Optimisation of electrode placement for new ambulatory ECG monitoring devices

Alan Kennedy, Dewar D. Finlay, Daniel Guldenring, Raymond Bond, Kieran Moran, James McLaughlin

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


    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 languageEnglish
    Title of host publicationComputing in Cardiology Conference, CinC 2016
    EditorsAlan Murray
    PublisherIEEE Computer Society
    Number of pages4
    ISBN (Electronic)9781509008964
    Publication statusPublished (in print/issue) - 1 Mar 2016
    Event43rd Computing in Cardiology Conference, CinC 2016 - Vancouver, Canada
    Duration: 11 Sept 201614 Sept 2016

    Publication series

    NameComputing in Cardiology
    ISSN (Print)2325-8861
    ISSN (Electronic)2325-887X


    Conference43rd Computing in Cardiology Conference, CinC 2016

    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 2017 Elsevier B.V., All rights reserved.


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