{"doi":"10.22489/cinc.2023.346","title":"Feasibility of ECGI Endocardial Solutions in Localizing the VT Reentrant Circuit","abstract":"Electrocardiographic imaging (ECGI) effectively reconstructs the epicardial surface's activation pattern, aiding in arrhythmia detection like Ventricular Tachycardia (VT).Yet, the feasibility of ECGI endocardial solutions for VT mapping remains unclear due to lost local activation details.Our goal is to assess the reliability and feasibility of endocardial ECGI solutions in categorizing reentrant circuits as either 2D epicardial, endocardial, or 3D circuits.We utilize Laplacian eigenmaps (LE) for dimensionality reduction and visualization.The LE of ECGI solutions on the left ventricle revealed a pattern for capturing endocardial breakthrough time.Considering activation order, we defined two VT circuit categories: closer to or on the epicardial or endocardial surface.Additionally, we used isochronal activation time maps to identify regions of reentrant circuit rotational activities.By analyzing activation percentage within the VT cycle on each surface, we categorize VT circuits as either 2D on endocardium or epicardium (full circuit on one surface) or 3D (involving the mid-myocardial wall).We validated our method on 23 simulation data sets from eight chronically infarcted porcine hearts.21/23 cases were accurately classified as closer to the Epicardium or Endocardium, and 82% were correctly categorized as either 2D, 3D, or mid-myocardial.","journal":"Computing in cardiology","year":2023,"id":415499,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9623,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1146107,"name":"Omar Gharbia","orcid":"0000-0003-2783-6767","position":1,"is_corresponding":false},{"id":333398,"name":"Natalia A. Trayanova","orcid":"0000-0002-8661-063X","position":2,"is_corresponding":false},{"id":532084,"name":"Linwei Wang","orcid":"0000-0001-5406-1034","position":3,"is_corresponding":false},{"id":457305,"name":"John L. Sapp","orcid":"0000-0002-9602-2751","position":4,"is_corresponding":false},{"id":920673,"name":"Maryam Toloubidokhti","orcid":"0000-0003-2923-441X","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T01:22:12.933438Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}