Unveiling fetal heart health: harnessing auto-metric graph neural networks and Hazelnut tree search for ECG-based arrhythmia detection.

Journal: Computer methods in biomechanics and biomedical engineering
Published Date: Apr 24, 2025

Abstract

Fetal electrocardiogram (ECG) provides a non-invasive means to assess fetal heart health, but isolating the fetal signal from the dominant maternal ECG remains challenging. This study introduces the FHH-AMGNN-HTSOA-ECG-AD method for enhanced fetal arrhythmia detection. It employs Dual Tree Complex Wavelet Transform for denoising and utilizes an Auto-Metric Graph Neural Network (AMGNN) optimized by the Hazelnut Tree Search Algorithm (HTSOA). This integration enables accurate classification of normal and abnormal fetal heart signals. Experimental results demonstrate that the proposed approach significantly outperforms existing methods in terms of accuracy, precision, and specificity.

Authors

  • M Suganthy
    Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, India.
  • B Sarala
    Department of Electronics and Communication Engineering, R.M.K. Engineering College, Kavaraipettai, India.
  • G Sumathy
    Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, India.
  • W T Chembian
    Department of Computer Science and Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College (Autonomous), Chennai, Tamil Nadu, India.

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