ECG arrhythmia classification via wavelet-driven feature extraction and swarm-optimised gradient boosting

作者：Umarani S, Kavitha V, Sasikumar MSS, Prabhu S · 发表于：Computers in biology and medicine · 年份：2026 · DOI：10.1016/j.compbiomed.2026.111839 · 研究领域：Arrhythmias, Cardiac、Wavelet Analysis、Electrocardiography、Signal Processing, Computer-Assisted、Boosting Machine Learning Algorithms、Humans、Databases, Factual、Classification Algorithms、Algorithms

Cardiovascular diseases have been the primary contributor to deaths worldwide, and hence, the need to detect arrhythmia from Electrocardiogram signals in a precise and efficient manner is a critical problem in the medical community. This work presents a lightweight and computationally efficient framework that integrates Discrete Wavelet Transform (DWT)-based statistical features, ECG morphological descriptors, and Artificial Bee Colony (ABC)-optimized eXtreme Gradient Boosting Machine (XGBM) classification for ECG beat analysis. This work has been implemented using the popular MIT-BIH Arrhythmia Database, which has 100,674 instances of ECG beats, divided into five AAMI classes, with a severe level of class imbalance, where 89.4% instances belong to the Normal class. ECG signals have been pre-processed using a seven-stage algorithm, including Butterworth high-pass filtering, notch filtering, Pan-Tompkins R-peak detection, beat segmentation, and normalisation. Then, a three-level Haar transform is implemented, and 32 statistical features have been extracted from the DWT decomposition, along with 32 morphological features, forming a 64-dimensional vector. The proposed ABC algorithm with 8 bees and 8 iterations optimizes the six XGBM model hyperparameters using a balanced fitness function of accuracy and macro F1-score and converges at the optimal fitness value of 0.8211. The proposed ABC-XGBM model has a classification accuracy of 95.14%, a macro F1-score of 0.948, a macro AUC o...