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Machine Learning Approach to Assess the Performance of Patch Based Leads in the Detection of Ischaemic Electrocardiogram Changes

  • Background: We have previously reported on the potential of patch-based ECG leads to observe changes typical during ischaemia. In this study we aim to assess the utility of patch-based leads in the detection of these changes. Method: Body surface potential maps (BSPM) from subjects (n=45) undergoing elective percutaneous coronary angioplasty (PTCA) were used. The short spaced lead (SSL), that was previously identified as having the greatest ST-segment change between baseline and peak balloon inflation (PBI), was selected as the basis for a patch based lead system. A feature set of J-point amplitudes for all bipolar leads available within the same 100 mm region were included (n=6). Current 12-lead ECG criteria were applied to 12-lead ECGs for the same subjects to benchmark performance. Results: The previously identified single SSL achieved sensitivity and specificity of 87% and 71% respectively using a Naive Bayes classifier. Adding other combinations of leads to this did not improve performance significantly. The 12-lead ECGBackground: We have previously reported on the potential of patch-based ECG leads to observe changes typical during ischaemia. In this study we aim to assess the utility of patch-based leads in the detection of these changes. Method: Body surface potential maps (BSPM) from subjects (n=45) undergoing elective percutaneous coronary angioplasty (PTCA) were used. The short spaced lead (SSL), that was previously identified as having the greatest ST-segment change between baseline and peak balloon inflation (PBI), was selected as the basis for a patch based lead system. A feature set of J-point amplitudes for all bipolar leads available within the same 100 mm region were included (n=6). Current 12-lead ECG criteria were applied to 12-lead ECGs for the same subjects to benchmark performance. Results: The previously identified single SSL achieved sensitivity and specificity of 87% and 71% respectively using a Naive Bayes classifier. Adding other combinations of leads to this did not improve performance significantly. The 12-lead ECG performance was 62/93% (sensitivity/specificity). Conclusion: This study suggests that short spaced leads can be sensitive to ischaemic ECG changes. However, due to the short distance between leads, they lack the specificity of the 12-lead ECG.show moreshow less

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Metadaten
Author:Daniel GüldenringORCiD, Michael R. Jennings, Pardis Biglarbeigi, Raymond R. BondORCiD, Rob Brisk, Alan KennedyORCiD, James D. McLaughlinORCiD, Dewar D. FinlayORCiD
DOI:https://doi.org/10.22489/CinC.2020.245
Identifier:9781728173825 OPAC HS OPAC extern
Identifier:2325-887X OPAC HS OPAC extern
Parent Title (English):Computing in Cardiology : 13-16. Sept. 2020, Rimini, Italy
Publisher:IEEE
Place of publication:Piscataway
Document Type:conference proceeding (article)
Conference:2020 Computing in Cardiology (CinC) 13-16. Sept. 2020, Rimini, Italy
Language:English
Date of Publication (online):2021/02/10
Year of first Publication:2020
Tag:EKG
Biomedizinische Signalverarbeitung; ECG
First Page:1
Last Page:4
Institutes:Fakultät Elektrotechnik
Publication Lists:Güldenring, Daniel
Publication reviewed:begutachtet
Licence (German):Es gilt das deutsche Urheberrecht
Release Date:2021/02/19
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