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

doi:10.3934/mbe.2023582

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 7: 13061-13085. doi: 10.3934/mbe.2023582

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Research article Special Issues

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

1.
School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009, Anhui, China
2.
School of Electrical and Information Engineering, North Minzu University, Yinchuan 750001, Ningxia, China
3.
Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia, Yinchuan 750001, Ningxia, China
4.
Institute of Wenzhou, Zhejiang University, Wenzhou 325000, Zhejiang, China
5.
Hangzhou Linghua Biotech Ltd, Hangzhou 310009, Zhejiang, China
6.
Key Laboratory for Biomedical Engineering of Ministry of Education, Hangzhou 310009, Zhejiang, China
7.
Institute of Biomedical Engineering, Zhejiang University, Hangzhou 310009, Zhejiang, China
8.
Department of Clinical Engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China
9.
Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, United Kingdom
10.
Department of Cardiology, Ningxia Hui Autonomous Region People's Hospital, Yinchuan 750021, Ningxia, China
11.
Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China

Academic Editor: Ulf Schmitz
† These two authors contributed equally.

Received: 29 January 2023 Revised: 20 May 2023 Accepted: 22 May 2023 Published: 05 June 2023

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 non-invasive detection of CMD.
- coronary microvascular dysfunction (CMD),
- myocardial ischemia,
- entropy,
- machine learning,
- electrocardiogram (ECG),
- vectorcardiogram (VCG)
Citation: Xiaoye Zhao, Yinlan Gong, Lihua Xu, Ling Xia, Jucheng Zhang, Dingchang Zheng, Zongbi Yao, Xinjie Zhang, Haicheng Wei, Jun Jiang, Haipeng Liu, Jiandong Mao. Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 13061-13085. doi: 10.3934/mbe.2023582

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Abstract

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 non-invasive detection of CMD.

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