Myocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide. The electrocardiogram (ECG) is a cornerstone diagnostic tool in identifying myocardial ischemia and infarction due to its accessibility, rapidity, and cost-effectiveness. Key ECG predictors of MI include ST-segment elevation or depression, T-wave inversions, pathological Q waves, and new-onset left bundle branch block (LBBB). Additionally, high-risk features such as hyperacute T waves, QT prolongation, and reciprocal changes provide early clues for ischemia. Advances in computational analysis, including machine learning, have further enhanced the predictive value of subtle ECG patterns by identifying changes invisible to the human eye. While ECG findings are critical for rapid diagnosis, their interpretation requires integration with clinical context, biomarker analysis, and imaging studies for accurate stratification of risk and management. Continued research on novel ECG parameters, such as fragmented QRS and spatial QRS-T angle, holds promise for improving early detection and outcomes in MI patients.
Author (s) Details
Rohit Raina
Department of General Medicine, AIIMS Bathinda, Punjab, 151001, India.
Preeti Singh Dhoat
Department of General Medicine, AIIMS Bathinda, Punjab, 151001, India.
Amandeep Kaur
Department of General Medicine, AIIMS Bathinda, Punjab, 151001, India.
Sonali Mina
Department of General Medicine, AIIMS Bathinda, Punjab, 151001, India.
Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49238-92-3/CH1
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