The temporal stability of recurrence quantification analysis attributes from chronic atrial fibrillation electrograms
journal contributionposted on 2019-09-23, 16:25 authored by Tiago Paggi de Almeida, Fernando Soares Schlindwein, João Salinet, Xin Li, Gavin Shen-Wei Chu, Jiun Haur Tuan, Peter James Stafford, G André Ng, Diogo Coutinho Soriano
Introduction The temporal behavior of atrial electrograms (AEGs) collected during persistent atrial fibrillation (persAF) directly affects ablative treatment outcomes. We investigated different durations of AEGs collected during persAF using recurrence quantification analysis (RQA). Methods 797 bipolar AEGs with different durations (from 0.5 s to 8 s) from 18 patients were investigated. Four RQA-based attributes were evaluated based on AEG durations: determinism (DET); recurrence rate (RR); laminarity (LAM); and diagonal lines’ entropy (ENTR). The Spearman correlation (ρ) between each duration versus 8 s was calculated. AEG classification was performed following the CARTO criteria (Biosense Webster) and receiving operating characteristic (ROC) curves were created for the RQA variables. Results The RQA variables successfully discriminated the AEGs: the area under the ROC curves were as high as 0.70 for AEGs with 3.5 s or greater. Three types of AEGs were found using these variables: normal, fractionated and temporally unstable. The number of unstable AEGs decreased with longer AEG segments. Different AEG durations significantly affected the RQA variables (P<0.0001), with no statistical difference between the durations 6 s, 7 s and 8 s for DET, LAM and ENTR, and no difference between 7 s and 8 s for RR (P<0.0001). AEGs with 3 s or longer have shown ρ ≥ 80% for all variables. Conclusion The RQA variables have been shown effective in the characterization of AEGs collected during persAF with a shorter duration than current recommendations, which motivates their use for the characterization of atrial substrate during persAF ablation.
The work reported in this paper was supported by the NIHR Leicester Biomedical Research Centre. DCS received Conselho Nacional de Desenvolvimento Científico e Tecnológico financial support (CNPq, 449467/2014-7 and 305616/2016-1). JS received grants from CNPq (200598/2009-0) and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, 2015/12799-9). TPA received research grants from CNPq (200251/2012-0), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brazil) and FAPESP (2017/00319-8).
CitationResearch on Biomedical Engineering, 2018, 34 (4), pp. 337-349 (13)
Author affiliation/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Department of Cardiovascular Sciences
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