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

• Published in: 2020 Computing in Cardiology pp. 1 - 4
• Main Authors: Jennings, Michael R, Biglarbeigi, Pardis, Bond, Raymond R, Brisk, Rob, Guldenring, Daniel, Kennedy, Alan, McLaughlin, James, Finlay, Dewar D
• Format: Conference Proceeding
• Language: English
• Published: Creative Commons; the authors hold their copyright 13.09.2020
• Subjects: Electric potential; Electrocardiography; Lead; Machine learning; Myocardium; Naive Bayes methods; Sensitivity and specificity
• ISSN: 2325-887X
• DOI: 10.22489/CinC.2020.245

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.