The Enigmatic U-wave Delineation and Classification Based on Hybrid Feature Fusion Using Stationary Wavelet Transform and Ensemble Machine Learning Algorithm

The detection and analysis of the elusive U-wave in an electrocardiogram (ECG) presents a captivating yet intricate research problem. Despite its correlation with early prognosis of cardiac disorders, the origin of the U-wave peak remains enigma. Advances in the detection and characterization of the U-wave hold tremendous potential for improving our understanding of heart disease and advancing its diagnosis and treatment. This challenge represents a significant opportunity for further research in the field of detecting cardiac dysfunctioning. In this study, a new approach for automatically identifying the U-wave peak in the TP-segment, TU-segment, and RR-interval using ensemble machine-learning technique has been developed. The method consists of three stages: pre-processing and segmentation, feature extraction, and classification. The ensemble machine learning algorithm and novel feature extraction techniques are utilized to achieve accurate results. The results are evaluated using a publicly available QT database with annotation of the waveform to produce highly accurate classifiers. The achieved average precision, recall, F1 score, and accuracy using an ensemble machine learning algorithm for detection of U-wave classes are 98.23%, 98.30%, 98.50%, and 98.40% respectively. The experimental result demonstrates that an ensemble machinelearning algorithm outperforms as compared to the other state-of-the-art techniques.