Authors - Murodov Gayrat Nekovich, Kholmuhamedov Bakhtiyor Farkhodovich, Avezov Sukhrob Sobirovich, Khudayberganov Nizomaddin Uktambay ogli, Yunusova Maftuna Shokirovna, Mansurova Shahinabonu Najmiddin qizi Abstract - The classification of ECG signals continues to be a major focus in intelligent healthcare systems, especially for the early identification of cardiac arrhythmias. In this work, we propose a hybrid probabilistic neural strategy that integrates Bayesian Networks with Artificial Neural Networks (ANNs) to enhance the reliability of ECG classification. The approach begins by extracting informative ECG features, such as crosscorrelation and phase-based characteristics. A Bayesian Network is then applied to model the probabilistic dependencies among these features and identify those most relevant to classification. At the same time, an ANN is trained on the refined feature set to learn complex non-linear patterns present in the signals. The two models are subsequently combined through a weighted voting mechanism to form an ensemble classifier. Experimental evaluation using an ECG dataset indicates that the proposed ensemble achieves higher accuracy and stability compared to its individual components. Notably, the method demonstrates strong capability in distinguishing multiple arrhythmia categories, which are typically difficult to classify. Overall, the results highlight the promise of hybrid probabilistic–neural models for improving automated ECG interpretation and supporting more accurate diagnosis of cardiac abnormalities.
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