P2426Validating the diagnostic value of a machine learning algorithm for STEMI detection

• Published in: European heart journal Vol. 40; no. Supplement_1
• Main Authors: Mehta, S, Botelho, R, Fernandez, F, Villagran, C, Frauenfelder, A, Matheus, C, Vieira, D, Torres, M A, Pinto, G, Mazzini, J, Pisana, L, Jacobucci, R, Marin, M A, Funatsu, C, Vallenilla, I
• Format: Journal Article
• Language: English
• Published: Oxford University Press 01.10.2019
• Subjects: Cardiovascular Signal Processing
• ISSN: 0195-668X; 1522-9645
• DOI: 10.1093/eurheartj/ehz748.0759

Abstract Background We have previously reported the use of Artificial Intelligence (AI) guided EKG analysis for detection of ST-Elevation Myocardial Infarction (STEMI). To demonstrate the diagnostic value of our algorithm, we compared AI predictions with reports that were confirmed as STEMI. Purpose To demonstrate the absolute proficiency of AI for detecting STEMI in a standard12-lead EKG. Methods An observational, retrospective, case-control study. Sample: 5,087 EKG records, including 2,543 confirmed STEMI cases obtained via feedback from health centers following appropriate patient management (thrombolysis, primary Percutaneous Coronary Intervention (PCI), pharmacoinvasive therapy or coronary artery bypass surgery). Records excluded patient and medical information. The sample was derived from the International Telemedical Systems (ITMS) database. LUMENGT-AI Algorithm was employed. Preprocessing: detection of QRS complexes by wavelet system, segmentation of each EKG into individual heartbeats (53,667 total beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented, “STEMI” and “Not-STEMI” classes were considered for each heartbeat, individual probabilities were aggregated to generate the final label for each record. Training & Testing: 90% and 10% of the sample were used, respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with NVIDIA GTX 1070 GPU, 8GB RAM. Results The model yielded an accuracy of 97.2%, a sensitivity of 95.8%, and a specificity of 98.5%. Conclusion(s) Our AI-based algorithm can reliably diagnose STEMI and will preclude the role of a cardiologist for screening and diagnosis, especially in the pre-hospital setting.