An Automatic Coronary Microvascular Dysfunction Classification Method Based on Hybrid ECG Features and Expert Features.

Objective: In recent years, the early diagnosis and treatment of coronary microvascular dysfunction (CMD) have become crucial for preventing coronary heart disease. This paper aims to develop a computer-assisted autonomous diagnosis method for CMD by using ECG features and expert features.

Approach: Clinical electrocardiogram (ECG), myocardial contrast echocardiography (MCE), and coronary angiography (CAG) are used in our method.

Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features.

Myocardial infarction (MI) is one of the most threatening cardiovascular diseases. This paper aims to explore a method for using an algorithm to autonomously classify MI based on the electrocardiogram (ECG).A detection method of MI that fuses continuous T-wave area (C_TWA) feature and ECG deep features is proposed.