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<p>Deep learning techniques have shown significant promise for the automated diagnosis of CVD using ECG analysis. Nevertheless, several critical challenges persist with current approaches: severe class imbalance, intricate temporal dependencies, and poor modeling of inter-beat relationships ultimately limit clinical applicability. This work presents a novel hybrid deep learning framework that combines a CNN for time-domain feature extraction, k-nearest neighbor-based hypergraph construction with cosine similarity to model inter-beat dependencies, and a residual-connection-enhanced hypergraph neural network (EHGNN) for robust classification. To address class imbalance, focal loss is implemented with adaptive class weighting. For evaluation, our model was tested on two benchmark datasets: the MIT-BIH Arrhythmia Database and the St. Petersburg Institute of Cardiological Technics (INCART) 12-lead Arrhythmia Database. In the case of the MITBIH dataset, a classification accuracy of 98.66% was achieved using the AAMI five-class classification system, whereas the results indicated a three-class arrhythmia detection accuracy of 95.46% for the INCART database. The model demonstrated consistent performance on two separate data sets, thus emphasizing its ability to generalize well. These results confirm that the proposed framework effectively addresses class imbalance through focal loss and SMOTE, temporal dependencies through CNN-based feature extraction, and inter-beat relationships through EHGNN, demonstrating great potential for clinical diagnostic systems by capturing complex heartbeat patterns that are otherwise overlooked by standard models</p>

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An enhanced hypergraph CNN with adaptive focal loss for automated ECG heartbeat classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月27日

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  • Akash Vijayan,
  • Suchetha Manikandan,
  • Deepak Joshi

摘要

Deep learning techniques have shown significant promise for the automated diagnosis of CVD using ECG analysis. Nevertheless, several critical challenges persist with current approaches: severe class imbalance, intricate temporal dependencies, and poor modeling of inter-beat relationships ultimately limit clinical applicability. This work presents a novel hybrid deep learning framework that combines a CNN for time-domain feature extraction, k-nearest neighbor-based hypergraph construction with cosine similarity to model inter-beat dependencies, and a residual-connection-enhanced hypergraph neural network (EHGNN) for robust classification. To address class imbalance, focal loss is implemented with adaptive class weighting. For evaluation, our model was tested on two benchmark datasets: the MIT-BIH Arrhythmia Database and the St. Petersburg Institute of Cardiological Technics (INCART) 12-lead Arrhythmia Database. In the case of the MITBIH dataset, a classification accuracy of 98.66% was achieved using the AAMI five-class classification system, whereas the results indicated a three-class arrhythmia detection accuracy of 95.46% for the INCART database. The model demonstrated consistent performance on two separate data sets, thus emphasizing its ability to generalize well. These results confirm that the proposed framework effectively addresses class imbalance through focal loss and SMOTE, temporal dependencies through CNN-based feature extraction, and inter-beat relationships through EHGNN, demonstrating great potential for clinical diagnostic systems by capturing complex heartbeat patterns that are otherwise overlooked by standard models