Deep neural network trained on surface ECG improves diagnostic accuracy of prior myocardial infarction over Q wave analysis. (25th November 2020)

• Record Type: Journal Article
• Authors: Rogers, A; Tung, J.S; Tooley, J; Bhatia, N.K; Kang, G; Alhusseini, M.I; Baykaner, T; Wang, P.J; Perez, M; Clifford, G; Tereshchenko, L; Narayan, S.M
• Is Part Of: European heart journal. Volume 41:(2020)Supplement 2
• Publication Date: 2020-11-25
• Subjects: Cardiovascular Signal Processing; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/ehjci/ehaa946.3440
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background: Detection of prior myocardial infarction (MI) may inform arrhythmia treatment and prognosis, yet cardiac imaging is resource intensive. ECG Q-wave analysis is quick and inexpensive but has poor accuracy for assessing prior MI. Purpose: To evaluate the ability of a deep neural network (DNN) trained on the surface ECG to identify patients with prior MI. Methods: We assessed 608 well-characterized patients (61.4±14.5 years, 31.2% female) at 2 academic centers. From one 12-lead ECG, median beats were calculated in 3 orthogonal planes (X, Y, Z; Fig. 1A) and used to train a DNN to identify a history of prior MI. Accuracy was compared to manual assessment of pathologic Q waves, defined as a deflection >25% of the subsequent R wave, >40ms in width, and >0.2mV amplitude in 1 of 3 ECG planes. Results: Of 608 patients, 175 had history of MI (28.7%). The DNN outperformed the accuracy of pathologic Q waves. In training, DNN converged to >98% accuracy and in testing, its accuracy was 71±5% (Fig. 1B) (k=5-fold cross validation). This outperformed the 62% accuracy of pathologic Q waves in this study (red dotted line, Fig. 1B). In the validation cohort, DNN provided an area under the receiver operating characteristics curve of 0.730 (Fig. 1C). Conclusion: Deep learning of a 12-lead ECG can identify features of prior myocardial injury more accurately than Q-wave analysis. In attempting to improve these results further, studies should explain what inputs weighted DNN decisions, and identify those that reflect abnormalities detectable clinically or on imaging. Funding Acknowledgement: Type of funding source: Public grant(s) – National budget only. Main funding source(s): NIH NRSA F32