@Article{cmes.2021.016485,
AUTHOR = {Revathi Jothiramalingam, Anitha Jude, Duraisamy Jude Hemanth},
TITLE = {Review of Computational Techniques for the Analysis of Abnormal Patterns of ECG Signal Provoked by Cardiac Disease},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {128},
YEAR = {2021},
NUMBER = {3},
PAGES = {875--906},
URL = {http://www.techscience.com/CMES/v128n3/44018},
ISSN = {1526-1506},
ABSTRACT = {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.},
DOI = {10.32604/cmes.2021.016485}
}