Imperial College London
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Dr Fu Siong Ng

Faculty of Medicine, National Heart & Lung Institute

Reader in Cardiac Electrophysiology
 
 
 
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Contact

 

+44 (0)20 7594 3614f.ng Website

 
 
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Location

 

430ICTEM buildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Sau:2023:10.1016/j.cvdhj.2023.01.004,
author = {Sau, A},
doi = {10.1016/j.cvdhj.2023.01.004},
journal = {Cardiovascular Digital Health Journal},
pages = {60--67},
title = {Artificial intelligence-enabled electrocardiogram to distinguish atrioventricular re-entrant tachycardia from atrioventricular nodal re-entrant tachycardia},
url = {http://dx.doi.org/10.1016/j.cvdhj.2023.01.004},
volume = {4},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundAccurately determining arrhythmia mechanism from a 12-lead electrocardiogram (ECG) of supraventricular tachycardia can be challenging. We hypothesized a convolutional neural network (CNN) can be trained to classify atrioventricular re-entrant tachycardia (AVRT) vs atrioventricular nodal re-entrant tachycardia (AVNRT) from the 12-lead ECG, when using findings from the invasive electrophysiology (EP) study as the gold standard.MethodsWe trained a CNN on data from 124 patients undergoing EP studies with a final diagnosis of AVRT or AVNRT. A total of 4962 5-second 12-lead ECG segments were used for training. Each case was labeled AVRT or AVNRT based on the findings of the EP study. The model performance was evaluated against a hold-out test set of 31 patients and compared to an existing manual algorithm.ResultsThe model had an accuracy of 77.4% in distinguishing between AVRT and AVNRT. The area under the receiver operating characteristic curve was 0.80. In comparison, the existing manual algorithm achieved an accuracy of 67.7% on the same test set. Saliency mapping demonstrated the network used the expected sections of the ECGs for diagnoses; these were the QRS complexes that may contain retrograde P waves.ConclusionWe describe the first neural network trained to differentiate AVRT from AVNRT. Accurate diagnosis of arrhythmia mechanism from a 12-lead ECG could aid preprocedural counseling, consent, and procedure planning. The current accuracy from our neural network is modest but may be improved with a larger training dataset.
AU  - Sau,A
DO  - 10.1016/j.cvdhj.2023.01.004
EP  - 67
PY  - 2023///
SN  - 2666-6936
SP  - 60
TI  - Artificial intelligence-enabled electrocardiogram to distinguish atrioventricular re-entrant tachycardia from atrioventricular nodal re-entrant tachycardia
T2  - Cardiovascular Digital Health Journal
UR  - http://dx.doi.org/10.1016/j.cvdhj.2023.01.004
UR  - https://www.sciencedirect.com/science/article/pii/S2666693623000051
UR  - http://hdl.handle.net/10044/1/102823
VL  - 4
ER  -
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