Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm

Xiaoye Zhao, Yinlan Gong, Lihua Xu, Ling Xia, Jucheng Zhang, Dingchang Zheng, Zongbi Yao, Xinjie Zhang, Haicheng Wei, Jun Jiang, Haipeng Liu, Jiandong Mao

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)
    45 Downloads (Pure)


    Purpose: Coronary microvascular dysfunction (CMD) is emerging as an important cause of myocardial ischemia, but there is a lack of a non-invasive method for reliable early detection of CMD. Aim: To develop an electrocardiogram (ECG)-based machine learning algorithm for CMD detection that will lay the groundwork for patient-specific non-invasive early detection of CMD. Methods: Vectorcardiography (VCG) was calculated from each 10-second ECG of CMD patients and healthy controls. Sample entropy (SampEn), approximate entropy (ApEn), and complexity index (CI) derived from multiscale entropy were extracted from ST-T segments of each lead in ECGs and VCGs. The most effective entropy subset was determined using the sequential backward selection algorithm under the intra-patient and inter-patient schemes, separately. Then, the corresponding optimal model was selected from eight machine learning models for each entropy feature based on five-fold cross-validations. Finally, the classification performance of SampEn-based, ApEn-based, and CI-based models was comprehensively evaluated and tested on a testing dataset to investigate the best one under each scheme. Results: ApEn-based SVM model was validated as the optimal one under the intra-patient scheme, with all testing evaluation metrics over 0.8. Similarly, ApEn-based SVM model was selected as the best one under the intra-patient scheme, with major evaluation metrics over 0.8. Conclusions: Entropies derived from ECGs and VCGs can effectively detect CMD under both intra-patient and inter-patient schemes. Our proposed models may provide the possibility of an ECG-based tool for noninvasive detection of CMD.

    Original languageEnglish
    Pages (from-to)13061-13085
    Number of pages25
    JournalMathematical Biosciences and Engineering
    Issue number7
    Publication statusPublished - 5 Jun 2023

    Bibliographical note

    This is an open access article distributed under the terms of the Creative Commons
    Attribution License (http://creativecommons.org/licenses/by/4.0)


    This word was funded by the Natural Science Foundation of Ningxia Province (No. 2022AAC03242), the North Minzu University Scientific Research Projects (No. 2021JCYJ10), the Natural Science Foundation of China (NSFC) (No. 62171408), the Major Scientific Project of Zhejiang Lab (No. 2020ND8AD01), the Key Research and Development Program of Zhejiang Province (No. 2020C03016), Ningxia First-Class Discipline and Scientific Research Projects (Electronic Science and Technology) (No. NXYLXK2017A07), Innovation Team of Lidar Atmosphere Remote Sensing of Ningxia Province, the high level talent selection and training plan of North Minzu University, and Plan for Leading Talents of the State Ethnic Affairs Commission of the People’s Republic of China.


    • coronary microvascular dysfunction (CMD)
    • myocardial ischemia
    • entropy
    • machine learning
    • electrocardiogram (ECG)
    • vectorcardiogram (VCG)

    ASJC Scopus subject areas

    • Modelling and Simulation
    • Agricultural and Biological Sciences(all)
    • Computational Mathematics
    • Applied Mathematics


    Dive into the research topics of 'Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm'. Together they form a unique fingerprint.

    Cite this