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）是一种由冠状动脉疾病（CAD）引发的危重病症，其早期检测对于防止进一步心肌损伤至关重要。本研究引入了一种利用单类分类（OCC）算法在超声心动图中进行早期MI检测的新方法。通过采用一种基于多模态子空间支持向量数据描述的新颖方法，我们的研究克服了超声心动图数据可用性有限的挑战。所提出的技术涉及一个专门的MI检测框架，该框架采用多视角超声心动图，并在非线性投影技巧中融合了高斯和拉普拉斯西格玛函数的复合核。此外，我们通过优化过程中调整对一种或多种模态的最大化策略，改进了投影矩阵的更新策略。我们的方法通过将从超声心动图数据中提取的特征高效地转换到优化的低维子空间，提升了MI检测能力。在包含多视角超声心动图的综合HMC-QU数据集上，专门针对目标类别实例训练的OCC模型显示出MI检测准确率的显著提升。我们的研究结果表明，所提出的多视角方法实现了71.24%的几何平均值，标志着基于超声心动图的MI诊断取得了实质性进展，并提供了更精确、更高效的诊断工具。