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Review of Computational Techniques for the Analysis of Abnormal Patterns of ECG Signal Provoked by Cardiac Disease

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摘要 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 withMyocardial 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 itmore 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 LeftVentricularHypertrophy,Bundle Branch Block,andMyocardial Infarction.It reveals the flaws and benefits in each segment,as well as recommendations for developing more advanced and robustmethods for diagnosing these diseases,which will increase the system’s accuracy.The current issues and prospective research directions are also addressed.
出处 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第9期875-906,共32页 工程与科学中的计算机建模(英文)
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