ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning.

A heart arrhythmia refers to a set of conditions characterized by irregular heart- beats, with an increasing mortality rate in recent years. Regular monitoring is essential for effective management, as early detection and timely treatment greatly improve survival outcomes. The electrocardiogram (ECG) remains the standard method for detecting arrhythmias, traditionally analyzed by cardiolo- gists and clinical experts. However, the incorporation of automated technology and computer-assisted systems offers substantial support in the accurate diagno- sis of heart arrhythmias. This research focused on developing a hybrid model with stack classifiers, which are state-of-the-art ensemble machine-learning techniques to accurately classify heart arrhythmias from ECG signals, eliminating the need for extensive human intervention. Other conventional machine-learning, bagging, and boosting ensemble algorithms were also explored along with the proposed stack classifiers. The classifiers were trained with a different number of features (50, 65, 80, 95) selected by feature engineering techniques (PCA, Chi-Square, RFE) from a dataset as the most important ones. As an outcome, the stack clas- sifier with XGBoost as the meta-classifier, trained with 65 important features determined by the Principal Component Analysis (PCA) technique, achieved the best performance among all the models. The proposed classifier achieved a perfor- mance of 99.58% accuracy, 99.57% precision, 99.58% recall, and 99.57% f1-score and can be promising for arrhythmia diagnosis.