2771-1463ISSN (Online)
2771-1471ISSN (Print)
Volume 8 , Issue 1 , PP: 44-51, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Mahmoud M. Ibrahim 1 *
Doi: https://doi.org/10.54216/JCHCI.080105
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.
Electrocardiogram , Heart rate variability , Signal processing , Heart rhythm analysis , Machine learning , Biomedical signal integration , Healthcare monitoring , Cardiac arrhythmias , Multimodal data fusion.
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