Comparing machine learning approaches to identify myocardial scar from the ECG. (25th November 2020)

• Record Type: Journal Article
• Authors: Tung, J; Rogers, A.J; Ravi, N; Bhatia, N.K; Shah, R.L; Purewal, S.K; Baykaner, T; Rappel, W.J; Viswanathan, M.N; Brodt, C.R; Wang, P.J; Clifford, G; Tereshchenko, L; Narayan, S.M
• Is Part Of: European heart journal. Volume 41:(2020)Supplement 2
• Publication Date: 2020-11-25
• Subjects: Myocardial Disease - Diagnostic Methods; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/ehjci/ehaa946.2048
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background: Detection of myocardial infarction (MI) traditionally requires ECG Q waves, which have poor sensitivity, or imaging, which is time consuming. We hypothesized that machine learning (ML) of the ECG could identify prior MI, but its accuracy may depend highly upon the architecture and parameters chosen. Purpose: To compare ML architectures that predict prior MI from the ECG. Methods: We curated ECGs in 608 patients seen in cardiology clinics at 2 centers. We transformed 12-lead ECGs to median beats in Frank (X, Y, Z) planes (fig. A). We tested 3 architectures: a 1D deep neural network (DNN), a 3D neural network, and a support vector machine (SVM). The 1D DNN used only temporal convolutions (fig B) while the 3D DNN uses a spatial convolution (fig C) prior to the fully-connected layer (fig. C). Predictive accuracy for history of MI was compared for all architectures (fig. D). Results: Patients (61.4±14.5 years, 31.2% female) had a 28.7% (175/608) prevalence of prior MI. Optimized SVM of 6 features provided accuracy of 66.1% for identifying prior MI, similar to ECG Q wave analysis. 1D DDN had accuracy of 63.6% with an area under curve (AUC) of 0.625. 3D DNN outperformed 1D DNN and SVM, providing an accuracy of 71±5% (using k=5-fold cross validation), with an AUC of 0.730. Conclusion: ECG machine learning can identify prior MI better than Q wave analysis, but is sensitive to technical parameters and specific computational architecture. It is important to develop a framework to enable robust comparisons of different ML studies and future refinements. Funding Acknowledgement: Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Institutes of Health - United States