  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.
