{"doi":"10.3389/fphys.2020.611266","title":"Non-invasive Spatial Mapping of Frequencies in Atrial Fibrillation: Correlation With Contact Mapping","abstract":"Introduction: Regional differences in activation rates may contribute to the electrical substrates that maintain atrial fibrillation (AF), and estimating them non-invasively may help guide ablation or select anti-arrhythmic medications. We tested whether non-invasive assessment of regional AF rate accurately represents intracardiac recordings. Methods : In 47 patients with AF (27 persistent, age 63 ± 13 years) we performed 57-lead non-invasive Electrocardiographic Imaging (ECGI) in AF, simultaneously with 64-pole intracardiac signals of both atria. ECGI was reconstructed by Tikhonov regularization. We constructed personalized 3D AF rate distribution maps by Dominant Frequency (DF) analysis from intracardiac and non-invasive recordings. Results: Raw intracardiac and non-invasive DF differed substantially, by 0.54 Hz [0.13 – 1.37] across bi-atrial regions ( R 2 = 0.11). Filtering by high spectral organization reduced this difference to 0.10 Hz (cycle length difference of 1 – 11 ms) [0.03 – 0.42] for patient-level comparisons ( R 2 = 0.62), and 0.19 Hz [0.03 – 0.59] and 0.20 Hz [0.04 – 0.61] for median and highest DF, respectively. Non-invasive and highest DF predicted acute ablation success ( p = 0.04). Conclusion: Non-invasive estimation of atrial activation rates is feasible and, when filtered by high spectral organization, provide a moderate estimate of intracardiac recording rates in AF. Non-invasive technology could be an effective tool to identify patients who may respond to AF ablation for personalized therapy.","journal":"Frontiers in Physiology","year":2021,"id":190550,"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":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9308,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":755438,"name":"Kian Waddell","orcid":null,"position":1,"is_corresponding":false},{"id":755439,"name":"Sarah Magee","orcid":null,"position":2,"is_corresponding":false},{"id":241956,"name":"Albert J. Rogers","orcid":"0000-0001-6585-534X","position":3,"is_corresponding":false},{"id":330035,"name":"Mahmood Alhusseini","orcid":null,"position":4,"is_corresponding":false},{"id":754788,"name":"Ismael Hernández‐Romero","orcid":"0000-0002-0525-7476","position":5,"is_corresponding":false},{"id":754789,"name":"Alejandro Costoya‐Sánchez","orcid":"0000-0002-5072-1282","position":6,"is_corresponding":false},{"id":754790,"name":"Alejandro Liberos","orcid":"0000-0002-6963-6328","position":7,"is_corresponding":false},{"id":241959,"name":"Sanjiv M. Narayan","orcid":"0000-0001-7552-5053","position":8,"is_corresponding":false},{"id":678238,"name":"Miguel Rodrigo","orcid":"0000-0002-6092-6847","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-18T23:49:26.966524Z","pmid":"33584334","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":[]}