Abstract
In this study we aim to determine, from body surface potential map (BSPM) data, the optimal bipolar chest electrode placement for maximum R-wave amplitude. The study data consisted of 117-lead 352-node BSPM data recorded from 229 healthy subjects. The dataset was split into a training set of 172 subjects and a testing set of the remaining 57 subjects. Optimal electrode placement was determined using a lead selection method based on the difference in R-wave amplitude across all 352 nodes for each patient. R-wave values were then extracted and used to create a median BSPM of the training data. From this median BSPM the optimal electrode placement was defined as the location of the minimum and maximum R-wave values. On the testing dataset this new optimal bipolar chest lead (R-lead) was then compared to all of the leads of the Mason-Likar 12-lead ECG and previously described bipolar chest leads, CM5, CS5, CC5 and CB5. The R-lead showed significant improvement in median R-wave amplitude over the next best lead, CM5 (2562μV vs. 2420μV, Wilcoxon sign ranked test, p< 0.001). Given the improvement in signal strength, an improvement in automated R-wave detection and R-R interval analysis from single lead ECG monitors may be achieved.
Original language | English |
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Title of host publication | Computing in Cardiology Conference, CinC 2016 |
Editors | Alan Murray |
Publisher | IEEE Computer Society |
Pages | 101-104 |
Number of pages | 4 |
ISBN (Electronic) | 9781509008964 |
Publication status | Published (in print/issue) - 1 Mar 2016 |
Event | 43rd Computing in Cardiology Conference, CinC 2016 - Vancouver, Canada Duration: 11 Sept 2016 → 14 Sept 2016 |
Publication series
Name | Computing in Cardiology |
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Volume | 43 |
ISSN (Print) | 2325-8861 |
ISSN (Electronic) | 2325-887X |
Conference
Conference | 43rd Computing in Cardiology Conference, CinC 2016 |
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Country/Territory | Canada |
City | Vancouver |
Period | 11/09/16 → 14/09/16 |
Bibliographical note
Funding Information:This work has been supported by the Northern Ireland Connected Health Innovation Centre and the PATHway project funded by the European Commission under the Horizon 2020 Programme (Call H2020-PHC-2014, Grant no. 643491)
Publisher Copyright:
© 2016 CCAL.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.