Key result
A machine learning approach combining SMOTEENN and XGBoost for 12-lead ECG classification achieved an accuracy of 97.48% in detecting 10 classes of cardiac arrhythmia.
Why the study?
Cardiac arrhythmia is a common condition that can cause severe health problems or sudden death, making early detection through ECG classification critical for saving lives.
Does a combination of SMOTEENN, XGBoost, and supervised machine learning algorithms improve the classification accuracy of cardiac arrhythmias from 12-lead ECGs?
Does a combination of SMOTEENN, XGBoost, and supervised machine learning algorithms improve the classification accuracy of cardiac arrhythmias from 12-lead ECGs?
A machine learning pipeline using SMOTEENN and XGBoost achieved 97.48% accuracy in classifying 10 types of cardiac conditions from 12-lead ECGs.
Warrants prospective validation of ML arrhythmia classifiers; leaves open real-world generalizability and clinical impact.
Cardiac Arrhythmia is one of those common diseases leading to severe health problems for patients and even sudden death in some cases. Early detection of arrhythmias has a great role in saving lives which can be achieved by analyzing and classifying ECG signal into one of the cardiac arrhythmia. This study gives a method to classify the arrhythmia patients have into one of ten classes, where one class represents the normal condition and the other classes represent various types of arrhythmia conditions. This dataset has been preprocessed. The dataset being highly unbalanced, a combination of oversampling and under sampling using SMOTEENN is applied and feature reduction is carried out using XGboost. The feature reduced dataset is then classified using different supervised learning algorithms of machine learning and an accuracy of 97.48% has occurred which is better than state of art method. This study can be further elaborated using real time data for classification.
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Manju et al. (2019) studied Cardiac Arrhythmia. Machine learning classification using SMOTEENN and XGBoost vs. State of art method was evaluated on Classification accuracy. A machine learning approach combining SMOTEENN and XGBoost for 12-lead ECG classification achieved an accuracy of 97.48% in detecting 10 classes of cardiac arrhythmia.
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