Zeng Wei, Yuan Chengzhi
School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012 People's Republic of China.
Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881 USA.
Cogn Neurodyn. 2023 Aug;17(4):941-964. doi: 10.1007/s11571-022-09870-7. Epub 2022 Aug 20.
Nowadays, cardiovascular diseases (CVD) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention issue. Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgement and several electrocardiographic (ECG) or vectorcardiographic (VCG) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection 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 disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector, which is decomposed into a series of proper rotation components (PRCs) by using the intrinsic time-scale decomposition (ITD) method. The novel cardiac vector may fully reflect the pathological alterations provoked by MI and may be correlated to the disparity of cardiac system dynamics between healthy and MI subjects. ITD is employed to measure the variability of cardiac vector and the first PRCs are extracted as predominant PRCs which contain most of the cardiac vector's energy. Second, four levels discrete wavelet transform with third-order Daubechies (db3) wavelet function is employed to decompose the predominant PRCs into different frequency bands, which combines with three-dimensional phase space reconstruction to derive features. The properties associated with the cardiac system dynamics are preserved. Since the frequency components above 40 Hz are lack of use in ECG analysis, in order to reduce the feature dimension, the advisable sub-band (D4) is selected for feature acquisition. Third, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. 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, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the tenfold cross-validation style, the achieved average classification accuracy is reported to be 98.20%. Results verify the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.
如今，心血管疾病（CVD）是人类死亡的主要原因之一，在预防问题上受到了广泛而深入的研究关注。心肌缺血是一种会导致心肌梗死（MI）的心血管疾病。心肌梗死的诊断标准辅以临床判断以及几种心电图（ECG）或向量心电图（VCG）程序。然而，心脏病专家对ECG或VCG信号的目视检查既繁琐又费力，还具有主观性。为了克服这些缺点，已经开发了许多心肌梗死检测技术，包括信号处理和人工智能工具。在本研究中，我们提出了一种基于心脏系统动力学差异以及标准12导联和Frank XYZ导联合成的心肌梗死自动检测新技术。首先，将12导联ECG信号与Frank XYZ导联合成，构建一个混合4维心脏向量，然后使用固有时间尺度分解（ITD）方法将其分解为一系列适当旋转分量（PRC）。这种新型心脏向量可以充分反映心肌梗死引起的病理变化，并且可能与健康人和心肌梗死患者心脏系统动力学的差异相关。ITD用于测量心脏向量的变异性，并提取第一个PRC作为包含大部分心脏向量能量的主要PRC。其次，采用具有三阶Daubechies（db3）小波函数的四级离散小波变换将主要PRC分解为不同频带，结合三维相空间重构来提取特征。与心脏系统动力学相关的特性得以保留。由于40Hz以上的频率成分在ECG分析中用处不大，为了降低特征维度，选择合适的子带（D4）进行特征提取。第三，然后使用神经网络对正常（健康）和心肌梗死心脏向量信号之间的心脏系统动力学进行建模、识别和分类。计算健康对照和心肌梗死心脏向量之间心脏系统动力学的差异，并基于一组估计器用于心肌梗死的检测。最后，在PhysioNet PTB数据库上进行实验，以评估所提出方法的有效性，其中从148例心肌梗死患者和52例健康对照中提取了传统12导联和Frank XYZ导联的ECG信号片段。采用十折交叉验证方式，报告的平均分类准确率为98.20%。结果验证了所提出方法的有效性，该方法可作为临床应用中心肌梗死自动检测的潜在候选方法。
