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{Arnold:2020:10.1016/j.cvdhj.2020.07.001,
author = {Arnold, AD and Howard, JP and Gopi, AA and Chan, CP and Ali, N and Keene, D and Shun-Shin, MJ and Ahmad, Y and Wright, IJ and Ng, FS and Linton, NWF and Kanagaratnam, P and Peters, NS and Rueckert, D and Francis, DP and Whinnett, ZI},
doi = {10.1016/j.cvdhj.2020.07.001},
journal = {Cardiovascular Digital Health Journal},
pages = {11--20},
title = {Discriminating electrocardiographic responses to His-bundle pacing using machine learning.},
url = {http://dx.doi.org/10.1016/j.cvdhj.2020.07.001},
volume = {1},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Background: His-bundle pacing (HBP) has emerged as an alternative to conventional ventricular pacing because of its ability to deliver physiological ventricular activation. Pacing at the His bundle produces different electrocardiographic (ECG) responses: selective His-bundle pacing (S-HBP), non-selective His bundle pacing (NS-HBP), and myocardium-only capture (MOC). These 3 capture types must be distinguished from each other, which can be challenging and time-consuming even for experts. Objective: The purpose of this study was to use artificial intelligence (AI) in the form of supervised machine learning using a convolutional neural network (CNN) to automate HBP ECG interpretation. Methods: We identified patients who had undergone HBP and extracted raw 12-lead ECG data during S-HBP, NS-HBP, and MOC. A CNN was trained, using 3-fold cross-validation, on 75% of the segmented QRS complexes labeled with their capture type. The remaining 25% was kept aside as a testing dataset. Results: The CNN was trained with 1297 QRS complexes from 59 patients. Cohen kappa for the neural network's performance on the 17-patient testing set was 0.59 (95% confidence interval 0.30 to 0.88; P <.0001), with an overall accuracy of 75%. The CNN's accuracy in the 17-patient testing set was 67% for S-HBP, 71% for NS-HBP, and 84% for MOC. Conclusion: We demonstrated proof of concept that a neural network can be trained to automate discrimination between HBP ECG responses. When a larger dataset is trained to higher accuracy, automated AI ECG analysis could facilitate HBP implantation and follow-up and prevent complications resulting from incorrect HBP ECG analysis.
AU  - Arnold,AD
AU  - Howard,JP
AU  - Gopi,AA
AU  - Chan,CP
AU  - Ali,N
AU  - Keene,D
AU  - Shun-Shin,MJ
AU  - Ahmad,Y
AU  - Wright,IJ
AU  - Ng,FS
AU  - Linton,NWF
AU  - Kanagaratnam,P
AU  - Peters,NS
AU  - Rueckert,D
AU  - Francis,DP
AU  - Whinnett,ZI
DO  - 10.1016/j.cvdhj.2020.07.001
EP  - 20
PY  - 2020///
SP  - 11
TI  - Discriminating electrocardiographic responses to His-bundle pacing using machine learning.
T2  - Cardiovascular Digital Health Journal
UR  - http://dx.doi.org/10.1016/j.cvdhj.2020.07.001
UR  - https://www.ncbi.nlm.nih.gov/pubmed/32954375
UR  - https://www.sciencedirect.com/science/article/pii/S2666693620300050?via%3Dihub
UR  - http://hdl.handle.net/10044/1/83471
VL  - 1
ER  -
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