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<p>In this paper, a three-term conjugate gradient method is proposed as an advanced optimization technique for enhancing ECG signal classification using deep learning. This method is designed for solving unconstrained optimization problems by leveraging a modified gradient difference vector while ensuring sufficient descent and convergence properties. The results demonstrate that the proposed method outperforms traditional techniques in terms of the number of iterations, function evaluations, and computational time required to reach a solution. It was also applied to train deep neural networks for ECG signal classification, achieving an accuracy of 96.32%, which is a significant improvement over existing models. This integration of deep learning and advanced gradient-based optimization techniques leads to higher classification accuracy for ECG signals, with a notable reduction in mean squared error, indicating better generalization capability and reduced prediction errors compared to other methods. These findings highlight the potential of the proposed approach in improving ECG classification accuracy, contributing to more efficient and precise diagnosis of heart diseases, ultimately assisting healthcare professionals in making faster and more accurate clinical decisions.</p>

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An improved three-term conjugate gradient approach for deep neural network training in ECG signal classification

  • 期刊论文

  • 发表日期：

    2025年11月11日

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  • Alaa Luqman Ibrahim,
  • Bayda Ghanim Fathi,
  • Maiwan Bahjat Abdulrazzaq

摘要

In this paper, a three-term conjugate gradient method is proposed as an advanced optimization technique for enhancing ECG signal classification using deep learning. This method is designed for solving unconstrained optimization problems by leveraging a modified gradient difference vector while ensuring sufficient descent and convergence properties. The results demonstrate that the proposed method outperforms traditional techniques in terms of the number of iterations, function evaluations, and computational time required to reach a solution. It was also applied to train deep neural networks for ECG signal classification, achieving an accuracy of 96.32%, which is a significant improvement over existing models. This integration of deep learning and advanced gradient-based optimization techniques leads to higher classification accuracy for ECG signals, with a notable reduction in mean squared error, indicating better generalization capability and reduced prediction errors compared to other methods. These findings highlight the potential of the proposed approach in improving ECG classification accuracy, contributing to more efficient and precise diagnosis of heart diseases, ultimately assisting healthcare professionals in making faster and more accurate clinical decisions.