Deep learning methods for screening patients' S-ICD implantation eligibility
Deep learning methods for screening patients' S-ICD implantation eligibility
Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) are used for prevention of sudden cardiac death triggered by ventricular arrhythmias. T Wave Over Sensing (TWOS) is an inherent risk with S-ICDs which can lead to inappropriate shocks. A major predictor of TWOS is a high T:R ratio (the ratio between the amplitudes of the T and R waves). Currently, patients' Electrocardiograms (ECGs) are screened over 10 s to measure the T:R ratio to determine the patients' eligibility for S-ICD implantation. Due to temporal variations in the T:R ratio, 10 s is not a long enough window to reliably determine the normal values of a patient's T:R ratio. In this paper, we develop a convolutional neural network (CNN) based model utilising phase space reconstruction matrices to predict T:R ratios from 10-second ECG segments without explicitly locating the R or T waves, thus avoiding the issue of TWOS. This tool can be used to automatically screen patients over a much longer period and provide an in-depth description of the behavior of the T:R ratio over that period. The tool can also enable much more reliable and descriptive screenings to better assess patients' eligibility for S-ICD implantation.
cs.LG
Dunn, Anthony J.
161d9c8e-6813-4909-95ea-6c11bbbca287
ElRefai, Mohamed H.
c54af0dd-d0a1-478d-957a-cb485c19417c
Roberts, Paul R.
193431e8-f9d5-48d6-8f62-ed9052b2571d
Coniglio, Stefano
03838248-2ce4-4dbc-a6f4-e010d6fdac67
Wiles, Benedict M.
a42ba978-24c3-4533-8eca-498102004477
Zemkoho, Alain B.
30c79e30-9879-48bd-8d0b-e2fbbc01269e
September 2021
Dunn, Anthony J.
161d9c8e-6813-4909-95ea-6c11bbbca287
ElRefai, Mohamed H.
c54af0dd-d0a1-478d-957a-cb485c19417c
Roberts, Paul R.
193431e8-f9d5-48d6-8f62-ed9052b2571d
Coniglio, Stefano
03838248-2ce4-4dbc-a6f4-e010d6fdac67
Wiles, Benedict M.
a42ba978-24c3-4533-8eca-498102004477
Zemkoho, Alain B.
30c79e30-9879-48bd-8d0b-e2fbbc01269e
Dunn, Anthony J., ElRefai, Mohamed H., Roberts, Paul R., Coniglio, Stefano, Wiles, Benedict M. and Zemkoho, Alain B.
(2021)
Deep learning methods for screening patients' S-ICD implantation eligibility.
Artificial Intelligence in Medicine, 119, [102139].
(doi:10.1016/j.artmed.2021.102139).
Abstract
Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) are used for prevention of sudden cardiac death triggered by ventricular arrhythmias. T Wave Over Sensing (TWOS) is an inherent risk with S-ICDs which can lead to inappropriate shocks. A major predictor of TWOS is a high T:R ratio (the ratio between the amplitudes of the T and R waves). Currently, patients' Electrocardiograms (ECGs) are screened over 10 s to measure the T:R ratio to determine the patients' eligibility for S-ICD implantation. Due to temporal variations in the T:R ratio, 10 s is not a long enough window to reliably determine the normal values of a patient's T:R ratio. In this paper, we develop a convolutional neural network (CNN) based model utilising phase space reconstruction matrices to predict T:R ratios from 10-second ECG segments without explicitly locating the R or T waves, thus avoiding the issue of TWOS. This tool can be used to automatically screen patients over a much longer period and provide an in-depth description of the behavior of the T:R ratio over that period. The tool can also enable much more reliable and descriptive screenings to better assess patients' eligibility for S-ICD implantation.
Text
2103.06021
- Author's Original
Restricted to Repository staff only
Request a copy
Text
2103.06021v1
- Accepted Manuscript
Restricted to Repository staff only
Request a copy
More information
Accepted/In Press date: 10 March 2021
Published date: September 2021
Additional Information:
Funding Information:
The work of Anthony J. Dunn is jointly funded by Decision Analysis Services Ltd. and EPSRC through the Studentship with Reference EP/R513325/1 . The work of Alain B. Zemkoho is supported by the EPSRC grant EP/V049038/1 and the Alan Turing Institute under the EPSRC grant EP/N510129/1 .
Publisher Copyright:
© 2021 University of Southampton
Keywords:
cs.LG
Identifiers
Local EPrints ID: 448024
URI: http://eprints.soton.ac.uk/id/eprint/448024
ISSN: 0933-3657
PURE UUID: 1e4939cf-b5c4-4ca2-9012-f5f1d45f3f56
Catalogue record
Date deposited: 30 Mar 2021 16:34
Last modified: 17 Mar 2024 03:40
Export record
Altmetrics
Contributors
Author:
Anthony J. Dunn
Author:
Mohamed H. ElRefai
Author:
Paul R. Roberts
Author:
Benedict M. Wiles
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics