Cardiovascular diseases (CVD) is the leading cause of human mortality and morbidity around the world, in which myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. Currently, electrocardiogram (ECG) is widely used by the clinicians to diagnose MI patients due to its inexpensiveness and non-invasive nature. Pathological alterations provoked by MI cause slow conduction by increasing axial resistance on coupling between cells. This issue may cause abnormal patterns in the dynamics of the tip of the cardiac vector in the ECG signals. However, manual interpretation of the pathological alternations induced by MI is a time-consuming, tedious and subjective task. To overcome such disadvantages, computer-aided diagnosis techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on hybrid feature extraction and artificial intelligence tools. Tunable quality factor ($Q$-factor) wavelet transform (TQWT), variational mode decomposition (VMD) and phase space reconstruction (PSR) are utilized to extract representative features to form cardiac vectors with synthesis of the standard 12-lead and Frank XYZ leads. They are combined with neural networks to model, identify and detect abnormal patterns in the dynamics of cardiac system caused by MI. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. Second, this vector is decomposed into a set of frequency subbands with a number of decomposition levels by using the TQWT method. Third, VMD is employed to decompose the subband of the 4-dimensional cardiac vector into different intrinsic modes, in which the first intrinsic mode contains the majority of the cardiac vector's energy and is considered to be the predominant intrinsic mode. It is selected to construct the reference variable for analysis. Fourth, phase space of the reference variable is reconstructed, in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Fifth, cardiac system dynamics can be modeled and identified using neural networks, which employ the ED of 3D PSR of the reference variable as the input features. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, data sets, which include conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls from PTB diagnostic ECG database, are used for evaluation. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 97.98%. Currently, ST segment evaluation is one of the major and traditional ways for the MI detection. However, there exist weak or even undetectable ST segments in many ECG signals. Since the proposed method does not rely on the information of ST waves, it can serve as a complementary MI detection algorithm in the intensive care unit (ICU) of hospitals to assist the clinicians in confirming their diagnosis. Overall, our results verify that the proposed features may satisfactorily reflect cardiac system dynamics, and are complementary to the existing ECG features for automatic cardiac function analysis.

心血管疾病 (CVD) 是全世界人类死亡和发病的主要原因，其中心肌梗塞 (MI) 是一种无症状的疾病，会对心肌造成不可逆转的损害。目前，心电图 (ECG) 由于其廉价和无创性而被临床医生广泛用于诊断 MI 患者。MI 引起的病理改变通过增加细胞间耦合的轴向阻力导致传导缓慢。此问题可能会导致心电图信号中心脏矢量尖端的动态模式异常。然而，对 MI 引起的病理变化的手动解释是一项耗时、乏味和主观的任务。为了克服这些缺点，已经开发了包括信号处理和人工智能工具在内的计算机辅助诊断技术。在这项研究中，我们提出了一种基于混合特征提取和人工智能工具自动检测 MI 的新技术。可调品质因数 ($问$-factor) 小波变换 (TQWT)、变分模式分解 (VMD) 和相空间重建 (PSR) 用于提取代表性特征以形成标准 12 导联和 Frank XYZ 导联合成的心脏向量。它们与神经网络相结合，对 MI 引起的心脏系统动力学中的异常模式进行建模、识别和检测。首先，将 12 导联心电图信号缩减为 3 维 VCG 信号，与 Frank XYZ 导联合成以构建混合 4 维心脏矢量。其次，使用 TQWT 方法将该向量分解为具有多个分解级别的一组频率子带。第三，使用VMD将4维心脏向量的子带分解为不同的固有模式，其中第一固有模式包含大部分心脏矢量能量，被认为是主要的固有模式。选择它来构建参考变量进行分析。第四，参考变量的相空间被重建，其中与非线性心脏系统动力学相关的属性被保留。三维 (3D) PSR 与欧几里得距离 (ED) 已被用于推导特征，这些特征表明正常（健康）和 MI 心脏矢量信号之间的心脏系统动力学存在显着差异。第五，可以使用神经网络对心脏系统动力学进行建模和识别，神经网络采用参考变量的 3D PSR 的 ED 作为输入特征。健康对照和 MI 心脏向量之间的心脏系统动力学差异被计算并用于基于一组估计器的 MI 检测。最后，数据集包括来自 148 名 MI 患者和来自 PTB 诊断 ECG 数据库的 52 名健康对照的常规 12 导联和 Frank XYZ 导联 ECG 信号片段，用于评估。通过使用 10 折交叉验证样式，据报道实现的平均分类准确率为 97.98%。目前，ST 段评估是 MI 检测的主要和传统方法之一。然而，许多心电图信号中存在微弱甚至检测不到的 ST 段。由于所提出的方法不依赖于 ST 波的信息，它可以作为医院重症监护病房 (ICU) 中的补充 MI 检测算法，以协助临床医生确认诊断。总的来说，我们的结果验证了所提出的特征可以令人满意地反映心脏系统动力学，并且是对现有的自动心脏功能分析心电图特征的补充。