Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification
<|reference_start|>Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification: Early detection of myocardial infarction (MI), a critical condition arising from coronary artery disease (CAD), is vital to prevent further myocardial damage. This study introduces a novel method for early MI detection using a one-class classification (OCC) algorithm in echocardiography. Our study overcomes the challenge of limited echocardiography data availability by adopting a novel approach based on Multi-modal Subspace Support Vector Data Description. The proposed technique involves a specialized MI detection framework employing multi-view echocardiography incorporating a composite kernel in the non-linear projection trick, fusing Gaussian and Laplacian sigmoid functions. Additionally, we enhance the update strategy of the projection matrices by adapting maximization for both or one of the modalities in the optimization process. Our method boosts MI detection capability by efficiently transforming features extracted from echocardiography data into an optimized lower-dimensional subspace. The OCC model trained specifically on target class instances from the comprehensive HMC-QU dataset that includes multiple echocardiography views indicates a marked improvement in MI detection accuracy. Our findings reveal that our proposed multi-view approach achieves a geometric mean of 71.24%, signifying a substantial advancement in echocardiography-based MI diagnosis and offering more precise and efficient diagnostic tools.<|reference_end|>
@article{zahid2024refining,
    title={Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite
  Kernel Strategy in One-Class Classification},
    author={Muhammad Uzair Zahid, Aysen Degerli, Fahad Sohrab, Serkan Kiranyaz,
  Tahir Hamid, Rashid Mazhar, and Moncef Gabbouj},
    journal={arXiv preprint arXiv:2402.06530},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06530},
    primaryClass={cs.LG cs.AI eess.SP}
}