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This study presents a deep learning (DL) model, Dual-branch ResNet Hybrid-5 (DBRH-5) with a novel Legendre Polynomial-based Attention (LegPoBA) mechanism, for accurate ECG arrhythmia classification. Leveraging the orthogonal properties of Legendre polynomials, LegPoBA enhances DBRH-5’s ability to capture intricate patterns in detecting subtle variations in ECG signals. Extensive experiments revealed the remarkable success of DBRH-5 over existing state-of-the-art models, attaining impressive scores. A hypothesis test further validates the statistical significance of the improvements. LegPoBA increased the model’s performance by 4.35% accuracy and reduced the loss rates by less than 1%. The DBRH-5 with LegPoBA model provides a reliable tool for automated arrhythmia diagnosis, significantly improving AI-driven healthcare.

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DBRH-5: Legendre Polynomial-Based Attention Mechanism in Dual-Branch ResNet Hybrid-5 Towards Arrhythmia Diagnosis

  • 会议论文

  • 发表日期：

    2025年

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  • Joseph Roger Arhin,
  • Xiaoling Zhang,
  • Kenneth Coker,
  • Francis Sam,
  • Jones Apawu,
  • Nicole Naa Darkua Ribeiro

摘要

This study presents a deep learning (DL) model, Dual-branch ResNet Hybrid-5 (DBRH-5) with a novel Legendre Polynomial-based Attention (LegPoBA) mechanism, for accurate ECG arrhythmia classification. Leveraging the orthogonal properties of Legendre polynomials, LegPoBA enhances DBRH-5’s ability to capture intricate patterns in detecting subtle variations in ECG signals. Extensive experiments revealed the remarkable success of DBRH-5 over existing state-of-the-art models, attaining impressive scores. A hypothesis test further validates the statistical significance of the improvements. LegPoBA increased the model’s performance by 4.35% accuracy and reduced the loss rates by less than 1%. The DBRH-5 with LegPoBA model provides a reliable tool for automated arrhythmia diagnosis, significantly improving AI-driven healthcare.