Authors
S Umarani, V Kavitha, M S S Sasikumar, S Prabhu
Published in
Computers in biology and medicine. Volume 213. Pages 111839. Jul 05, 2026. Epub Jul 05, 2026.
Abstract
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 of 0.983, Matthews Correlation Coefficient of 0.925, and G-Mean of 0.932 with class-wise AUC values > 0.94. An ablation study has shown that the proposed DWT adds +3.7% and the proposed ABC optimization adds +1.14% in accuracy improvement. Five-fold cross-validation has shown a stable performance with a mean accuracy of 0.952 ± 0.001 at a time complexity of 1.0 ms per sample without the dependency of the GPU. The proposed framework is better than the other deep learning models such as CardioAttentionNet with a classification accuracy of 91.20% and the proposed transformer-based classifier with a classification accuracy of 90.50%.
PMID:
42402238
Bibliographic data and abstract were imported from PubMed on 06 Jul 2026.
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