Combining XGBoost with novel 1D morphological features from GLCM and GLRLM yielded an accuracy of 90.46% and an AUC of 0.982 for classifying cardiac arrhythmias, outperforming wavelet features.
Does a novel feature extraction method using GLCM and GLRLM combined with XGBoost improve the classification of cardiac arrhythmias on 12-lead ECGs compared to wavelet features?
A novel feature extraction method using GLCM and GLRLM combined with XGBoost provides highly accurate automatic classification of cardiac arrhythmias from 12-lead ECGs.
Effect estimate: AUC 0.982
The electrocardiogram (ECG) is the most commonly used tool for diagnosing cardiovascular diseases. Recently, there have been a number of attempts to classify cardiac arrhythmias using machine learning and deep learning techniques. In this study, we propose a novel method to generate the gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) from one-dimensional signals. From the GLCM and GLRLM, we extracted morphological features for automatic ECG signal classification. The extracted features were combined with six machine learning algorithms (decision tree, k-nearest neighbor, naïve Bayes, logistic regression, random forest, and XGBoost) to classify cardiac arrhythmias. Experiments were conducted on a 12-lead ECG database collected from Chapman University and Shaoxing People’s Hospital. Of the six machine learning algorithms, combining XGBoost with the proposed features yielded an accuracy of 90.46%, an AUC of 0.982, a sensitivity of 0.892, a precision of 0.900, and an F1 score of 0.895 and presented better results than wavelet features with XGBoost. The experimental results show the effectiveness of the proposed feature extraction algorithm.
Lee et al. (Tue,) conducted a other in Cardiac arrhythmias. GLCM and GLRLM morphological features combined with XGBoost vs. Wavelet features with XGBoost was evaluated on Cardiac arrhythmia classification accuracy (AUC 0.982). Combining XGBoost with novel 1D morphological features from GLCM and GLRLM yielded an accuracy of 90.46% and an AUC of 0.982 for classifying cardiac arrhythmias, outperforming wavelet features.