Li_et_al_CinC_2018_Classifications_paper_final.pdf (7.62 MB)
A K-Nearest Neighbour Classifier for Predicting Catheter Ablation Responses Using Noncontact Electrograms During Persistent Atrial Fibrillation
conference contribution
posted on 2019-06-10, 15:37 authored by X Li, GS Chu, TP Almeida, JL Salinet, AR Mistry, Z Vali, PJ Stafford, FS Schlindwein, GA NgThe mechanisms for the initiation and maintenance of
atrial fibrillation (AF) are still poorly understood. Identification of atrial sites which are effective ablation targets remains challenging. Supervised machine learning
has emerged as an effective tool for handling classification problems with multiple features. The main goal of
this work is to use learning algorithms in predicting the
responses of ablating electrograms and their effect on terminating AF and the cycle length changes. A total of 3,206
electrograms (EGMs) from ten persistent AF (persAF) patients were used. 5-fold cross-validation was applied, in
which 80 % of the data were used as training set and 20
% used as validation. Dominant frequency (DF) and organisation index (OI) were calculated from EGMs (264
seconds) for all patients and used as input features. A
k-nearest neighbour (KNN) classifier was trained using
ablation lesion data and deployed in additional 17,274
EGMs that were not ablated. The classification accuracy
of 85.2 % was achieved for the KNN classifier.
We have proposed a supervised learning algorithm using
DF features, which has shown the ability of accurately
performing EGM signal classification that could be potentially used to identify ablation targets and become a robust
real-time patient diagnosis system.
Funding
This work was supported by the NIHR Leicester Biomedical Research Centre. XL received research grants from Medical Research Council, UK. TPA received research grants from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, n. 2017/00319-8).
History
Citation
Computing in Cardiology 2018; Vol 45Author affiliation
/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of EngineeringSource
Computing in Cardiology 2018Version
- AM (Accepted Manuscript)
Published in
Computing in Cardiology 2018; Vol 45Publisher
Institute of Electrical and Electronics Engineers (IEEE)issn
2325-887XAcceptance date
2018-07-02Copyright date
2018Available date
2019-06-10Language
enAdministrator link
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