients (DRE) needs precise localization of the seizure onset zone (SOZ). Previous studies analyzing this issue still face limitations, such as inadequate analysis of features, low sensitivity and limited generality. Our study proposed an innovative and effective SOZ localization method based on multiple epileptogenic biomarkers (spike and HFOs), and analysis of single-contact (MEBM-SC) to address the above problems. We extracted contacts epileptic features from signal distributions and signal energy based on machine learning and end-to-end deep learning. Among them, a normalized pathological ripple rate was designed to reduce the disturbance of physiological ripple and enhance the performance of SOZ localization. Then, a feature selection alg"],"journal":["Bioengineering (Basel, Switzerland)"],"pubmed_title":["Automatic Localization of Seizure Onset Zone Based on Multi-Epileptogenic Biomarkers Analysis of Single-Contact from Interictal SEEG."],"pmcid":["PMC9774098"],"funding_grant_id":["61471064","2020XD-A06-1","82030037","CX2021206"],"pubmed_authors":["Wei P","Yang Y","Kang G","Li S","Guo J","Huang J","Zhao G","Su Z","Wang Y"],"additional_accession":[]},"is_claimable":false,"name":"Automatic Localization of Seizure Onset Zone Based on Multi-Epileptogenic Biomarkers Analysis of Single-Contact from Interictal SEEG.","description":"Successful surgery on drug-resistant epilepsy patients (DRE) needs precise localization of the seizure onset zone (SOZ). Previous studies analyzing this issue still face limitations, such as inadequate analysis of features, low sensitivity and limited generality. Our study proposed an innovative and effective SOZ localization method based on multiple epileptogenic biomarkers (spike and HFOs), and analysis of single-contact (MEBM-SC) to address the above problems. We extracted contacts epileptic features from signal distributions and signal energy based on machine learning and end-to-end deep learning. Among them, a normalized pathological ripple rate was designed to reduce the disturbance of physiological ripple and enhance the performance of SOZ localization. Then, a feature selection alg","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2025-04-18T16:33:44.692Z","creation":"2025-04-07T03:48:36.679Z"},"accession":"S-EPMC9774098","cross_references":{"pubmed":["36550975"],"doi":["10.3390/bioengineering9120769"]}}