Citation

BibTex format

@article{Cantwell:2019:10.1016/j.compbiomed.2018.10.015,
author = {Cantwell, C and Mohamied, Y and Tzortzis, K and Garasto, S and Houston, C and Chowdhury, R and Ng, F and Bharath, A and Peters, N and Cantwell, CD and Mohamied, Y and Tzortzis, KN and Garasto, S and Houston, C and Chowdhury, RA and Ng, FS and Bharath, AA and Peters, NS},
doi = {10.1016/j.compbiomed.2018.10.015},
journal = {Computers in Biology and Medicine},
pages = {339--351},
title = {Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling},
url = {http://dx.doi.org/10.1016/j.compbiomed.2018.10.015},
volume = {104},
year = {2019}
}
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TY  - JOUR
AB  - We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often through catheter ablation, which involves the targeted localised destruction of regions of the myocardium responsible for initiating or perpetuating the arrhythmia. Ablation targets are either anatomically defined, or identified based on their functional properties as determined through the analysis of contact intracardiac electrograms acquired with increasing spatial density by modern electroanatomic mapping systems. While numerous quantitative approaches have been investigated over the past decades for identifying these critical curative sites, few have provided a reliable and reproducible advance in success rates. Machine learning techniques, including recent deep-learning approaches, offer a potential route to gaining new insight from this wealth of highly complex spatio-temporal information that existing methods struggle to analyse. Coupled with predictive modelling, these techniques offer exciting opportunities to advance the field and produce more accurate diagnoses and robust personalised treatment. We outline some of these methods and illustrate their use in making predictions from the contact electrogram and augmenting predictive modelling tools, both by more rapidly predicting future states of the system and by inferring the parameters of these models from experimental observations.
AU  - Cantwell,C
AU  - Mohamied,Y
AU  - Tzortzis,K
AU  - Garasto,S
AU  - Houston,C
AU  - Chowdhury,R
AU  - Ng,F
AU  - Bharath,A
AU  - Peters,N
AU  - Cantwell,CD
AU  - Mohamied,Y
AU  - Tzortzis,KN
AU  - Garasto,S
AU  - Houston,C
AU  - Chowdhury,RA
AU  - Ng,FS
AU  - Bharath,AA
AU  - Peters,NS
DO  - 10.1016/j.compbiomed.2018.10.015
EP  - 351
PY  - 2019///
SN  - 0010-4825
SP  - 339
TI  - Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling
T2  - Computers in Biology and Medicine
UR  - http://dx.doi.org/10.1016/j.compbiomed.2018.10.015
UR  - http://arxiv.org/abs/1810.04227v1
UR  - http://hdl.handle.net/10044/1/63445
VL  - 104
ER  -
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