Evolution of single-lead ECG for STEMI detection using a deep learning approach. (1st January 2022)

• Record Type: Journal Article
• Authors: Gibson, C. Michael; Mehta, Sameer; Ceschim, Mariana R.S.; Frauenfelder, Alejandra; Vieira, Daniel; Botelho, Roberto; Fernandez, Francisco; Villagran, Carlos; Niklitschek, Sebastian; Matheus, Cristina I.; Pinto, Gladys; Vallenilla, Isabella; Lopez, Claudia; Acosta, Maria I.; Munguia, Anibal; Fitzgerald, Clara; Mazzini, Jorge; Pisana, Lorena; Quintero, Samantha
• Is Part Of: International journal of cardiology. Volume 346(2022)
• Page Start: 47
• Page End: 52
• Publication Date: 2022-01-01
• Subjects: Acute coronary syndrome -- Electrocardiogram -- Deep learning -- Wearable devices; ACS Acute Coronary Syndrome -- AMI Acute Myocardial Infarction -- CNN Convolutional Neural Network -- ECG Electrocardiogram -- EMS Emergency Medical Services -- LATIN Latin America Telemedicine Infarct Network -- DL Deep learning -- ML Machine learning -- PCI Percutaneous Coronary Intervention -- STEMI ST-elevation Myocardial Infarction; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2021.11.039
• Languages: English
• ISSNs: 0167-5273

Abstract

Abstract: Background: While ST-Elevation Myocardial Infarction (STEMI) door-to-balloon times are often below 90 min, symptom to door times remain long at 2.5-h, due at least in part to a delay in diagnosis. Objectives: To develop and validate a machine learning-guided algorithm which uses a single‑lead electrocardiogram (ECG) for STEMI detection to speed diagnosis. Methods: Data was extracted from the Latin America Telemedicine Infarct Network (LATIN), a population-based Acute Myocardial Infarction (AMI) program that provides care to patients in Brazil, Colombia, Mexico, and Argentina through telemedicine. Sample : the first dataset was comprised of 8511 ECGs that were used for various machine learning experiments to test our Deep Learning approach for STEMI diagnosis. The second dataset of 2542 confirmed STEMI diagnosis EKG records, including specific ischemic heart wall information (anterior, inferior, and lateral), was derived from the previous dataset to test the STEMI localization model. Preprocessing : Detection of QRS complexes by wavelet system, segmentation of each EKG record into individual heartbeats with fixed window of 0.4 s to the left and 0.9 s to the right of main. Training & Testing : 90% and 10% of the total dataset, respectively, were used for both models. Classification : two 1-D convolutional neural networks were implemented, two classes were considered for first models (STEMI/Not-STEMI) and three classes for the second model (Anterior/Inferior/Lateral) each corresponding to the heart wall affected. These individual probabilities were aggregated to generate the final label for each model. Results: The single‑lead ECG strategy was able to provide an accuracy of 90.5% for STEMI detection with Lead V2, which also yielded the best results overall among individual leads. STEMI Localization model provided promising results for anterior and inferior wall STEMIs but remained suboptimal for Lateral STEMI. Conclusions: An Artificial Intelligence-enhanced single‑lead ECG is a promising screening tool. This technology provides an autonomous and accurate STEMI diagnostic alternative that can be incorporated into wearable devices, potentially providing patients reliable means to seek treatment early and offers the potential to thereby improve STEMI outcomes in the long run. Highlights: Deep learning Versus Standard 12-Lead ECG We present an AI algorithm to identify a STEMI via accurate and reliable analysis of cardiologist-annotated 12-lead ECGs. Single-Lead STEMI Detection A STEMI-detecting AI algorithm was produced by analyzing information from single-lead ECG. Lead V2 showed the best results. 12-Lead Versus Single-Lead Comparing AI-based 12-lead and 1-lead ECGs noted superior STEMI detection in 12-lead ECG and well-matched lead V2 accuracy. Comparison of Study Cardiologists vs. AI Algorithms Using an External Test Dataset AI-based algorithms demonstrated superiority using both the AI-guided 12-lead and the V2 single-lead ECG methodologies.