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

Michael R. Jennings, Pardis Biglarbeigi, Raymond R. Bond, Rob Brisk, Daniel Guldenring, Alan Kennedy, James McLaughlin, Dewar D. Finlay

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 ECG performance was 62/93% (sensi-tivity/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.

Original languageEnglish
Title of host publication2020 Computing in Cardiology (CinC)
Number of pages4
ISBN (Electronic)9781728173825
ISBN (Print)9781728111056
Publication statusPublished - 13 Sept 2020
EventCinC 2020: Computing in Cardiology - Rimini, Italy
Duration: 13 Sept 202016 Sept 2020

Publication series

ISSN (Print)2325-8861
ISSN (Electronic)2325-887X


ConferenceCinC 2020


  • Electric potential
  • Machine learning
  • Electrocardiography
  • Lead
  • Sensitivity and specificity
  • Myocardium
  • Naive Bayes methods

ASJC Scopus subject areas

  • General Computer Science
  • Cardiology and Cardiovascular Medicine


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