Myocardial infarction (MI) poses a significant clinical challenge, necessitating expeditious and precise detection to mitigate potentially fatal outcomes. Current MI diagnosis predominantly relies on electrocardiography (ECG); however, it is fraught with limitations, including inter-observer variability and a reliance on expert interpretation. This study introduces an automated MI detection framework that capitalizes on hybrid signal processing methodologies and deterministic learning theory. The initial step involves the extraction of the Shannon energy envelope (SEE) and its derivative from a single-lead ECG. Integration of the SEE into the ECG’s phase portrait provides a means to capture the underlying nonlinear system dynamics. Subsequently, the application of fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) yields discriminative features originating from the most energetically dominant intrinsic mode components (IMFs) within the SEE. Profound dissimilarities are discernible between ECG signals recorded from healthy subjects and those afflicted with MI. In the subsequent phase, deterministic learning theory, implemented through neural networks, is employed to facilitate the classification of ECG signals into two distinct groups. The method’s efficacy is meticulously evaluated using the PTB diagnostic ECG database, resulting in a noteworthy average classification accuracy of 99.21\(\%\) within a tenfold cross-validation framework. In summation, the findings affirm that the proposed features not only complement conventional ECG attributes but also align closely with the underlying dynamics of the ECG system, ultimately fortifying the automatic detection of MI. The imperative requirement for early and accurate MI diagnosis is addressed through our approach, offering a robust and dependable means to fulfill this pivotal clinical need.

心肌梗死 (MI) 提出了重大的临床挑战，需要快速、精确的检测以减轻潜在的致命后果。目前心肌梗死的诊断主要依靠心电图（ECG）；然而，它充满了局限性，包括观察者之间的差异和对​​专家解释的依赖。本研究介绍了一种利用混合信号处理方法和确定性学习理论的自动化 MI 检测框架。第一步涉及从单导联心电图中提取香农能量包络线 (SEE) 及其导数。将 SEE 集成到 ECG 相图中提供了一种捕获潜在非线性系统动态的方法。随后，快速自适应多元经验模态分解 (FA-MVEMD) 的应用产生了源自 SEE 中最具能量主导的固有模态分量 (IMF) 的判别特征。健康受试者和患有心肌梗塞的受试者记录的心电图信号之间存在显着差异。在后续阶段，采用通过神经网络实现的确定性学习理论来促进将心电图信号分类为两个不同的组。使用 PTB 诊断心电图数据库对该方法的功效进行了仔细评估，在十倍交叉验证框架内获得了 99.21 \(\%\)的值得注意的平均分类准确度。总而言之，研究结果证实，所提出的特征不仅补充了传统心电图属性，而且与心电图系统的基本动态紧密结合，最终加强了心肌梗死的自动检测。我们的方法满足了早期准确诊断 MI 的迫切要求，提供了强大而可靠的方法来满足这一关键的临床需求。