Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/44333
Title: Unsupervised k‐mean classification of atrial electrograms from human persistent atrial fibrillation
Authors: Almeida, TP
Soriano, DC
Li, X
Chu, GS
Salinet, JL
Schlindwein, FS
Stafford, PJ
Ng, GA
Yoneyama, T
First Published: 23-Sep-2018
Presented at: Computing in Cardiology 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Computing in Cardiology 2018; Vol 45
Abstract: The dichotomous criterion for atrial electrogram (AEG) classification as proposed by commercial systems (normal/fractionated) to guide ablation has been shown insufficient for persistent atrial fibrillation (persAF) therapy. In this study, we used unsupervised classification to investigate possible sub-groups of persAF AEGs. 3745 bipolar AEGs were collected from 14 persAF patients after pulmonary vein isolation. Automated AEG classification (normal/fractionated) was performed using the CARTO criterion (Biosense Webster). The CARTO attributes (ICL, ACI and SCI) were used to create a 3D space distribution. K-mean with five groups was implemented. Group 1 (43%) represents normal AEGs with low ICL, high ACI and SCI. Groups 2 (9%) and 3 (9%) have shown similar low ICL, but Group 3 has shown AEGs with short activation intervals, as opposed to Group 2. Group 4 (23%) suggests moderated fractionation, with high ACI but low SCI. Group 5 (15%) has shown highly fractionated AEGs with high ICL, low ACI and SCI. The three attributes were significantly different among the five groups (P<0.0001), except ICL between Groups 3 and 4 (P>0.999) and SCI between Groups 3 and 5 (P>0.999). The five sub-groups of AEGs found by the k-mean have shown distinct characteristics, which could provide a more detailed characterization of the atrial substrate during ablation.
DOI Link: 10.22489/CinC.2018.127
ISSN: 2325-887X
Links: http://www.cinc.org/archives/2018/pdf/CinC2018-127.pdf
http://hdl.handle.net/2381/44333
Version: Publisher Version
Status: Peer-reviewed
Type: Conference Paper
Rights: Copyright © the authors, 2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Conference Papers & Presentations, Dept. of Cardiovascular Sciences

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