Key result
This review summarizes various computational techniques, including preprocessing, feature extraction, and classification, for analyzing abnormal ECG patterns to diagnose cardiac diseases.
Why the study?
ECG interpretation of myocardial infarction is complicated by anomalies like left bundle branch block and left ventricular hypertrophy that cause ST variations, which can lead specialists to make erroneous diagnoses.
This review highlights current computational methods and future research directions for improving the accuracy of ECG-based diagnosis of complex cardiac conditions like LVH, LBBB, and MI.
ECG interpretation challenges with concomitant LBBB or LVH leave open the utility of computer modeling; validation trials needed before adoption.
The 12-lead ECG aids in the diagnosis of myocardial infarction and is helpful in the prediction of cardiovascular disease complications. It does, though, have certain drawbacks. For other electrocardiographic anomalies such as Left Bundle Branch Block and Left Ventricular Hypertrophy syndrome, the ECG signal with Myocardial Infarction is difficult to interpret. These diseases cause variations in the ST portion of the ECG signal. It reduces the clarity of ECG signals, making it more difficult to diagnose these diseases. As a result, the specialist is misled into making an erroneous diagnosis by using the incorrect therapeutic technique. Based on these concepts, this article reviews the different procedures involved in ECG signal pre-processing, feature extraction, feature selection, and classification techniques to diagnose heart disorders such as Left Ventricular Hypertrophy, Bundle Branch Block, and Myocardial Infarction. It reveals the flaws and benefits in each segment, as well as recommendations for developing more advanced and robust methods for diagnosing these diseases, which will increase the system’s accuracy. The current issues and prospective research directions are also addressed.
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Revathi Jothiramalingam (2021) conducted a review in Cardiac diseases (Myocardial Infarction, Bundle Branch Block, Left Ventricular Hypertrophy). Computational techniques for ECG signal analysis was evaluated. This review summarizes various computational techniques, including preprocessing, feature extraction, and classification, for analyzing abnormal ECG patterns to diagnose cardiac diseases.
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