Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals

Cardiovascular diseases (CVDs) are main causes of death globally with coronary artery disease (CAD) being the most important. Timely diagnosis and treatment CAD is crucial to reduce incidence complications like myocardial infarction (MI) ischemia-induced congestive heart failure (CHF). Electrocardiogram (ECG) signals commonly employed as diagnostic screening tool detect CAD. In this study, an automated system (AS) was developed for categorization electrocardiogram into normal, CAD, (CHF) classes using convolutional neural network (CNN) unique GaborCNN models. Weight balancing used balance imbalanced dataset. High classification accuracies more than 98.5% were obtained by CNN models respectively, 4-class disease, classes. a preferred model due its good performance reduced computational complexity compared model. To best our knowledge, first study propose categorizing ECG signals. Our proposed equipped be validated bigger database has potential aid clinicians screen CVDs