Developing an accurate and automatic algorithm for detection and localization of myocardial infarction (MI) remains a great challenge for multi-lead electrocardiograph (ECG) signals. The core is a novel technique of multi-dimensional association information analysis for a multi-lead ECG tensor. Tensorization based on Discrete Wavelet Transform is investigated to construct an effective ECG tensor containing multi-dimensional association information from 12-lead ECG signals. The multi-lead feature extraction algorithm based on Parallel Factor Analysis is developed to automatically extract the low-dimensional and highly recognizable lead characteristic features of the tensor. After that a bagged decision tree is constructed to categorize 12 types of heartbeats, healthy controls and 11 kinds of MI, from the lead features. Using the PTB database, we compare with the existing MI diagnosis methods. For MI detection, significant improvement of the accuracy, sensitivity and specificity are achieved; as high as 99.88%, 99.98% and 99.39% respectively. Furthermore, an experiment with 36-dimensional features obtained from the ECG tensor is conducted for the localization of 11 kinds of MI, and our proposed method achieved an accuracy of 99.40%, sensitivity of 99.86%, and specificity of 99.89%. The proposed algorithm can effectually accomplish the localization of 11 categories of MI by using the lead features extracted from the multi-dimensional association ECG tensor, which has not been achieved in literature. The accurate and comprehensive tool development will greatly help cardiologists diagnose 12-lead ECG signals of MI.

对于多导联心电图（ECG）信号，开发一种准确，自动的心肌梗死（MI）检测和定位算法仍然是一项巨大的挑战。核心是用于多导联ECG张量的多维关联信息分析的新技术。研究了基于离散小波变换的张量化方法，从12导联ECG信号中构造了一个包含多维关联信息的有效ECG张量。开发了基于并行因子分析的多引线特征提取算法，以自动提取张量的低维和高度可识别的引线特征。之后，构建一个袋装决策树，根据主要特征对12种心跳，健康控制和11种MI进行分类。使用PTB数据库，我们将其与现有的心梗诊断方法进行比较。对于MI检测，可以显着提高准确性，灵敏性和特异性。分别高达99.88％，99.98％和99.39％。此外，从ECG张量获得的36维特征的实验进行了11种MI的定位，我们提出的方法实现了99.40％的准确性，99.86％的灵敏度和99.89％的特异性。通过从多维关联心电图张量提取的先导特征，该算法可以有效地完成11类心梗的定位，这在文献中还没有实现。准确而全面的工具开发将极大地帮助心脏病医生诊断MI的12导联ECG信号。大大提高了准确性，敏感性和特异性；分别高达99.88％，99.98％和99.39％。此外，从ECG张量获得的36维特征的实验进行了11种MI的定位，我们提出的方法实现了99.40％的准确性，99.86％的灵敏度和99.89％的特异性。通过从多维关联心电图张量提取的先导特征，该算法可以有效地完成11类心梗的定位，这在文献中还没有实现。准确而全面的工具开发将极大地帮助心脏病医生诊断MI的12导联ECG信号。大大提高了准确性，敏感性和特异性；分别高达99.88％，99.98％和99.39％。此外，从ECG张量获得的36维特征的实验进行了11种MI的定位，我们提出的方法实现了99.40％的准确性，99.86％的灵敏度和99.89％的特异性。该算法可以利用多维关联心电图张量中提取的先导特征有效地完成11类心梗的定位，这在文献中还没有实现。准确而全面的工具开发将极大地帮助心脏病医生诊断MI的12导联ECG信号。进行了从ECG张量获得的36维特征的实验以定位11种MI，我们提出的方法获得了99.40％的准确度，99.86％的灵敏度和99.89％的特异性。通过从多维关联心电图张量提取的先导特征，该算法可以有效地完成11类心梗的定位，这在文献中还没有实现。准确而全面的工具开发将极大地帮助心脏病医生诊断MI的12导联ECG信号。进行了从ECG张量获得的36维特征的实验以定位11种MI，我们提出的方法获得了99.40％的准确度，99.86％的灵敏度和99.89％的特异性。通过从多维关联心电图张量提取的先导特征，该算法可以有效地完成11类心梗的定位，这在文献中还没有实现。准确而全面的工具开发将极大地帮助心脏病医生诊断MI的12导联ECG信号。通过从多维关联心电图张量提取的先导特征，该算法可以有效地完成11类心梗的定位，这在文献中还没有实现。准确而全面的工具开发将极大地帮助心脏病医生诊断MI的12导联ECG信号。通过从多维关联心电图张量提取的先导特征，该算法可以有效地完成11类心梗的定位，这在文献中还没有实现。准确而全面的工具开发将极大地帮助心脏病医生诊断MI的12导联ECG信号。