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Optimisation of electrode placement for new ambulatory ECG monitoring devices

Kennedy, Alan, Finlay, Dewar D., Guldenring, Daniel, Bond, Raymond R., Moran, Kieran orcid logoORCID: 0000-0003-2015-8967 and McLaughlin, James (2017) Optimisation of electrode placement for new ambulatory ECG monitoring devices. In: Computing in Cardiology 2016, 11-14 Sept 2016, Vancouver, Canada. ISBN ESSN 2325-887X

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 (2562 vs. 2420 oxon 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.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Electrocardiography; Lead; Electrodes; Monitoring; Testing; Training; patient monitoring; biomedical electrodes
Subjects:Medical Sciences > Sports sciences
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Health and Human Performance
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Computing in Cardiology Conference (CinC), 2016. . IEEE. ISBN ESSN 2325-887X
Publisher:IEEE
Official URL:http://ieeexplore.ieee.org/document/7868689/
Copyright Information:© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:21922
Deposited On:16 Aug 2017 14:33 by Giulia Migliorato . Last Modified 26 May 2022 13:29
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