Key result
Machine learning ECG analysis aids diagnosis of cardiac diseases including MI, arrhythmias, and hypertrophy.
This review summarizes various machine learning approaches for ECG-based diagnosis of cardiac conditions such as myocardial infarction and arrhythmias.
May guide ML-ECG algorithm selection; leaves open prospective validation before clinical adoption.
Electrocardiogram (ECG) is a P, QRS and T wave demonstrating the electrical activity of the heart.Feature extraction and segmentation in ECG plays a significant role in diagnosing most of the cardiac disease.The main objective of this paper is to review the various machine learning approaches for diagnosing Myocardial Infarction (heart attack), differentiate Arrhythmias (heart beat variation), Hypertrophy (increase thickness of the heart muscle) and Enlargement of Heart.Further, we also present various machine learning approaches and compare different methods and results used to analyze the ECG.The existing methods are compared and contrasted based on qualitative and qualitative parameters viz., purpose of the work, algorithms adopted and results obtained.
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H.K. et al. (2017) conducted a review in Cardiac diseases (Myocardial Infarction, Arrhythmias, Hypertrophy, Enlargement of Heart). Machine learning approaches for ECG analysis was evaluated. Various machine learning approaches for ECG analysis can be utilized and compared to diagnose cardiac diseases such as myocardial infarction, arrhythmias, and hypertrophy.
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