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Deep learning methods for screening patients' S-ICD implantation eligibility

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 seconds to measure the T:R ratio, determining the patients' eligibility for S-ICD implantation. Due to temporal variations in the T:R ratio, 10 seconds is not long enough 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 behaviour 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
0933-3657
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.
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].

Record type: Article

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 seconds to measure the T:R ratio, determining the patients' eligibility for S-ICD implantation. Due to temporal variations in the T:R ratio, 10 seconds is not long enough 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 behaviour 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.

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More information

Accepted/In Press date: 10 March 2021
Published date: 10 March 2021
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
ORCID for Stefano Coniglio: ORCID iD orcid.org/0000-0001-9568-4385
ORCID for Alain B. Zemkoho: ORCID iD orcid.org/0000-0003-1265-4178

Catalogue record

Date deposited: 30 Mar 2021 16:34
Last modified: 02 Nov 2021 02:48

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Contributors

Author: Anthony J. Dunn
Author: Mohamed H. ElRefai
Author: Paul R. Roberts
Author: Benedict M. Wiles

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