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.

翻译：心肌梗死（MI）是冠状动脉疾病（CAD）引发的危重病症，其早期检测对防止心肌进一步损伤至关重要。本研究提出一种基于一类分类（OCC）算法的心超声早期MI检测新方法。针对心超声数据稀缺的挑战，我们采用基于多模态子空间支持向量数据描述的新策略。该技术构建了专用MI检测框架，通过多视角心超声成像，在非线性投影技巧中融合高斯与拉普拉斯Sigmoid函数形成复合核函数。此外，我们通过优化过程中对单模态或双模态投影矩阵的自适应最大化策略，改进了投影矩阵的更新机制。该方法通过将心超声数据提取的特征高效转化为优化后的低维子空间，显著提升了MI检测能力。基于HMC-QU综合数据集（包含多视角心超声图像）中目标类实例专门训练的OCC模型，其MI检测准确率获得显著提升。实验结果表明，本研究所提出的多视角方法实现了71.24%的几何均值，标志着基于心超声的MI诊断技术取得重要进展，为临床提供了更精准高效的诊断工具。