aspg-25-2876-1792226008 20261017083328 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cognitive Human-Computer Interaction JCHCI 2771-1471 2771-1463 2024 8 1 Mahmoud M. Ibrahim Decision support department, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Sharqiyah, Egypt This project focuses on healthcare diagnostics where it examines the problem of accurate heartbeat classification by merging Electrocardiogram (ECG) signals. ECG signals have such variability and complexity that it is hard to accurately detect various cardiac rhythms. That is why this research came up with an ensemble framework that combined recurrent neural networks (RNNs), and convolutional neural networks (CNNs) reinforced by group normalization (GN). By incorporating these techniques, the authors aimed to improve the stability and efficiency of RNNs with respect to temporal dependencies as well as CNN for spatial features. The ensemble model exhibited a greater accuracy in classifying different heartbeats after careful experimentation and analysis. During training, the inclusion of GN in the CNN part ensured its stability thereby promoting generalization of the model. This study shows that combining ECG signals is efficient and also highlights the necessity of specific normalization methods used to refine medical diagnostics. 2024 19 22 2876 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCHCI.080105 https://americaspg.com/journal/25/article/2876 https://americaspg.com/storage/articles/manuscripts/01KWT4K579V62S1CX48TKV523W.pdf