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）作为一种由冠状动脉疾病（CAD）引发的危急病症，早期检测对于防止进一步心肌损伤至关重要。本研究提出了一种基于单分类（OCC）算法的超声心动图早期MI检测新方法。通过采用多模态子空间支持向量数据描述的新颖策略，我们克服了超声心动图数据有限性的挑战。所提出的技术包含一个专用MI检测框架，该框架采用多视角超声心动图，在非线性投影技巧中融合高斯与拉普拉斯Sigmoid函数构成复合核。此外，我们通过优化过程中对两种或其中一种模态进行最大化适配，改进了投影矩阵的更新策略。该方法通过将超声心动图数据中提取的特征高效转换至优化的低维子空间，从而提升了MI检测能力。基于涵盖多视角超声心动图的HMC-QU数据集中目标类别实例专门训练的OCC模型，显示MI检测准确率显著提升。研究结果表明，所提出的多视角方法实现了71.24%的几何均值，标志着基于超声心动图的MI诊断取得重大进展，为临床提供了更精确高效的诊断工具。