Abstract: Early detection of cardiovascular diseases is vital, especially considering the alarming number of deaths worldwide caused by heart attacks, as highlighted by the world health organization. This emphasizes the urgent need to develop automated systems that can ensure timely and accurate identification of cardiovascular conditions, potentially saving countless lives. This paper presents a novel approach for heartbeats classification, aiming to enhance both accuracy and prediction speed in classification tasks. The model is based on two distinct types of features. First, morphological features that obtained by applying wavelet scattering network to each ECG heartbeat, and the maximum relevance minimum redundancy algorithm was also applied to reduce the computational cost. Second, dynamic features, which capture the duration of two pre R–R intervals and one post R–R interval within the analyzed heartbeat. The feature fusion technique is applied to combine both morphological and dynamic features, and employ a convolutional neural network for the classification of 15 different ECG heartbeat classes. Our proposed method demonstrates an overall accuracy of 98.50% when tested on the Massachusetts institute of Technology -Boston’s Beth Israel hospital arrhythmia database. The results obtained from our approach highlight its superior performance compared to existing automated heartbeat classification models.

Mohamed Elmehdi AIT BOURKHA, Anas HATIM, Dounia NASIR and Said EL BEID, “Optimized Wavelet Scattering Network and CNN for ECG Heartbeat Classification from MIT–BIH Arrhythmia Database” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01602116

@article{BOURKHA2025,
title = {Optimized Wavelet Scattering Network and CNN for ECG Heartbeat Classification from MIT–BIH Arrhythmia Database},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01602116},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01602116},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Mohamed Elmehdi AIT BOURKHA and Anas HATIM and Dounia NASIR and Said EL BEID}
}