A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG

• Published in: PloS one 2019-08, Vol.14 (8), p.e0220294-e0220294
• Main Authors: Ledezma, Carlos A, Zhou, Xin, Rodríguez, Blanca, Tan, P J, Díaz-Zuccarini, Vanessa
• Format: Article
• Language: English
• Subjects: Action potential; Action Potentials; Artificial intelligence; Artificial neural networks; Biological markers; Biology and Life Sciences; Biomarkers; Biomarkers - analysis; Cables; Cardiovascular disease; Cardiovascular diseases; Classification; Computer and Information Sciences; Computer applications; Computer simulation; Concept learning; Coronary artery disease; Coronary heart disease; Diagnosis; Echocardiography; EKG; Electrocardiography; Electrocardiography - methods; Etiology (Medicine); Heart diseases; Humans; Ischemia; Ischemia - diagnosis; Learning algorithms; Machine Learning; Medicine and Health Sciences; Methods; Models, Theoretical; Myocytes; Neural networks; Neural Networks, Computer; Populations; Research and Analysis Methods; Studies; Variability
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0220294

Early detection of coronary heart disease (CHD) has the potential to prevent the millions of deaths that this disease causes worldwide every year. However, there exist few automatic methods to detect CHD at an early stage. A challenge in the development of these methods is the absence of relevant datasets for their training and validation. Here, the ten Tusscher-Panfilov 2006 model and the O'Hara-Rudy model for human myocytes were used to create two populations of models that were in concordance with data obtained from healthy individuals (control populations) and included inter-subject variability. The effects of ischemia were subsequently included in the control populations to simulate the effects of mild and severe ischemic events on single cells, full ischemic cables of cells and cables of cells with various sizes of ischemic regions. Action potential and pseudo-ECG biomarkers were measured to assess how the evolution of ischemia could be quantified. Finally, two neural network classifiers were trained to identify the different degrees of ischemia using the pseudo-ECG biomarkers. The control populations showed action potential and pseudo-ECG biomarkers within the physiological ranges and the trends in the biomarkers commonly identified in ischemic patients were observed in the ischemic populations. On the one hand, inter-subject variability in the ischemic pseudo-ECGs precluded the detection and classification of early ischemic events using any single biomarker. On the other hand, the neural networks showed sensitivity and positive predictive value above 95%. Additionally, the neural networks revealed that the biomarkers that were relevant for the detection of ischemia were different from those relevant for its classification. This work showed that a computational approach could be used, when data is scarce, to validate proof-of-concept machine learning methods to detect ischemic events.