AI-Enhanced ECG diagnosis system for acute myocardial infarction with LBBB: Constant-Q transform and ResNet-50 integration
This study introduces an advanced Electrocardiogram (ECG) diagnostic framework that melds signal processing techniques with deep learning models to significantly boost accuracy in identifying acute myocardial infarction (MI) and MI related to left bundle branch block (LBBB). By merging the Constant-Q Transform (CQT) with a pre-trained model, this system showcases exceptional performance, an impressive 98.99% accuracy and a remarkably low 0.0029% training loss after 100 trained epochs. Rigorous 10-fold cross-validation substantiates and fortifies these findings. This