Imperial College London
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ProfessorKimChristensen

Faculty of Natural Sciences, Department of Physics

Professor of Theoretical Physics
 
 
 
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Contact

 

+44 (0)20 7594 7574k.christensen Website

 
 
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Assistant

 

Mrs Carolyn Dale +44 (0)20 7594 7579

 
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Location

 

812Blackett LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{McGillivray:2018:10.1098/rsos.172434,
author = {McGillivray, MF and Cheng, W and Peters, NS and Christensen, K},
doi = {10.1098/rsos.172434},
journal = {ROYAL SOCIETY OPEN SCIENCE},
title = {Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation},
url = {http://dx.doi.org/10.1098/rsos.172434},
volume = {5},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Mapping resolution has recently been identified as a key limitation in successfully locating the drivers of atrial fibrillation (AF). Using a simple cellular automata model of AF, we demonstrate a method by which re-entrant drivers can be located quickly and accurately using a collection of indirect electrogram measurements. The method proposed employs simple, out-of-the-box machine learning algorithms to correlate characteristic electrogram gradients with the displacement of an electrogram recording from a re-entrant driver. Such a method is less sensitive to local fluctuations in electrical activity. As a result, the method successfully locates 95.4% of drivers in tissues containing a single driver, and 95.1% (92.6%) for the first (second) driver in tissues containing two drivers of AF. Additionally, we demonstrate how the technique can be applied to tissues with an arbitrary number of drivers. In its current form, the techniques presented are not refined enough for a clinical setting. However, the methods proposed offer a promising path for future investigations aimed at improving targeted ablation for AF.
AU  - McGillivray,MF
AU  - Cheng,W
AU  - Peters,NS
AU  - Christensen,K
DO  - 10.1098/rsos.172434
PY  - 2018///
SN  - 2054-5703
TI  - Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation
T2  - ROYAL SOCIETY OPEN SCIENCE
UR  - http://dx.doi.org/10.1098/rsos.172434
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000431110100069&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/60090
VL  - 5
ER  -
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