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

ABSTRACT: 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 cable

SUBMITTER: Ledezma CA 

PROVIDER: S-EPMC6690680 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature