Key result
O-XGBoost classifier outperforms state-of-the-art methods in classifying arrhythmic heartbeats.
Why the study?
Does an optimal extreme gradient boosting (O-XGBoost) classifier improve the identification and classification of arrhythmic heartbeats from ECG signals compared to state-of-the-art methods?
Does an optimal extreme gradient boosting (O-XGBoost) classifier improve the identification and classification of arrhythmic heartbeats from ECG signals compared to state-of-the-art methods?
An optimized XGBoost classifier with feature selection and SMOTE balancing can effectively identify and classify arrhythmic heartbeats from ECG signals.
discrete wavelet transform. Three sets of features, i.e. statistical, temporal, and spectral, are extracted from the processed ECG signals followed by random forest aided recursive feature elimination strategy to select the prominent features for proper classification of arrhythmic heartbeats by the proposed optimal extreme gradient boosting (O-XGBoost) classifier. Hyperparameters such as learning rate, tree-specific parameters, and regularization parameters have been optimized to improve the performance of the XGBoost classifier. Moreover, the synthetic minority over-sampling technique has been employed for balancing the dataset in order to improve the classification performance. Quantitative results reveal the remarkable performance over state-of-the-art methods. The proposed model can be implemented in any computer-aided diagnostic system with similar topological structures.
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Majumder et al. (2023) studied Arrhythmic heartbeats. Optimal extreme gradient boosting (O-XGBoost) classifier vs. State-of-the-art methods was evaluated on Classification performance. An optimal extreme gradient boosting (O-XGBoost) classifier using statistical, temporal, and spectral features demonstrated remarkable performance over state-of-the-art methods for classifying arrhythmic heartbeats.
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