Monitoring significant ST changes through deep learning

According to the statistics (2016 update) from the American Heart Association (AHA), 15.5 million people over 20  years old in the US have coronary heart disease, and every 42 s, an American suffers from myocardial infarction (MI) [1]. For patients admitted into hospitals with suspected acute coronary syndrome (ACS), electrocardiography (ECG) is an important risk-stratification and assessment tool to guide further treatment for MI, and ST-segment changes in ECG constitute the principle biomarker for such purpose.
Source: Journal of Electrocardiology - Category: Cardiology Authors: Source Type: research