ECG Signal Classification Using Multi-Resolution Wavelet Features and Gradient Boosting Frameworks - Bai, Jinge (International Journal of Computer Science and Information Technology, 2025)
Accurate and automated classification of Electrocardiogram (ECG) signals is critical for early diagnosis of cardiovascular diseases. In this study, we propose a hybrid machine learning framework that combines multi-resolution analysis through Discrete Wavelet Transform (DWT) with powerful ensemble learning models, specifically Gradient Boosting and LightGBM classifiers. Multi-scale time-frequency features were extracted using diverse wavelet families, and were further processed through feature standardization and dimensionality reduction techniques including Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). Extensive experiments demonstrate that LightGBM achieves superior classification performance, reaching an accuracy of 98.41%, while Gradient Boosting attains an accuracy of 95.24%. The results highlight the effectiveness of combining handcrafted feature engineering from the wavelet domain with advanced boosting techniques for robust ECG signal categorization. This framework offers a promising tool for developing intelligent diagnostic support systems in clinical practice.