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

Michael Jennings, Pardis Biglarbeigi, RR Bond, Rob Brisk, Daniel Güldenring, Alan Kennedy, James McLaughlin, D Finlay

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Abstract

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% (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.
Original languageEnglish
Number of pages4
DOIs
Publication statusAccepted/In press - 13 Sep 2020
EventComputing in Cardiology 2020 - Palacongressi, Rimini, Italy
Duration: 13 Sep 202016 Sep 2020

Conference

ConferenceComputing in Cardiology 2020
Abbreviated titleCinC20
Country/TerritoryItaly
CityRimini
Period13/09/2016/09/20

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