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.…
Author: | Daniel GüldenringORCiD, Michael R. Jennings, Pardis Biglarbeigi, Raymond R. BondORCiD, Rob Brisk, Alan KennedyORCiD, James D. McLaughlinORCiD, Dewar D. FinlayORCiD |
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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 |