  Myocardial infarction (MI), or commonly known as heart attack, is a
life-threatening health problem worldwide from which 32.4 million people suffer
each year. Early diagnosis and treatment of MI are crucial to prevent further
heart tissue damages or death. The earliest and most reliable sign of ischemia
is regional wall motion abnormality (RWMA) of the affected part of the
ventricular muscle. Echocardiography can easily, inexpensively, and
non-invasively exhibit the RWMA. In this article, we introduce a three-phase
approach for early MI detection in low-quality echocardiography: 1)
segmentation of the entire left ventricle (LV) wall using a state-of-the-art
deep learning model, 2) analysis of the segmented LV wall by feature
engineering, and 3) early MI detection. The main contributions of this study
are highly accurate segmentation of the LV wall from low-quality
echocardiography, pseudo labeling approach for ground-truth formation of the
unannotated LV wall, and the first public echocardiographic dataset (HMC-QU)*
for MI detection. Furthermore, the outputs of the proposed approach can
significantly help cardiologists for a better assessment of the LV wall
characteristics. The proposed approach has achieved 95.72% sensitivity and
99.58% specificity for the LV wall segmentation, and 85.97% sensitivity, 74.03%
specificity, and 86.85% precision for MI detection on the HMC-QU dataset. *The
benchmark HMC-QU dataset is publicly shared at the repository
https://www.kaggle.com/aysendegerli/hmcqu-dataset
