Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

Xiaoye Zhao, Jucheng Zhang, Yinglan Gong, Lihua Xu, Haipeng Liu, Shujun Wei, Yuan Wu, Ganhua Cha, Haicheng Wei, Jiandong Mao, Ling Xia

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    Background: Myocardial ischemia is a common early symptom of cardiovascular disease (CVD). Reliable detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. The vectorcardiogram (VCG) could improve the performance of ECG-based myocardial ischemia detection by affording temporal-spatial characteristics related to myocardial ischemia and capturing subtle changes in ST-T segment in continuous cardiac cycles. We aim to investigate if the combination of ECG and VCG could improve the performance of machine learning algorithms in automatic myocardial ischemia detection. Methods: The ST-T segments of 20-second, 12-lead ECGs, and VCGs were extracted from 377 patients with myocardial ischemia and 52 healthy controls. Then, sample entropy (SampEn, of 12 ECG leads and of three VCG leads), spatial heterogeneity index (SHI, of VCG) and temporal heterogeneity index (THI, of VCG) are calculated. Using a grid search, four SampEn and two features are selected as input signal features for ECG-only and VCG-only models based on support vector machine (SVM), respectively. Similarly, three features (S I , THI, and SHI, where S I is the SampEn of lead I) are further selected for the ECG + VCG model. 5-fold cross validation was used to assess the performance of ECG-only, VCG-only, and ECG + VCG models. To fully evaluate the algorithmic generalization ability, the model with the best performance was selected and tested on a third independent dataset of 148 patients with myocardial ischemia and 52 healthy controls. Results: The ECG + VCG model with three features (S I , THI, and SHI) yields better classifying results than ECG-only and VCG-only models with the average accuracy of 0.903, sensitivity of 0.903, specificity of 0.905, F1 score of 0.942, and AUC of 0.904, which shows better performance with fewer features compared with existing works. On the third independent dataset, the testing showed an AUC of 0.814. Conclusion: The SVM algorithm based on the ECG + VCG model could reliably detect myocardial ischemia, providing a potential tool to assist cardiologists in the early diagnosis of CVD in routine screening during primary care services.
    Original languageEnglish
    Article number854191
    Number of pages16
    JournalFrontiers in Physiology
    Publication statusPublished - 30 May 2022

    Bibliographical note

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


    Funding Information: This work was supported by North Minzu University Scientific Research Projects under grant nos. 2021JCYJ10, the Natural Science Foundation of China (NSFC) under grant nos. 61527811 and 62171408, the Key Research and Development Program of Zhejiang Province grant nos. 2020C03060 and 2020C03016, the Zhejiang Provincial Natural Science Foundation of China grant nos. LY17H180003, the Leading Talents of Scientific and Technological Innovation of Ningxia Province, the Plan for Leading Talents of the State Ethnic Affairs Commission of the People’s Republic of China, High level talent selection and training plan of North Minzu University, the Innovation Team of Lidar Atmosphere Remote Sensing of Ningxia Province, and Ningxia first-class discipline and scientific research projects (electronic science and technology) grant nos. NXYLXK 2017A07.


    • Physiology
    • myocardial ischemia
    • vectorcardiogram (VCG)
    • sample entropy (SampEn)
    • Lyapunov index
    • support vector machine (SVM)


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